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17,880 results on '"Lill, AT"'

1. A Lightweight and Extensible Cell Segmentation and Classification Model for Whole Slide Images

Author

Shvetsov, Nikita, Kilvaer, Thomas K., Tafavvoghi, Masoud, Sildnes, Anders, Møllersen, Kajsa, Busund, Lill-Tove Rasmussen, and Bongo, Lars Ailo

Subjects
Computer Science - Computer Vision and Pattern Recognition, Computer Science - Artificial Intelligence, I.4.6, I.4.9, I.2.10
Abstract

Developing clinically useful cell-level analysis tools in digital pathology remains challenging due to limitations in dataset granularity, inconsistent annotations, high computational demands, and difficulties integrating new technologies into workflows. To address these issues, we propose a solution that enhances data quality, model performance, and usability by creating a lightweight, extensible cell segmentation and classification model. First, we update data labels through cross-relabeling to refine annotations of PanNuke and MoNuSAC, producing a unified dataset with seven distinct cell types. Second, we leverage the H-Optimus foundation model as a fixed encoder to improve feature representation for simultaneous segmentation and classification tasks. Third, to address foundation models' computational demands, we distill knowledge to reduce model size and complexity while maintaining comparable performance. Finally, we integrate the distilled model into QuPath, a widely used open-source digital pathology platform. Results demonstrate improved segmentation and classification performance using the H-Optimus-based model compared to a CNN-based model. Specifically, average $R^2$ improved from 0.575 to 0.871, and average $PQ$ score improved from 0.450 to 0.492, indicating better alignment with actual cell counts and enhanced segmentation quality. The distilled model maintains comparable performance while reducing parameter count by a factor of 48. By reducing computational complexity and integrating into workflows, this approach may significantly impact diagnostics, reduce pathologist workload, and improve outcomes. Although the method shows promise, extensive validation is necessary prior to clinical deployment., Comment: 27 pages, 11 figures

Published
2025

2. Demonstrating dynamic surface codes

Author

Eickbusch, Alec, McEwen, Matt, Sivak, Volodymyr, Bourassa, Alexandre, Atalaya, Juan, Claes, Jahan, Kafri, Dvir, Gidney, Craig, Warren, Christopher W., Gross, Jonathan, Opremcak, Alex, Miao, Nicholas Zobrist Kevin C., Roberts, Gabrielle, Satzinger, Kevin J., Bengtsson, Andreas, Neeley, Matthew, Livingston, William P., Greene, Alex, Rajeev, Acharya, Beni, Laleh Aghababaie, Aigeldinger, Georg, Alcaraz, Ross, Andersen, Trond I., Ansmann, Markus, Frank, Arute, Arya, Kunal, Asfaw, Abraham, Babbush, Ryan, Ballard, Brian, Bardin, Joseph C., Bilmes, Alexander, Jenna, Bovaird, Bowers, Dylan, Brill, Leon, Broughton, Michael, Browne, David A., Buchea, Brett, Buckley, Bob B., Tim, Burger, Burkett, Brian, Bushnell, Nicholas, Cabrera, Anthony, Campero, Juan, Chang, Hung-Shen, Chiaro, Ben, Chih, Liang-Ying, Cleland, Agnetta Y., Cogan, Josh, Collins, Roberto, Conner, Paul, Courtney, William, Alexander, Crook, L., Curtin, Ben, Das, Sayan, Barba, Alexander Del Toro, Demura, Sean, De Lorenzo, Laura, Di Paolo, Agustin, Donohoe, Paul, Drozdov, Ilya K., Dunsworth, Andrew, Elbag, Aviv Moshe, Elzouka, Mahmoud, Erickson, Catherine, Ferreira, Vinicius S., Burgos, Leslie Flores, Forati, Ebrahim, Fowler, Austin G., Foxen, Brooks, Ganjam, Suhas, Gonzalo, Garcia, Gasca, Robert, Genois, Élie, Giang, William, Gilboa, Dar, Gosula, Raja, Dau, Alejandro Grajales, Dietrich, Graumann, Ha, Tan, Habegger, Steve, Hansen, Monica, Harrigan, Matthew P., Harrington, Sean D., Heslin, Stephen, Heu, Paula, Higgott, Oscar, Hiltermann, Reno, Hilton, Jeremy, Huang, Hsin-Yuan, Huff, Ashley, Huggins, William J., Jeffrey, Evan, Jiang, Zhang, Jin, Xiaoxuan, Jones, Cody, Joshi, Chaitali, Juhas, Pavol, Kabel, Andreas, Kang, Hui, Amir, Karamlou, H., Kechedzhi, Kostyantyn, Khaire, Trupti, Khattar, Tanuj, Khezri, Mostafa, Kim, Seon, Kobrin, Bryce, Korotkov, Alexander N., Kostritsa, Fedor, Kreikebaum, John Mark, Kurilovich, Vladislav D., Landhuis, David, Tiano, Lange-Dei, Langley, Brandon W., Lau, Kim-Ming, Ledford, Justin, Lee, Kenny, Lester, Brian J., Guevel, Loïck Le, Wing, Li, Yan, Lill, Alexander T., Locharla, Aditya, Lucero, Erik, Lundahl, Daniel, Lunt, Aaron, Madhuk, Sid, Maloney, Ashley, Mandrà, Salvatore, Martin, Leigh S., Martin, Orion, Maxfield, Cameron, McClean, Jarrod R., Meeks, Seneca, Anthony, Megrant, Molavi, Reza, Molina, Sebastian, Montazeri, Shirin, Movassagh, Ramis, Newman, Michael, Nguyen, Anthony, Nguyen, Murray, Ni, Chia-Hung, Oas, Logan, Orosco, Raymond, Ottosson, Kristoffer, Pizzuto, Alex, Potter, Rebecca, Pritchard, Orion, Quintana, Chris, Ramachandran, Ganesh, Reagor, Matthew J., Rhodes, David M., Rosenberg, Eliott, Rossi, Elizabeth, Sankaragomathi, Kannan, Schurkus, Henry F., Shearn, Michael J., Shorter, Aaron, Shutty, Noah, Shvarts, Vladimir, Small, Spencer, Smith, W. Clarke, Springer, Sofia, Sterling, George, Suchard, Jordan, Szasz, Aaron, Sztein, Alex, Thor, Douglas, Tomita, Eifu, Torres, Alfredo, Torunbalci, M. Mert, Vaishnav, Abeer, Vargas, Justin, Sergey, Vdovichev, Vidal, Guifre, Heidweiller, Catherine Vollgraff, Waltman, Steven, Waltz, Jonathan, Wang, Shannon X., Ware, Brayden, Weidel, Travis, White, Theodore, Wong, Kristi, Woo, Bryan W. K., Woodson, Maddy, Xing, Cheng, Yao, Z. Jamie, Yeh, Ping, Ying, Bicheng, Yoo, Juhwan, Yosri, Noureldin, Young, Grayson, Zalcman, Adam, Yaxing, Zhang, Zhu, Ningfeng, Boixo, Sergio, Kelly, Julian, Smelyanskiy, Vadim, Neven, Hartmut, Bacon, Dave, Chen, Zijun, Klimov, Paul V., Roushan, Pedram, Neill, Charles, Chen, Yu, and Morvan, Alexis

Subjects
Quantum Physics
Abstract

A remarkable characteristic of quantum computing is the potential for reliable computation despite faulty qubits. This can be achieved through quantum error correction, which is typically implemented by repeatedly applying static syndrome checks, permitting correction of logical information. Recently, the development of time-dynamic approaches to error correction has uncovered new codes and new code implementations. In this work, we experimentally demonstrate three time-dynamic implementations of the surface code, each offering a unique solution to hardware design challenges and introducing flexibility in surface code realization. First, we embed the surface code on a hexagonal lattice, reducing the necessary couplings per qubit from four to three. Second, we walk a surface code, swapping the role of data and measure qubits each round, achieving error correction with built-in removal of accumulated non-computational errors. Finally, we realize the surface code using iSWAP gates instead of the traditional CNOT, extending the set of viable gates for error correction without additional overhead. We measure the error suppression factor when scaling from distance-3 to distance-5 codes of $\Lambda_{35,\text{hex}} = 2.15(2)$, $\Lambda_{35,\text{walk}} = 1.69(6)$, and $\Lambda_{35,\text{iSWAP}} = 1.56(2)$, achieving state-of-the-art error suppression for each. With detailed error budgeting, we explore their performance trade-offs and implications for hardware design. This work demonstrates that dynamic circuit approaches satisfy the demands for fault-tolerance and opens new alternative avenues for scalable hardware design., Comment: 11 pages, 5 figures, Supplementary Information

Published
2024

3. Scaling and logic in the color code on a superconducting quantum processor

Author

Lacroix, Nathan, Bourassa, Alexandre, Heras, Francisco J. H., Zhang, Lei M., Bausch, Johannes, Senior, Andrew W., Edlich, Thomas, Shutty, Noah, Sivak, Volodymyr, Bengtsson, Andreas, McEwen, Matt, Higgott, Oscar, Kafri, Dvir, Claes, Jahan, Morvan, Alexis, Chen, Zijun, Zalcman, Adam, Madhuk, Sid, Acharya, Rajeev, Beni, Laleh Aghababaie, Aigeldinger, Georg, Alcaraz, Ross, Andersen, Trond I., Ansmann, Markus, Arute, Frank, Arya, Kunal, Asfaw, Abraham, Atalaya, Juan, Babbush, Ryan, Ballard, Brian, Bardin, Joseph C., Bilmes, Alexander, Blackwell, Sam, Bovaird, Jenna, Bowers, Dylan, Brill, Leon, Broughton, Michael, Browne, David A., Buchea, Brett, Buckley, Bob B., Burger, Tim, Burkett, Brian, Bushnell, Nicholas, Cabrera, Anthony, Campero, Juan, Chang, Hung-Shen, Chiaro, Ben, Chih, Liang-Ying, Cleland, Agnetta Y., Cogan, Josh, Collins, Roberto, Conner, Paul, Courtney, William, Crook, Alexander L., Curtin, Ben, Das, Sayan, Demura, Sean, De Lorenzo, Laura, Di Paolo, Agustin, Donohoe, Paul, Drozdov, Ilya, Dunsworth, Andrew, Eickbusch, Alec, Elbag, Aviv Moshe, Elzouka, Mahmoud, Erickson, Catherine, Ferreira, Vinicius S., Burgos, Leslie Flores, Forati, Ebrahim, Fowler, Austin G., Foxen, Brooks, Ganjam, Suhas, Garcia, Gonzalo, Gasca, Robert, Genois, Élie, Giang, William, Gilboa, Dar, Gosula, Raja, Dau, Alejandro Grajales, Graumann, Dietrich, Greene, Alex, Gross, Jonathan A., Ha, Tan, Habegger, Steve, Hansen, Monica, Harrigan, Matthew P., Harrington, Sean D., Heslin, Stephen, Heu, Paula, Hiltermann, Reno, Hilton, Jeremy, Hong, Sabrina, Huang, Hsin-Yuan, Huff, Ashley, Huggins, William J., Jeffrey, Evan, Jiang, Zhang, Jin, Xiaoxuan, Joshi, Chaitali, Juhas, Pavol, Kabel, Andreas, Kang, Hui, Karamlou, Amir H., Kechedzhi, Kostyantyn, Khaire, Trupti, Khattar, Tanuj, Khezri, Mostafa, Kim, Seon, Klimov, Paul V., Kobrin, Bryce, Korotkov, Alexander N., Kostritsa, Fedor, Kreikebaum, John Mark, Kurilovich, Vladislav D., Landhuis, David, Lange-Dei, Tiano, Langley, Brandon W., Laptev, Pavel, Lau, Kim-Ming, Ledford, Justin, Lee, Kenny, Lester, Brian J., Guevel, Loïck Le, Li, Wing Yan, Li, Yin, Lill, Alexander T., Livingston, William P., Locharla, Aditya, Lucero, Erik, Lundahl, Daniel, Lunt, Aaron, Maloney, Ashley, Mandrà, Salvatore, Martin, Leigh S., Martin, Orion, Maxfield, Cameron, McClean, Jarrod R., Meeks, Seneca, Megrant, Anthony, Miao, Kevin C., Molavi, Reza, Molina, Sebastian, Montazeri, Shirin, Movassagh, Ramis, Neill, Charles, Newman, Michael, Nguyen, Anthony, Nguyen, Murray, Ni, Chia-Hung, Niu, Murphy Y., Oas, Logan, Oliver, William D., Orosco, Raymond, Ottosson, Kristoffer, Pizzuto, Alex, Potter, Rebecca, Pritchard, Orion, Quintana, Chris, Ramachandran, Ganesh, Reagor, Matthew J., Resnick, Rachel, Rhodes, David M., Roberts, Gabrielle, Rosenberg, Eliott, Rosenfeld, Emma, Rossi, Elizabeth, Roushan, Pedram, Sankaragomathi, Kannan, Schurkus, Henry F., Shearn, Michael J., Shorter, Aaron, Shvarts, Vladimir, Small, Spencer, Smith, W. Clarke, Springer, Sofia, Sterling, George, Suchard, Jordan, Szasz, Aaron, Sztein, Alex, Thor, Douglas, Tomita, Eifu, Torres, Alfredo, Torunbalci, M. Mert, Vaishnav, Abeer, Vargas, Justin, Vdovichev, Sergey, Vidal, Guifre, Heidweiller, Catherine Vollgraff, Waltman, Steven, Waltz, Jonathan, Wang, Shannon X., Ware, Brayden, Weidel, Travis, White, Theodore, Wong, Kristi, Woo, Bryan W. K., Woodson, Maddy, Xing, Cheng, Yao, Z. Jamie, Yeh, Ping, Ying, Bicheng, Yoo, Juhwan, Yosri, Noureldin, Young, Grayson, Zhang, Yaxing, Zhu, Ningfeng, Zobrist, Nicholas, Neven, Hartmut, Kohli, Pushmeet, Davies, Alex, Boixo, Sergio, Kelly, Julian, Jones, Cody, Gidney, Craig, and Satzinger, Kevin J.

Subjects
Quantum Physics
Abstract

Quantum error correction is essential for bridging the gap between the error rates of physical devices and the extremely low logical error rates required for quantum algorithms. Recent error-correction demonstrations on superconducting processors have focused primarily on the surface code, which offers a high error threshold but poses limitations for logical operations. In contrast, the color code enables much more efficient logic, although it requires more complex stabilizer measurements and decoding techniques. Measuring these stabilizers in planar architectures such as superconducting qubits is challenging, and so far, realizations of color codes have not addressed performance scaling with code size on any platform. Here, we present a comprehensive demonstration of the color code on a superconducting processor, achieving logical error suppression and performing logical operations. Scaling the code distance from three to five suppresses logical errors by a factor of $\Lambda_{3/5}$ = 1.56(4). Simulations indicate this performance is below the threshold of the color code, and furthermore that the color code may be more efficient than the surface code with modest device improvements. Using logical randomized benchmarking, we find that transversal Clifford gates add an error of only 0.0027(3), which is substantially less than the error of an idling error correction cycle. We inject magic states, a key resource for universal computation, achieving fidelities exceeding 99% with post-selection (retaining about 75% of the data). Finally, we successfully teleport logical states between distance-three color codes using lattice surgery, with teleported state fidelities between 86.5(1)% and 90.7(1)%. This work establishes the color code as a compelling research direction to realize fault-tolerant quantum computation on superconducting processors in the near future.

Published
2024

4. Fully Automatic Content-Aware Tiling Pipeline for Pathology Whole Slide Images

Author

Jabar, Falah, Busund, Lill-Tove Rasmussen, Ricciuti, Biagio, Tafavvoghi, Masoud, Pøhl, Mette, Andersen, Sigve, Donnem, Tom, Kwiatkowski, David J., and Rakaee, Mehrdad

Subjects
Computer Science - Multimedia
Abstract

In recent years, the use of deep learning (DL) methods, including convolutional neural networks (CNNs) and vision transformers (ViTs), has significantly advanced computational pathology, enhancing both diagnostic accuracy and efficiency. Hematoxylin and Eosin (H&E) Whole Slide Images (WSI) plays a crucial role by providing detailed tissue samples for the analysis and training of DL models. However, WSIs often contain regions with artifacts such as tissue folds, blurring, as well as non-tissue regions (background), which can negatively impact DL model performance. These artifacts are diagnostically irrelevant and can lead to inaccurate results. This paper proposes a fully automatic supervised DL pipeline for WSI Quality Assessment (WSI-QA) that uses a fused model combining CNNs and ViTs to detect and exclude WSI regions with artifacts, ensuring that only qualified WSI regions are used to build DL-based computational pathology applications. The proposed pipeline employs a pixel-based segmentation model to classify WSI regions as either qualified or non-qualified based on the presence of artifacts. The proposed model was trained on a large and diverse dataset and validated with internal and external data from various human organs, scanners, and H&E staining procedures. Quantitative and qualitative evaluations demonstrate the superiority of the proposed model, which outperforms state-of-the-art methods in WSI artifact detection. The proposed model consistently achieved over 95% accuracy, precision, recall, and F1 score across all artifact types. Furthermore, the WSI-QA pipeline shows strong generalization across different tissue types and scanning conditions., Comment: Submitted to Medical Image Analysis journal, February 2025

Published
2024

5. Accelerated Hydration Site Localization and Thermodynamic Profiling

Author

Hinz, Florian B., Masters, Matthew R., Kieu, Julia N., Mahmoud, Amr H., and Lill, Markus A.

Subjects
Quantitative Biology - Biomolecules, Computer Science - Machine Learning
Abstract

Water plays a fundamental role in the structure and function of proteins and other biomolecules. The thermodynamic profile of water molecules surrounding a protein are critical for ligand binding and recognition. Therefore, identifying the location and thermodynamic behavior of relevant water molecules is important for generating and optimizing lead compounds for affinity and selectivity to a given target. Computational methods have been developed to identify these hydration sites, but are largely limited to simplified models that fail to capture multi-body interactions, or dynamics-based methods that rely on extensive sampling. Here we present a method for fast and accurate localization and thermodynamic profiling of hydration sites for protein structures. The method is based on a geometric deep neural network trained on a large, novel dataset of explicit water molecular dynamics simulations. We confirm the accuracy and robustness of our model on experimental data and demonstrate it's utility on several case studies.

Published
2024

6. Artificial intelligence to improve clinical coding practice in Scandinavia: a crossover randomized controlled trial

Author

Chomutare, Taridzo, Svenning, Therese Olsen, Hernández, Miguel Ángel Tejedor, Ngo, Phuong Dinh, Budrionis, Andrius, Markljung, Kaisa, Hind, Lill Irene, Torsvik, Torbjørn, Mikalsen, Karl Øyvind, Babic, Aleksandar, and Dalianis, Hercules

Subjects
Computer Science - Computers and Society, Computer Science - Artificial Intelligence, Computer Science - Computation and Language, Computer Science - Human-Computer Interaction, Computer Science - Machine Learning
Abstract

\textbf{Trial design} Crossover randomized controlled trial. \textbf{Methods} An AI tool, Easy-ICD, was developed to assist clinical coders and was tested for improving both accuracy and time in a user study in Norway and Sweden. Participants were randomly assigned to two groups, and crossed over between coding complex (longer) texts versus simple (shorter) texts, while using our tool versus not using our tool. \textbf{Results} Based on Mann-Whitney U test, the median coding time difference for complex clinical text sequences was 123 seconds (\emph{P}\textless.001, 95\% CI: 81 to 164), representing a 46\% reduction in median coding time when our tool is used. There was no significant time difference for simpler text sequences. For coding accuracy, the improvement we noted for both complex and simple texts was not significant. \textbf{Conclusions} This study demonstrates the potential of AI to transform common tasks in clinical workflows, with ostensible positive impacts on work efficiencies for complex clinical coding tasks. Further studies within hospital workflows are required before these presumed impacts can be more clearly understood., Comment: 13 pages, 4 figures, 4 tables

Published
2024

7. Observation of disorder-free localization and efficient disorder averaging on a quantum processor

Author

Gyawali, Gaurav, Cochran, Tyler, Lensky, Yuri, Rosenberg, Eliott, Karamlou, Amir H., Kechedzhi, Kostyantyn, Berndtsson, Julia, Westerhout, Tom, Asfaw, Abraham, Abanin, Dmitry, Acharya, Rajeev, Beni, Laleh Aghababaie, Andersen, Trond I., Ansmann, Markus, Arute, Frank, Arya, Kunal, Astrakhantsev, Nikita, Atalaya, Juan, Babbush, Ryan, Ballard, Brian, Bardin, Joseph C., Bengtsson, Andreas, Bilmes, Alexander, Bortoli, Gina, Bourassa, Alexandre, Bovaird, Jenna, Brill, Leon, Broughton, Michael, Browne, David A., Buchea, Brett, Buckley, Bob B., Buell, David A., Burger, Tim, Burkett, Brian, Bushnell, Nicholas, Cabrera, Anthony, Campero, Juan, Chang, Hung-Shen, Chen, Zijun, Chiaro, Ben, Claes, Jahan, Cleland, Agnetta Y., Cogan, Josh, Collins, Roberto, Conner, Paul, Courtney, William, Crook, Alexander L., Das, Sayan, Debroy, Dripto M., De Lorenzo, Laura, Barba, Alexander Del Toro, Demura, Sean, Di Paolo, Agustin, Donohoe, Paul, Drozdov, Ilya, Dunsworth, Andrew, Earle, Clint, Eickbusch, Alec, Elbag, Aviv Moshe, Elzouka, Mahmoud, Erickson, Catherine, Faoro, Lara, Fatemi, Reza, Ferreira, Vinicius S., Burgos, Leslie Flores, Forati, Ebrahim, Fowler, Austin G., Foxen, Brooks, Ganjam, Suhas, Gasca, Robert, Giang, William, Gidney, Craig, Gilboa, Dar, Gosula, Raja, Dau, Alejandro Grajales, Graumann, Dietrich, Greene, Alex, Gross, Jonathan A., Habegger, Steve, Hamilton, Michael C., Hansen, Monica, Harrigan, Matthew P., Harrington, Sean D., Heslin, Stephen, Heu, Paula, Hill, Gordon, Hilton, Jeremy, Hoffmann, Markus R., Huang, Hsin-Yuan, Huff, Ashley, Huggins, William J., Ioffe, Lev B., Isakov, Sergei V., Jeffrey, Evan, Jiang, Zhang, Jones, Cody, Jordan, Stephen, Joshi, Chaitali, Juhas, Pavol, Kafri, Dvir, Kang, Hui, Khaire, Trupti, Khattar, Tanuj, Khezri, Mostafa, Kieferová, Mária, Kim, Seon, Klimov, Paul V., Klots, Andrey R., Kobrin, Bryce, Korotkov, Alexander N., Kostritsa, Fedor, Kreikebaum, John Mark, Kurilovich, Vladislav D., Landhuis, David, Lange-Dei, Tiano, Langley, Brandon W., Laptev, Pavel, Lau, Kim-Ming, Guevel, Loïck Le, Ledford, Justin, Lee, Joonho, Lee, Kenny, Lester, Brian J., Li, Wing Yan, Lill, Alexander T., Liu, Wayne, Livingston, William P., Locharla, Aditya, Lundahl, Daniel, Lunt, Aaron, Madhuk, Sid, Maloney, Ashley, Mandrà, Salvatore, Martin, Leigh S., Martin, Steven, Martin, Orion, Maxfield, Cameron, McClean, Jarrod R., McEwen, Matt, Meeks, Seneca, Megrant, Anthony, Mi, Xiao, Miao, Kevin C., Mieszala, Amanda, Molina, Sebastian, Montazeri, Shirin, Morvan, Alexis, Movassagh, Ramis, Neill, Charles, Nersisyan, Ani, Newman, Michael, Nguyen, Anthony, Nguyen, Murray, Ni, Chia-Hung, Niu, Murphy Yuezhen, Oliver, William D., Ottosson, Kristoffer, Pizzuto, Alex, Potter, Rebecca, Pritchard, Orion, Pryadko, Leonid P., Quintana, Chris, Reagor, Matthew J., Rhodes, David M., Roberts, Gabrielle, Rocque, Charles, Rubin, Nicholas C., Saei, Negar, Sankaragomathi, Kannan, Satzinger, Kevin J., Schurkus, Henry F., Schuster, Christopher, Shearn, Michael J., Shorter, Aaron, Shutty, Noah, Shvarts, Vladimir, Sivak, Volodymyr, Skruzny, Jindra, Small, Spencer, Smith, W. Clarke, Springer, Sofia, Sterling, George, Suchard, Jordan, Szalay, Marco, Szasz, Aaron, Sztein, Alex, Thor, Douglas, Torunbalci, M. Mert, Vaishnav, Abeer, Vdovichev, Sergey, Vidal, Guifré, Heidweiller, Catherine Vollgraff, Waltman, Steven, Wang, Shannon X., White, Theodore, Wong, Kristi, Woo, Bryan W. K., Xing, Cheng, Yao, Z. Jamie, Yeh, Ping, Ying, Bicheng, Yoo, Juhwan, Yosri, Noureldin, Young, Grayson, Zalcman, Adam, Zhang, Yaxing, Zhu, Ningfeng, Zobrist, Nicholas, Boixo, Sergio, Kelly, Julian, Lucero, Erik, Chen, Yu, Smelyanskiy, Vadim, Neven, Hartmut, Kovrizhin, Dmitry, Knolle, Johannes, Halimeh, Jad C., Aleiner, Igor, Moessner, Roderich, and Roushan, Pedram

Subjects
Quantum Physics, Condensed Matter - Disordered Systems and Neural Networks, Condensed Matter - Strongly Correlated Electrons, High Energy Physics - Lattice
Abstract

One of the most challenging problems in the computational study of localization in quantum manybody systems is to capture the effects of rare events, which requires sampling over exponentially many disorder realizations. We implement an efficient procedure on a quantum processor, leveraging quantum parallelism, to efficiently sample over all disorder realizations. We observe localization without disorder in quantum many-body dynamics in one and two dimensions: perturbations do not diffuse even though both the generator of evolution and the initial states are fully translationally invariant. The disorder strength as well as its density can be readily tuned using the initial state. Furthermore, we demonstrate the versatility of our platform by measuring Renyi entropies. Our method could also be extended to higher moments of the physical observables and disorder learning.

Published
2024

8. Visualizing Dynamics of Charges and Strings in (2+1)D Lattice Gauge Theories

Author

Cochran, Tyler A., Jobst, Bernhard, Rosenberg, Eliott, Lensky, Yuri D., Gyawali, Gaurav, Eassa, Norhan, Will, Melissa, Abanin, Dmitry, Acharya, Rajeev, Beni, Laleh Aghababaie, Andersen, Trond I., Ansmann, Markus, Arute, Frank, Arya, Kunal, Asfaw, Abraham, Atalaya, Juan, Babbush, Ryan, Ballard, Brian, Bardin, Joseph C., Bengtsson, Andreas, Bilmes, Alexander, Bourassa, Alexandre, Bovaird, Jenna, Broughton, Michael, Browne, David A., Buchea, Brett, Buckley, Bob B., Burger, Tim, Burkett, Brian, Bushnell, Nicholas, Cabrera, Anthony, Campero, Juan, Chang, Hung-Shen, Chen, Zijun, Chiaro, Ben, Claes, Jahan, Cleland, Agnetta Y., Cogan, Josh, Collins, Roberto, Conner, Paul, Courtney, William, Crook, Alexander L., Curtin, Ben, Das, Sayan, Demura, Sean, De Lorenzo, Laura, Di Paolo, Agustin, Donohoe, Paul, Drozdov, Ilya, Dunsworth, Andrew, Eickbusch, Alec, Elbag, Aviv Moshe, Elzouka, Mahmoud, Erickson, Catherine, Ferreira, Vinicius S., Burgos, Leslie Flores, Forati, Ebrahim, Fowler, Austin G., Foxen, Brooks, Ganjam, Suhas, Gasca, Robert, Genois, Élie, Giang, William, Gilboa, Dar, Gosula, Raja, Dau, Alejandro Grajales, Graumann, Dietrich, Greene, Alex, Gross, Jonathan A., Habegger, Steve, Hansen, Monica, Harrigan, Matthew P., Harrington, Sean D., Heu, Paula, Higgott, Oscar, Hilton, Jeremy, Huang, Hsin-Yuan, Huff, Ashley, Huggins, William J., Jeffrey, Evan, Jiang, Zhang, Jones, Cody, Joshi, Chaitali, Juhas, Pavol, Kafri, Dvir, Kang, Hui, Karamlou, Amir H., Kechedzhi, Kostyantyn, Khaire, Trupti, Khattar, Tanuj, Khezri, Mostafa, Kim, Seon, Klimov, Paul V., Kobrin, Bryce, Korotkov, Alexander N., Kostritsa, Fedor, Kreikebaum, John Mark, Kurilovich, Vladislav D., Landhuis, David, Lange-Dei, Tiano, Langley, Brandon W., Lau, Kim-Ming, Ledford, Justin, Lee, Kenny, Lester, Brian J., Guevel, Loïck Le, Li, Wing Yan, Lill, Alexander T., Livingston, William P., Locharla, Aditya, Lundahl, Daniel, Lunt, Aaron, Madhuk, Sid, Maloney, Ashley, Mandrà, Salvatore, Martin, Leigh S., Martin, Orion, Maxfield, Cameron, McClean, Jarrod R., McEwen, Matt, Meeks, Seneca, Megrant, Anthony, Miao, Kevin C., Molavi, Reza, Molina, Sebastian, Montazeri, Shirin, Movassagh, Ramis, Neill, Charles, Newman, Michael, Nguyen, Anthony, Nguyen, Murray, Ni, Chia-Hung, Niu, Murphy Yuezhen, Oliver, William D., Ottosson, Kristoffer, Pizzuto, Alex, Potter, Rebecca, Pritchard, Orion, Quintana, Chris, Ramachandran, Ganesh, Reagor, Matthew J., Rhodes, David M., Roberts, Gabrielle, Sankaragomathi, Kannan, Satzinger, Kevin J., Schurkus, Henry F., Shearn, Michael J., Shorter, Aaron, Shutty, Noah, Shvarts, Vladimir, Sivak, Volodymyr, Small, Spencer, Smith, W. Clarke, Springer, Sofia, Sterling, George, Suchard, Jordan, Szasz, Aaron, Sztein, Alex, Thor, Douglas, Torunbalci, M. Mert, Vaishnav, Abeer, Vargas, Justin, Vdovichev, Sergey, Vidal, Guifre, Heidweiller, Catherine Vollgraff, Waltman, Steven, Wang, Shannon X., Ware, Brayden, White, Theodore, Wong, Kristi, Woo, Bryan W. K., Xing, Cheng, Yao, Z. Jamie, Yeh, Ping, Ying, Bicheng, Yoo, Juhwan, Yosri, Noureldin, Young, Grayson, Zalcman, Adam, Zhang, Yaxing, Zhu, Ningfeng, Zobris, Nicholas, Boixo, Sergio, Kelly, Julian, Lucero, Erik, Chen, Yu, Smelyanskiy, Vadim, Neven, Hartmut, Gammon-Smith, Adam, Pollmann, Frank, Knap, Michael, and Roushan, Pedram

Subjects
Quantum Physics, Condensed Matter - Strongly Correlated Electrons, High Energy Physics - Lattice
Abstract

Lattice gauge theories (LGTs) can be employed to understand a wide range of phenomena, from elementary particle scattering in high-energy physics to effective descriptions of many-body interactions in materials. Studying dynamical properties of emergent phases can be challenging as it requires solving many-body problems that are generally beyond perturbative limits. We investigate the dynamics of local excitations in a $\mathbb{Z}_2$ LGT using a two-dimensional lattice of superconducting qubits. We first construct a simple variational circuit which prepares low-energy states that have a large overlap with the ground state; then we create particles with local gates and simulate their quantum dynamics via a discretized time evolution. As the effective magnetic field is increased, our measurements show signatures of transitioning from deconfined to confined dynamics. For confined excitations, the magnetic field induces a tension in the string connecting them. Our method allows us to experimentally image string dynamics in a (2+1)D LGT from which we uncover two distinct regimes inside the confining phase: for weak confinement the string fluctuates strongly in the transverse direction, while for strong confinement transverse fluctuations are effectively frozen. In addition, we demonstrate a resonance condition at which dynamical string breaking is facilitated. Our LGT implementation on a quantum processor presents a novel set of techniques for investigating emergent particle and string dynamics.

Published
2024

9. Deep learning-based classification of breast cancer molecular subtypes from H&E whole-slide images

Author

Tafavvoghi, Masoud, Sildnes, Anders, Rakaee, Mehrdad, Shvetsov, Nikita, Bongo, Lars Ailo, Busund, Lill-Tove Rasmussen, and Møllersen, Kajsa

Subjects
Electrical Engineering and Systems Science - Image and Video Processing, Computer Science - Artificial Intelligence, Computer Science - Computer Vision and Pattern Recognition
Abstract

Classifying breast cancer molecular subtypes is crucial for tailoring treatment strategies. While immunohistochemistry (IHC) and gene expression profiling are standard methods for molecular subtyping, IHC can be subjective, and gene profiling is costly and not widely accessible in many regions. Previous approaches have highlighted the potential application of deep learning models on H&E-stained whole slide images (WSI) for molecular subtyping, but these efforts vary in their methods, datasets, and reported performance. In this work, we investigated whether H&E-stained WSIs could be solely leveraged to predict breast cancer molecular subtypes (luminal A, B, HER2-enriched, and Basal). We used 1,433 WSIs of breast cancer in a two-step pipeline: first, classifying tumor and non-tumor tiles to use only the tumor regions for molecular subtyping; and second, employing a One-vs-Rest (OvR) strategy to train four binary OvR classifiers and aggregating their results using an eXtreme Gradient Boosting (XGBoost) model. The pipeline was tested on 221 hold-out WSIs, achieving an overall macro F1 score of 0.95 for tumor detection and 0.73 for molecular subtyping. Our findings suggest that, with further validation, supervised deep learning models could serve as supportive tools for molecular subtyping in breast cancer. Our codes are made available to facilitate ongoing research and development., Comment: 16 pages, 5 figures (+4 supplementary figures), 4 tables

Published
2024

10. Quantum error correction below the surface code threshold

Author

Acharya, Rajeev, Aghababaie-Beni, Laleh, Aleiner, Igor, Andersen, Trond I., Ansmann, Markus, Arute, Frank, Arya, Kunal, Asfaw, Abraham, Astrakhantsev, Nikita, Atalaya, Juan, Babbush, Ryan, Bacon, Dave, Ballard, Brian, Bardin, Joseph C., Bausch, Johannes, Bengtsson, Andreas, Bilmes, Alexander, Blackwell, Sam, Boixo, Sergio, Bortoli, Gina, Bourassa, Alexandre, Bovaird, Jenna, Brill, Leon, Broughton, Michael, Browne, David A., Buchea, Brett, Buckley, Bob B., Buell, David A., Burger, Tim, Burkett, Brian, Bushnell, Nicholas, Cabrera, Anthony, Campero, Juan, Chang, Hung-Shen, Chen, Yu, Chen, Zijun, Chiaro, Ben, Chik, Desmond, Chou, Charina, Claes, Jahan, Cleland, Agnetta Y., Cogan, Josh, Collins, Roberto, Conner, Paul, Courtney, William, Crook, Alexander L., Curtin, Ben, Das, Sayan, Davies, Alex, De Lorenzo, Laura, Debroy, Dripto M., Demura, Sean, Devoret, Michel, Di Paolo, Agustin, Donohoe, Paul, Drozdov, Ilya, Dunsworth, Andrew, Earle, Clint, Edlich, Thomas, Eickbusch, Alec, Elbag, Aviv Moshe, Elzouka, Mahmoud, Erickson, Catherine, Faoro, Lara, Farhi, Edward, Ferreira, Vinicius S., Burgos, Leslie Flores, Forati, Ebrahim, Fowler, Austin G., Foxen, Brooks, Ganjam, Suhas, Garcia, Gonzalo, Gasca, Robert, Genois, Élie, Giang, William, Gidney, Craig, Gilboa, Dar, Gosula, Raja, Dau, Alejandro Grajales, Graumann, Dietrich, Greene, Alex, Gross, Jonathan A., Habegger, Steve, Hall, John, Hamilton, Michael C., Hansen, Monica, Harrigan, Matthew P., Harrington, Sean D., Heras, Francisco J. H., Heslin, Stephen, Heu, Paula, Higgott, Oscar, Hill, Gordon, Hilton, Jeremy, Holland, George, Hong, Sabrina, Huang, Hsin-Yuan, Huff, Ashley, Huggins, William J., Ioffe, Lev B., Isakov, Sergei V., Iveland, Justin, Jeffrey, Evan, Jiang, Zhang, Jones, Cody, Jordan, Stephen, Joshi, Chaitali, Juhas, Pavol, Kafri, Dvir, Kang, Hui, Karamlou, Amir H., Kechedzhi, Kostyantyn, Kelly, Julian, Khaire, Trupti, Khattar, Tanuj, Khezri, Mostafa, Kim, Seon, Klimov, Paul V., Klots, Andrey R., Kobrin, Bryce, Kohli, Pushmeet, Korotkov, Alexander N., Kostritsa, Fedor, Kothari, Robin, Kozlovskii, Borislav, Kreikebaum, John Mark, Kurilovich, Vladislav D., Lacroix, Nathan, Landhuis, David, Lange-Dei, Tiano, Langley, Brandon W., Laptev, Pavel, Lau, Kim-Ming, Guevel, Loïck Le, Ledford, Justin, Lee, Kenny, Lensky, Yuri D., Leon, Shannon, Lester, Brian J., Li, Wing Yan, Li, Yin, Lill, Alexander T., Liu, Wayne, Livingston, William P., Locharla, Aditya, Lucero, Erik, Lundahl, Daniel, Lunt, Aaron, Madhuk, Sid, Malone, Fionn D., Maloney, Ashley, Mandrá, Salvatore, Martin, Leigh S., Martin, Steven, Martin, Orion, Maxfield, Cameron, McClean, Jarrod R., McEwen, Matt, Meeks, Seneca, Megrant, Anthony, Mi, Xiao, Miao, Kevin C., Mieszala, Amanda, Molavi, Reza, Molina, Sebastian, Montazeri, Shirin, Morvan, Alexis, Movassagh, Ramis, Mruczkiewicz, Wojciech, Naaman, Ofer, Neeley, Matthew, Neill, Charles, Nersisyan, Ani, Neven, Hartmut, Newman, Michael, Ng, Jiun How, Nguyen, Anthony, Nguyen, Murray, Ni, Chia-Hung, O'Brien, Thomas E., Oliver, William D., Opremcak, Alex, Ottosson, Kristoffer, Petukhov, Andre, Pizzuto, Alex, Platt, John, Potter, Rebecca, Pritchard, Orion, Pryadko, Leonid P., Quintana, Chris, Ramachandran, Ganesh, Reagor, Matthew J., Rhodes, David M., Roberts, Gabrielle, Rosenberg, Eliott, Rosenfeld, Emma, Roushan, Pedram, Rubin, Nicholas C., Saei, Negar, Sank, Daniel, Sankaragomathi, Kannan, Satzinger, Kevin J., Schurkus, Henry F., Schuster, Christopher, Senior, Andrew W., Shearn, Michael J., Shorter, Aaron, Shutty, Noah, Shvarts, Vladimir, Singh, Shraddha, Sivak, Volodymyr, Skruzny, Jindra, Small, Spencer, Smelyanskiy, Vadim, Smith, W. Clarke, Somma, Rolando D., Springer, Sofia, Sterling, George, Strain, Doug, Suchard, Jordan, Szasz, Aaron, Sztein, Alex, Thor, Douglas, Torres, Alfredo, Torunbalci, M. Mert, Vaishnav, Abeer, Vargas, Justin, Vdovichev, Sergey, Vidal, Guifre, Villalonga, Benjamin, Heidweiller, Catherine Vollgraff, Waltman, Steven, Wang, Shannon X., Ware, Brayden, Weber, Kate, White, Theodore, Wong, Kristi, Woo, Bryan W. K., Xing, Cheng, Yao, Z. Jamie, Yeh, Ping, Ying, Bicheng, Yoo, Juhwan, Yosri, Noureldin, Young, Grayson, Zalcman, Adam, Zhang, Yaxing, Zhu, Ningfeng, and Zobrist, Nicholas

Subjects
Quantum Physics
Abstract

Quantum error correction provides a path to reach practical quantum computing by combining multiple physical qubits into a logical qubit, where the logical error rate is suppressed exponentially as more qubits are added. However, this exponential suppression only occurs if the physical error rate is below a critical threshold. In this work, we present two surface code memories operating below this threshold: a distance-7 code and a distance-5 code integrated with a real-time decoder. The logical error rate of our larger quantum memory is suppressed by a factor of $\Lambda$ = 2.14 $\pm$ 0.02 when increasing the code distance by two, culminating in a 101-qubit distance-7 code with 0.143% $\pm$ 0.003% error per cycle of error correction. This logical memory is also beyond break-even, exceeding its best physical qubit's lifetime by a factor of 2.4 $\pm$ 0.3. We maintain below-threshold performance when decoding in real time, achieving an average decoder latency of 63 $\mu$s at distance-5 up to a million cycles, with a cycle time of 1.1 $\mu$s. To probe the limits of our error-correction performance, we run repetition codes up to distance-29 and find that logical performance is limited by rare correlated error events occurring approximately once every hour, or 3 $\times$ 10$^9$ cycles. Our results present device performance that, if scaled, could realize the operational requirements of large scale fault-tolerant quantum algorithms., Comment: 10 pages, 4 figures, Supplementary Information

Published
2024

11. Linear-Time MaxCut in Multigraphs Parameterized Above the Poljak-Turz\'ik Bound

Author

Lill, Jonas, Petrova, Kalina, and Weber, Simon

Subjects
Computer Science - Data Structures and Algorithms, Computer Science - Computational Complexity, Computer Science - Discrete Mathematics
Abstract

MaxCut is a classical NP-complete problem and a crucial building block in many combinatorial algorithms. The famous Edwards-Erd\H{o}s bound states that any connected graph on n vertices with m edges contains a cut of size at least $m/2 + (n-1)/4$. Crowston, Jones and Mnich [Algorithmica, 2015] showed that the MaxCut problem on simple connected graphs admits an FPT algorithm, where the parameter k is the difference between the desired cut size c and the lower bound given by the Edwards-Erd\H{o}s bound. This was later improved by Etscheid and Mnich [Algorithmica, 2017] to run in parameterized linear time, i.e., $f(k)\cdot O(m)$. We improve upon this result in two ways: Firstly, we extend the algorithm to work also for multigraphs (alternatively, graphs with positive integer weights). Secondly, we change the parameter; instead of the difference to the Edwards-Erd\H{o}s bound, we use the difference to the Poljak-Turz\'ik bound. The Poljak-Turz\'ik bound states that any weighted graph G has a cut of size at least $w(G)/2 + w_{MSF}(G)/4$, where w(G) denotes the total weight of G, and $w_{MSF}(G)$ denotes the weight of its minimum spanning forest. In connected simple graphs the two bounds are equivalent, but for multigraphs the Poljak-Turz\'ik bound can be larger and thus yield a smaller parameter k. Our algorithm also runs in parameterized linear time, i.e., $f(k)\cdot O(m+n)$., Comment: 20 pages

Published
2024

13. Thermalization and criticality on an analogue–digital quantum simulator

Author

Andersen, T. I., Astrakhantsev, N., Karamlou, A. H., Berndtsson, J., Motruk, J., Szasz, A., Gross, J. A., Schuckert, A., Westerhout, T., Zhang, Y., Forati, E., Rossi, D., Kobrin, B., Paolo, A. Di, Klots, A. R., Drozdov, I., Kurilovich, V., Petukhov, A., Ioffe, L. B., Elben, A., Rath, A., Vitale, V., Vermersch, B., Acharya, R., Beni, L. A., Anderson, K., Ansmann, M., Arute, F., Arya, K., Asfaw, A., Atalaya, J., Ballard, B., Bardin, J. C., Bengtsson, A., Bilmes, A., Bortoli, G., Bourassa, A., Bovaird, J., Brill, L., Broughton, M., Browne, D. A., Buchea, B., Buckley, B. B., Buell, D. A., Burger, T., Burkett, B., Bushnell, N., Cabrera, A., Campero, J., Chang, H.-S., Chen, Z., Chiaro, B., Claes, J., Cleland, A. Y., Cogan, J., Collins, R., Conner, P., Courtney, W., Crook, A. L., Das, S., Debroy, D. M., Lorenzo, L. De, Barba, A. Del Toro, Demura, S., Donohoe, P., Dunsworth, A., Earle, C., Eickbusch, A., Elbag, A. M., Elzouka, M., Erickson, C., Faoro, L., Fatemi, R., Ferreira, V. S., Burgos, L. Flores, Fowler, A. G., Foxen, B., Ganjam, S., Gasca, R., Giang, W., Gidney, C., Gilboa, D., Giustina, M., Gosula, R., Dau, A. Grajales, Graumann, D., Greene, A., Habegger, S., Hamilton, M. C., Hansen, M., Harrigan, M. P., Harrington, S. D., Heslin, S., Heu, P., Hill, G., Hoffmann, M. R., Huang, H.-Y., Huang, T., Huff, A., Huggins, W. J., Isakov, S. V., Jeffrey, E., Jiang, Z., Jones, C., Jordan, S., Joshi, C., Juhas, P., Kafri, D., Kang, H., Kechedzhi, K., Khaire, T., Khattar, T., Khezri, M., Kieferová, M., Kim, S., Kitaev, A., Klimov, P., Korotkov, A. N., Kostritsa, F., Kreikebaum, J. M., Landhuis, D., Langley, B. W., Laptev, P., Lau, K.-M., Guevel, L. Le, Ledford, J., Lee, J., Lee, K. W., Lensky, Y. D., Lester, B. J., Li, W. Y., Lill, A. T., Liu, W., Livingston, W. P., Locharla, A., Lundahl, D., Lunt, A., Madhuk, S., Maloney, A., Mandrà, S., Martin, L. S., Martin, O., Martin, S., Maxfield, C., McClean, J. R., McEwen, M., Meeks, S., Miao, K. C., Mieszala, A., Molina, S., Montazeri, S., Morvan, A., Movassagh, R., Neill, C., Nersisyan, A., Newman, M., Nguyen, A., Nguyen, M., Ni, C.-H., Niu, M. Y., Oliver, W. D., Ottosson, K., Pizzuto, A., Potter, R., Pritchard, O., Pryadko, L. P., Quintana, C., Reagor, M. J., Rhodes, D. M., Roberts, G., Rocque, C., Rosenberg, E., Rubin, N. C., Saei, N., Sankaragomathi, K., Satzinger, K. J., Schurkus, H. F., Schuster, C., Shearn, M. J., Shorter, A., Shutty, N., Shvarts, V., Sivak, V., Skruzny, J., Small, S., Smith, W. Clarke, Springer, S., Sterling, G., Suchard, J., Szalay, M., Sztein, A., Thor, D., Torres, A., Torunbalci, M. M., Vaishnav, A., Vdovichev, S., Villalonga, B., Heidweiller, C. Vollgraff, Waltman, S., Wang, S. X., White, T., Wong, K., Woo, B. W. K., Xing, C., Yao, Z. Jamie, Yeh, P., Ying, B., Yoo, J., Yosri, N., Young, G., Zalcman, A., Zhu, N., Zobrist, N., Neven, H., Babbush, R., Boixo, S., Hilton, J., Lucero, E., Megrant, A., Kelly, J., Chen, Y., Smelyanskiy, V., Vidal, G., Roushan, P., Läuchli, A. M., Abanin, D. A., and Mi, X.

Published
2025
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16. Thermalization and Criticality on an Analog-Digital Quantum Simulator

Author

Andersen, Trond I., Astrakhantsev, Nikita, Karamlou, Amir H., Berndtsson, Julia, Motruk, Johannes, Szasz, Aaron, Gross, Jonathan A., Schuckert, Alexander, Westerhout, Tom, Zhang, Yaxing, Forati, Ebrahim, Rossi, Dario, Kobrin, Bryce, Di Paolo, Agustin, Klots, Andrey R., Drozdov, Ilya, Kurilovich, Vladislav D., Petukhov, Andre, Ioffe, Lev B., Elben, Andreas, Rath, Aniket, Vitale, Vittorio, Vermersch, Benoit, Acharya, Rajeev, Beni, Laleh Aghababaie, Anderson, Kyle, Ansmann, Markus, Arute, Frank, Arya, Kunal, Asfaw, Abraham, Atalaya, Juan, Ballard, Brian, Bardin, Joseph C., Bengtsson, Andreas, Bilmes, Alexander, Bortoli, Gina, Bourassa, Alexandre, Bovaird, Jenna, Brill, Leon, Broughton, Michael, Browne, David A., Buchea, Brett, Buckley, Bob B., Buell, David A., Burger, Tim, Burkett, Brian, Bushnell, Nicholas, Cabrera, Anthony, Campero, Juan, Chang, Hung-Shen, Chen, Zijun, Chiaro, Ben, Claes, Jahan, Cleland, Agnetta Y., Cogan, Josh, Collins, Roberto, Conner, Paul, Courtney, William, Crook, Alexander L., Das, Sayan, Debroy, Dripto M., De Lorenzo, Laura, Barba, Alexander Del Toro, Demura, Sean, Donohoe, Paul, Dunsworth, Andrew, Earle, Clint, Eickbusch, Alec, Elbag, Aviv Moshe, Elzouka, Mahmoud, Erickson, Catherine, Faoro, Lara, Fatemi, Reza, Ferreira, Vinicius S., Burgos, Leslie Flores, Fowler, Austin G., Foxen, Brooks, Ganjam, Suhas, Gasca, Robert, Giang, William, Gidney, Craig, Gilboa, Dar, Giustina, Marissa, Gosula, Raja, Dau, Alejandro Grajales, Graumann, Dietrich, Greene, Alex, Habegger, Steve, Hamilton, Michael C., Hansen, Monica, Harrigan, Matthew P., Harrington, Sean D., Heslin, Stephen, Heu, Paula, Hill, Gordon, Hoffmann, Markus R., Huang, Hsin-Yuan, Huang, Trent, Huff, Ashley, Huggins, William J., Isakov, Sergei V., Jeffrey, Evan, Jiang, Zhang, Jones, Cody, Jordan, Stephen, Joshi, Chaitali, Juhas, Pavol, Kafri, Dvir, Kang, Hui, Kechedzhi, Kostyantyn, Khaire, Trupti, Khattar, Tanuj, Khezri, Mostafa, Kieferová, Mária, Kim, Seon, Kitaev, Alexei, Klimov, Paul V., Korotkov, Alexander N., Kostritsa, Fedor, Kreikebaum, John Mark, Landhuis, David, Langley, Brandon W., Laptev, Pavel, Lau, Kim-Ming, Guevel, Loïck Le, Ledford, Justin, Lee, Joonho, Lee, Kenny, Lensky, Yuri D., Lester, Brian J., Li, Wing Yan, Lill, Alexander T., Liu, Wayne, Livingston, William P., Locharla, Aditya, Lundahl, Daniel, Lunt, Aaron, Madhuk, Sid, Maloney, Ashley, Mandrà, Salvatore, Martin, Leigh S., Martin, Orion, Martin, Steven, Maxfield, Cameron, McClean, Jarrod R., McEwen, Matt, Meeks, Seneca, Miao, Kevin C., Mieszala, Amanda, Molina, Sebastian, Montazeri, Shirin, Morvan, Alexis, Movassagh, Ramis, Neill, Charles, Nersisyan, Ani, Newman, Michael, Nguyen, Anthony, Nguyen, Murray, Ni, Chia-Hung, Niu, Murphy Yuezhen, Oliver, William D., Ottosson, Kristoffer, Pizzuto, Alex, Potter, Rebecca, Pritchard, Orion, Pryadko, Leonid P., Quintana, Chris, Reagor, Matthew J., Rhodes, David M., Roberts, Gabrielle, Rocque, Charles, Rosenberg, Eliott, Rubin, Nicholas C., Saei, Negar, Sankaragomathi, Kannan, Satzinger, Kevin J., Schurkus, Henry F., Schuster, Christopher, Shearn, Michael J., Shorter, Aaron, Shutty, Noah, Shvarts, Vladimir, Sivak, Volodymyr, Skruzny, Jindra, Small, Spencer, Smith, W. Clarke, Springer, Sofia, Sterling, George, Suchard, Jordan, Szalay, Marco, Sztein, Alex, Thor, Douglas, Torres, Alfredo, Torunbalci, M. Mert, Vaishnav, Abeer, Vdovichev, Sergey, Villalonga, Benjamin, Heidweiller, Catherine Vollgraff, Waltman, Steven, Wang, Shannon X., White, Theodore, Wong, Kristi, Woo, Bryan W., Xing, Cheng, Yao, Z. Jamie, Yeh, Ping, Ying, Bicheng, Yoo, Juhwan, Yosri, Noureldin, Young, Grayson, Zalcman, Adam, Zhu, Ningfeng, Zobrist, Nicholas, Neven, Hartmut, Babbush, Ryan, Boixo, Sergio, Hilton, Jeremy, Lucero, Erik, Megrant, Anthony, Kelly, Julian, Chen, Yu, Smelyanskiy, Vadim, Vidal, Guifre, Roushan, Pedram, Lauchli, Andreas M., Abanin, Dmitry A., and Mi, Xiao

Subjects
Quantum Physics, Condensed Matter - Mesoscale and Nanoscale Physics, Condensed Matter - Strongly Correlated Electrons
Abstract

Understanding how interacting particles approach thermal equilibrium is a major challenge of quantum simulators. Unlocking the full potential of such systems toward this goal requires flexible initial state preparation, precise time evolution, and extensive probes for final state characterization. We present a quantum simulator comprising 69 superconducting qubits which supports both universal quantum gates and high-fidelity analog evolution, with performance beyond the reach of classical simulation in cross-entropy benchmarking experiments. Emulating a two-dimensional (2D) XY quantum magnet, we leverage a wide range of measurement techniques to study quantum states after ramps from an antiferromagnetic initial state. We observe signatures of the classical Kosterlitz-Thouless phase transition, as well as strong deviations from Kibble-Zurek scaling predictions attributed to the interplay between quantum and classical coarsening of the correlated domains. This interpretation is corroborated by injecting variable energy density into the initial state, which enables studying the effects of the eigenstate thermalization hypothesis (ETH) in targeted parts of the eigenspectrum. Finally, we digitally prepare the system in pairwise-entangled dimer states and image the transport of energy and vorticity during thermalization. These results establish the efficacy of superconducting analog-digital quantum processors for preparing states across many-body spectra and unveiling their thermalization dynamics.

Published
2024

17. Fast TILs -- A Pipeline for Efficient TILs Estimation in Non-Small Cell Lung Cancer

Author

Shvetsov, Nikita, Sildnes, Anders, Tafavvoghi, Masoud, Busund, Lill-Tove Rasmussen, Dalen, Stig, Møllersen, Kajsa, Bongo, Lars Ailo, and Kilvaer, Thomas K.

Subjects
Computer Science - Computer Vision and Pattern Recognition, 68T07, I.4.6, I.4.9, J.3
Abstract

Addressing the critical need for accurate prognostic biomarkers in cancer treatment, quantifying tumor-infiltrating lymphocytes (TILs) in non-small cell lung cancer (NSCLC) presents considerable challenges. Manual TIL quantification in whole slide images (WSIs) is laborious and subject to variability, potentially undermining patient outcomes. Our study introduces an automated pipeline that utilizes semi-stochastic patch sampling, patch classification to retain prognostically relevant patches, and cell quantification using the HoVer-Net model to streamline the TIL evaluation process. This pipeline efficiently excludes approximately 70% of areas not relevant for prognosis and requires only 5% of the remaining patches to maintain prognostic accuracy (c-index = 0.65). The computational efficiency achieved does not sacrifice prognostic accuracy, as demonstrated by the TILs score's strong association with patient survival, which outperforms traditional CD8 IHC scoring methods. While the pipeline demonstrates potential for enhancing NSCLC prognostication and personalization of treatment, comprehensive clinical validation is still required. Future research should focus on verifying its broader clinical utility and investigating additional biomarkers to improve NSCLC prognosis., Comment: 25 pages, 10 figures, 7 appendix pages

Published
2024

19. Associations of milk, dairy products, calcium and vitamin D intake with risk of developing Parkinson´s disease within the EPIC4ND cohort

Author

Gröninger, Mareike, Sabin, Jara, Kaaks, Rudolf, Amiano, Pilar, Aune, Dagfinn, Castro, Natalia Cabrera, Guevara, Marcela, Hansen, Johnni, Homann, Jan, Masala, Giovanna, Nicolas, Geneviève, Peters, Susan, Sacerdote, Carlotta, Sánchez, Maria-Jose, De Magistris, Maria Santucci, Sieri, Sabina, Vermeulen, Roel, Zhao, Yujia, Lill, Christina M., and Katzke, Verena A.

Published
2024
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20. Phase transitions in random circuit sampling

Author

Morvan, A., Villalonga, B., Mi, X., Mandrà, S., Bengtsson, A., Klimov, P. V., Chen, Z., Hong, S., Erickson, C., Drozdov, I. K., Chau, J., Laun, G., Movassagh, R., Asfaw, A., Brandão, L. T. A. N., Peralta, R., Abanin, D., Acharya, R., Allen, R., Andersen, T. I., Anderson, K., Ansmann, M., Arute, F., Arya, K., Atalaya, J., Bardin, J. C., Bilmes, A., Bortoli, G., Bourassa, A., Bovaird, J., Brill, L., Broughton, M., Buckley, B. B., Buell, D. A., Burger, T., Burkett, B., Bushnell, N., Campero, J., Chang, H.-S., Chiaro, B., Chik, D., Chou, C., Cogan, J., Collins, R., Conner, P., Courtney, W., Crook, A. L., Curtin, B., Debroy, D. M., Barba, A. Del Toro, Demura, S., Paolo, A. Di, Dunsworth, A., Faoro, L., Farhi, E., Fatemi, R., Ferreira, V. S., Burgos, L. Flores, Forati, E., Fowler, A. G., Foxen, B., Garcia, G., Genois, É., Giang, W., Gidney, C., Gilboa, D., Giustina, M., Gosula, R., Dau, A. Grajales, Gross, J. A., Habegger, S., Hamilton, M. C., Hansen, M., Harrigan, M. P., Harrington, S. D., Heu, P., Hoffmann, M. R., Huang, T., Huff, A., Huggins, W. J., Ioffe, L. B., Isakov, S. V., Iveland, J., Jeffrey, E., Jiang, Z., Jones, C., Juhas, P., Kafri, D., Khattar, T., Khezri, M., Kieferová, M., Kim, S., Kitaev, A., Klots, A. R., Korotkov, A. N., Kostritsa, F., Kreikebaum, J. M., Landhuis, D., Laptev, P., Lau, K.-M., Laws, L., Lee, J., Lee, K. W., Lensky, Y. D., Lester, B. J., Lill, A. T., Liu, W., Livingston, W. P., Locharla, A., Malone, F. D., Martin, O., Martin, S., McClean, J. R., McEwen, M., Miao, K. C., Mieszala, A., Montazeri, S., Mruczkiewicz, W., Naaman, O., Neeley, M., Neill, C., Nersisyan, A., Newman, M., Ng, J. H., Nguyen, A., Nguyen, M., Niu, M. Yuezhen, O’Brien, T. E., Omonije, S., Opremcak, A., Petukhov, A., Potter, R., Pryadko, L. P., Quintana, C., Rhodes, D. M., Rocque, C., Rosenberg, E., Rubin, N. C., Saei, N., Sank, D., Sankaragomathi, K., Satzinger, K. J., Schurkus, H. F., Schuster, C., Shearn, M. J., Shorter, A., Shutty, N., Shvarts, V., Sivak, V., Skruzny, J., Smith, W. C., Somma, R. D., Sterling, G., Strain, D., Szalay, M., Thor, D., Torres, A., Vidal, G., Heidweiller, C. Vollgraff, White, T., Woo, B. W. K., Xing, C., Yao, Z. J., Yeh, P., Yoo, J., Young, G., Zalcman, A., Zhang, Y., Zhu, N., Zobrist, N., Rieffel, E. G., Biswas, R., Babbush, R., Bacon, D., Hilton, J., Lucero, E., Neven, H., Megrant, A., Kelly, J., Roushan, P., Aleiner, I., Smelyanskiy, V., Kechedzhi, K., Chen, Y., and Boixo, S.

Published
2024
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22. Inhibition of tumour necrosis factor alpha by Etanercept attenuates Shiga toxin-induced brain pathology

Author

Robin Christ, Devon Siemes, Shuo Zhao, Lars Widera, Philippa Spangenberg, Julia Lill, Stephanie Thiebes, Jenny Bottek, Lars Borgards, Andreia G. Pinho, Nuno A. Silva, Susana Monteiro, Selina K. Jorch, Matthias Gunzer, Bente Siebels, Hannah Voss, Hartmut Schlüter, Olga Shevchuk, Jianxu Chen, and Daniel R. Engel

Subjects
Neurology. Diseases of the nervous system, RC346-429
Abstract

Abstract Infection with enterohemorrhagic E. coli (EHEC) causes severe changes in the brain leading to angiopathy, encephalopathy and microglial activation. In this study, we investigated the role of tumour necrosis factor alpha (TNF-α) for microglial activation and brain pathology using a preclinical mouse model of EHEC infection. LC–MS/MS proteomics of mice injected with a combination of Shiga toxin (Stx) and lipopolysaccharide (LPS) revealed extensive alterations of the brain proteome, in particular enrichment of pathways involved in complement activation and coagulation cascades. Inhibition of TNF-α by the drug Etanercept strongly mitigated these changes, particularly within the complement pathway, suggesting TNF-α-dependent vasodilation and endothelial injury. Analysis of microglial populations using a novel human-in-the-loop deep learning algorithm for the segmentation of microscopic imaging data indicated specific morphological changes, which were reduced to healthy condition after inhibition of TNF-α. Moreover, the Stx/LPS-mediated angiopathy was significantly attenuated by inhibition of TNF-α. Overall, our findings elucidate the critical role of TNF-α in EHEC-induced brain pathology and highlight a potential therapeutic target for mitigating neuroinflammation, microglial activation and injury associated with EHEC infection. Graphical Abstract

Published
2025
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23. Physiotherapists' Experiences with 'Sterk og Stødig' for Home-Dwelling Older Adults – A Qualitative Study

Author

Ane Brekke, Bård Erik Bogen, Ingebjørg Kyrdalen, and Lill Anette Juvik

Subjects
physiotherapy, fall prevention, strong and steady, Medicine (General), R5-920
Abstract

Aim: To explore physiotherapists' experiences with recruiting, training, and supervising volunteer instructors responsible for leading Sterk og stødig (English: Strong and Steady) exercise groups as part of the municipality's fall prevention services for home-dwelling older adults. Method: Four individual in-depth interviews with physiotherapists employed across various municipalities were analysed using systematic text condensation. Findings: Five themes were identified: (1) A natural role that provides variety in the workday, (2) Recruitment – a new role, (3) Volunteers as bridges for knowledge sharing, (4) Continuous supervision to ensure correct exercise dosage, and (5) Strong and Steady for the target group? The study shows that the training and supervisory roles in Strong and Steady were perceived as natural, characterised by confidence, competence, and a positive attitude towards preventive work with the target group. Volunteer instructors served as knowledge facilitators on exercise and fall prevention for group participants. Recruiting suitable volunteer instructors was essential for both training effectiveness and program continuity but was also resource-intensive for the physiotherapists. There were differing views on including group participants outside the target group. Conclusion: Physiotherapists find that tasks related to the Strong and Steady program align well with their professional roles. Despite the resource-intensive nature of recruitment, collaboration with volunteer group instructors serves as a valuable resource in fall prevention efforts. Identifying appropriate solutions related to recruitment work is essential.

Published
2025

24. Normdaten am laufenden Band

Author

Leiske, Patrick, primary, Lill, Jens, additional, Markert, Michael, additional, Meiners, Hanna-Lena, additional, and Rosenkötter, Martha, additional

Published
2024
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25. Voltage-driven 90 switching of bulk perpendicular magnetic anisotropy in ferrimagnets

Author

Xiao, Zhengyu, Xie, Ruiwen, Maccari, Fernando, Klaassen, Philipp, Eggert, Benedikt, Wang, Di, Dai, Yuting, Lizarraga, Raquel, Lill, Johanna, Helbig, Tom, Wende, Heiko, Kummer, Kurt, Ollefs, Katharina, Skokov, Konstantin, Zhang, Hongbin, Quan, Zhiyong, Xu, Xiaohong, Kruk, Robert, Hahn, Horst, Gutfleisch, Oliver, and Ye, Xinglong

Subjects
Condensed Matter - Materials Science
Abstract

Unravelling the mechanism behind bulk perpendicular magnetic anisotropy (PMA) in amorphous rare earth-transition metal films has proven challenging. This is largely due to the inherent complexity of amorphous structure and the entangled potential origins arising from microstructure and atomic structure factors. Here, we present an approach wherein the magneto-electric effect is harnessed to induce 90{\deg} switching of bulk PMA in Tb-Co films to in-plane directions by applying voltages of only -1.2 V. This manipulation is achieved by voltage-driven insertion of hydrogen atoms into interstitial sites between Tb and Co atoms, which serves as a perturbation to local atomic structure. Using angle-dependent X-ray magnetic circular dichroism, we find that the anisotropy switching originates from the distortion of crystal field around Tb which reorients the alignment of Tb orbital moments. Initially aligned along Tb-Co bonding directions, the easy magnetization axis undergoes reorientation and switches by 90{\deg}, as substantiated by ab-initio calculations. Our study not only concludes the atomic origin of Tb-Co atom bonding configuration in shaping bulk PMA, but also establishes the groundwork for electrically programmable ferrimagnetic spintronics, such as controlling domain wall motion and programming artificial spin textures., Comment: 4 Figures in manuscript (15 pages); 16 Figures, two Tables in Supplementary materials (21 pages)

Published
2023

26. Bosonized Momentum Distribution of a Fermi Gas via Friedrichs Diagrams

Author

Lill, Sascha

Subjects
Mathematical Physics, Condensed Matter - Quantum Gases, Condensed Matter - Strongly Correlated Electrons, 81V70, 81V74, 81Q12, 81S05
Abstract

Recently, Benedikter and the author proved an approximate formula for the momentum distribution of a 3d fermionic gas interacting by a short-range pair potential in the mean-field regime, within a trial state close to the ground state. Here, we derive an exact formula for the momentum distribution in this trial state, using a diagrammatic formalism due to Friedrichs. We further demonstrate how the formula of Benedikter and the author arises from a restriction of the contributing diagrams to those corresponding to a bosonization approximation., Comment: 27 pages, 10 figures

Published
2023

27. Momentum Distribution of a Fermi Gas in the Random Phase Approximation

Author

Benedikter, Niels and Lill, Sascha

Subjects
Mathematical Physics, Condensed Matter - Quantum Gases, Condensed Matter - Strongly Correlated Electrons, 81V70, 82B10, 81Q10, 35P05
Abstract

We consider a system of interacting fermions on the three-dimensional torus in the mean-field scaling limit. Our objective is computing the occupation number of the Fourier modes in a trial state obtained through the random phase approximation for the ground state. Our result shows that -- in the trial state -- the momentum distribution has a jump discontinuity, i. e., the system has a well-defined Fermi surface. Moreover the Fermi momentum does not depend on the interaction potential (it is universal)., Comment: 53 pages, 6 figures. v4: paper shortened by avoiding the gapped number operator

Published
2023

28. Overview of the Alaskan Layered Pollution and Chemical Analysis (ALPACA) Field Experiment

Author

Simpson, William R, Mao, Jingqiu, Fochesatto, Gilberto J, Law, Kathy S, DeCarlo, Peter F, Schmale, Julia, Pratt, Kerri A, Arnold, Steve R, Stutz, Jochen, Dibb, Jack E, Creamean, Jessie M, Weber, Rodney J, Williams, Brent J, Alexander, Becky, Hu, Lu, Yokelson, Robert J, Shiraiwa, Manabu, Decesari, Stefano, Anastasio, Cort, D’Anna, Barbara, Gilliam, Robert C, Nenes, Athanasios, St. Clair, Jason M, Trost, Barbara, Flynn, James H, Savarino, Joel, Conner, Laura D, Kettle, Nathan, Heeringa, Krista M, Albertin, Sarah, Baccarini, Andrea, Barret, Brice, Battaglia, Michael A, Bekki, Slimane, Brado, TJ, Brett, Natalie, Brus, David, Campbell, James R, Cesler-Maloney, Meeta, Cooperdock, Sol, de Carvalho, Karolina Cysneiros, Delbarre, Hervé, DeMott, Paul J, Dennehy, Conor JS, Dieudonné, Elsa, Dingilian, Kayane K, Donateo, Antonio, Doulgeris, Konstantinos M, Edwards, Kasey C, Fahey, Kathleen, Fang, Ting, Guo, Fangzhou, Heinlein, Laura MD, Holen, Andrew L, Huff, Deanna, Ijaz, Amna, Johnson, Sarah, Kapur, Sukriti, Ketcherside, Damien T, Levin, Ezra, Lill, Emily, Moon, Allison R, Onishi, Tatsuo, Pappaccogli, Gianluca, Perkins, Russell, Pohorsky, Roman, Raut, Jean-Christophe, Ravetta, Francois, Roberts, Tjarda, Robinson, Ellis S, Scoto, Federico, Selimovic, Vanessa, Sunday, Michael O, Temime-Roussel, Brice, Tian, Xinxiu, Wu, Judy, and Yang, Yuhan

Subjects
Earth Sciences, Atmospheric Sciences, Environmental Sciences, Health Effects of Household Energy Combustion, Social Determinants of Health, Health Effects of Indoor Air Pollution, Climate-Related Exposures and Conditions, 2.2 Factors relating to the physical environment
Abstract

The Alaskan Layered Pollution And Chemical Analysis (ALPACA) field experiment was a collaborative study designed to improve understanding of pollution sources and chemical processes during winter (cold climate and low-photochemical activity), to investigate indoor pollution, and to study dispersion of pollution as affected by frequent temperature inversions. A number of the research goals were motivated by questions raised by residents of Fairbanks, Alaska, where the study was held. This paper describes the measurement strategies and the conditions encountered during the January and February 2022 field experiment, and reports early examples of how the measurements addressed research goals, particularly those of interest to the residents. Outdoor air measurements showed high concentrations of particulate matter and pollutant gases including volatile organic carbon species. During pollution events, low winds and extremely stable atmospheric conditions trapped pollution below 73 m, an extremely shallow vertical scale. Tethered-balloon-based measurements intercepted plumes aloft, which were associated with power plant point sources through transport modeling. Because cold climate residents spend much of their time indoors, the study included an indoor air quality component, where measurements were made inside and outside a house to study infiltration and indoor sources. In the absence of indoor activities such as cooking and/or heating with a pellet stove, indoor particulate matter concentrations were lower than outdoors; however, cooking and pellet stove burns often caused higher indoor particulate matter concentrations than outdoors. The mass-normalized particulate matter oxidative potential, a health-relevant property measured here by the reactivity with dithiothreiol, of indoor particles varied by source, with cooking particles having less oxidative potential per mass than pellet stove particles.

Published
2024

32. Self-adjointness and domain of generalized spin-boson models with mild ultraviolet divergences

Author

Lill, Sascha and Lonigro, Davide

Subjects
Mathematical Physics, Mathematics - Functional Analysis, Quantum Physics, 46N50, 47A10, 47B25, 81Q10, 81T16
Abstract

We provide a rigorous construction of a large class of generalized spin-boson models with ultraviolet-divergent form factors. This class comprises various models of many possibly non-identical atoms with arbitrary but finite numbers of levels, interacting with a boson field. Ultraviolet divergences are assumed to be mild, such that no self-energy renormalization is necessary. Our construction is based on recent results by A. Posilicano, which also allow us to state an explicit formula for the domain of self-adjointness for our Hamiltonians., Comment: 20 pages

Published
2023

33. Geometric Constraints in Probabilistic Manifolds: A Bridge from Molecular Dynamics to Structured Diffusion Processes

Author

Diamond, Justin and Lill, Markus

Subjects
Computer Science - Machine Learning, Quantitative Biology - Quantitative Methods
Abstract

Understanding the macroscopic characteristics of biological complexes demands precision and specificity in statistical ensemble modeling. One of the primary challenges in this domain lies in sampling from particular subsets of the state-space, driven either by existing structural knowledge or specific areas of interest within the state-space. We propose a method that enables sampling from distributions that rigorously adhere to arbitrary sets of geometric constraints in Euclidean spaces. This is achieved by integrating a constraint projection operator within the well-regarded architecture of Denoising Diffusion Probabilistic Models, a framework founded in generative modeling and probabilistic inference. The significance of this work becomes apparent, for instance, in the context of deep learning-based drug design, where it is imperative to maintain specific molecular profile interactions to realize the desired therapeutic outcomes and guarantee safety., Comment: Published at ICML 2023 Workshop on Structured Probabilistic Inference and Generative Modeling

Published
2023

34. Towards designing improved cancer immunotherapy targets with a peptide-MHC-I presentation model, HLApollo

Author

William John Thrift, Nicolas W. Lounsbury, Quade Broadwell, Amy Heidersbach, Emily Freund, Yassan Abdolazimi, Qui T. Phung, Jieming Chen, Aude-Hélène Capietto, Ann-Jay Tong, Christopher M. Rose, Craig Blanchette, Jennie R. Lill, Benjamin Haley, Lélia Delamarre, Richard Bourgon, Kai Liu, and Suchit Jhunjhunwala

Subjects
Science
Abstract

Abstract Based on the success of cancer immunotherapy, personalized cancer vaccines have emerged as a leading oncology treatment. Antigen presentation on MHC class I (MHC-I) is crucial for the adaptive immune response to cancer cells, necessitating highly predictive computational methods to model this phenomenon. Here, we introduce HLApollo, a transformer-based model for peptide-MHC-I (pMHC-I) presentation prediction, leveraging the language of peptides, MHC, and source proteins. HLApollo provides end-to-end treatment of MHC-I sequences and deconvolution of multi-allelic data, using a negative-set switching strategy to mitigate misassigned negatives in unlabelled ligandome data. HLApollo shows a 12.65% increase in average precision (AP) on ligandome data and a 4.1% AP increase on immunogenicity test data compared to next-best models. Incorporating protein features from protein language models yields further gains and reduces the need for gene expression measurements. Guided by clinical use, we demonstrate pan-allelic generalization which effectively captures rare alleles in underrepresented ancestries.

Published
2024
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35. Two-stage binding of mitochondrial ferredoxin-2 to the core iron-sulfur cluster assembly complex

Author

Ralf Steinhilper, Linda Boß, Sven-A. Freibert, Vinzent Schulz, Nils Krapoth, Susann Kaltwasser, Roland Lill, and Bonnie J. Murphy

Subjects
Science
Abstract

Abstract Iron-sulfur (FeS) protein biogenesis in eukaryotes begins with the de novo assembly of [2Fe-2S] clusters by the mitochondrial core iron-sulfur cluster assembly (ISC) complex. This complex comprises the scaffold protein ISCU2, the cysteine desulfurase subcomplex NFS1-ISD11-ACP1, the allosteric activator frataxin (FXN) and the electron donor ferredoxin-2 (FDX2). The structural interaction of FDX2 with the complex remains unclear. Here, we present cryo-EM structures of the human FDX2-bound core ISC complex showing that FDX2 and FXN compete for overlapping binding sites. FDX2 binds in either a ‘distal’ conformation, where its helix F interacts electrostatically with an arginine patch of NFS1, or a ‘proximal’ conformation, where this interaction tightens and the FDX2-specific C terminus binds to NFS1, facilitating the movement of the [2Fe-2S] cluster of FDX2 closer to the ISCU2 FeS cluster assembly site for rapid electron transfer. Structure-based mutational studies verify the contact areas of FDX2 within the core ISC complex.

Published
2024
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36. Mass cytometry reveals cellular correlates of immune response heterogeneity to SARS-CoV-2 vaccination in the elderly

Author

Ratnadeep Mukherjee, Linn Margrethe Eggesbø, Asia-Sophia Wolf, Ingrid Fadum Kjønstad, Guri Solum, Anthony Ravussin, Sabin Bhandari, Anna Hayman Robertson, Per Magnus, Lill Trogstad, Anja Bråthen Kristoffersen, Unni Cecilie Nygaard, and Siri Mjaaland

Subjects
Immunologic diseases. Allergy, RC581-607, Neoplasms. Tumors. Oncology. Including cancer and carcinogens, RC254-282
Abstract

Abstract Heterogeneity in vaccine response, particularly in vulnerable populations like the elderly, represents a significant public health challenge. We conducted an in-depth examination of immune cell profiles before and after SARS-CoV-2 vaccination utilizing mass cytometry in a cohort of healthy Norwegian seniors (65–80 years). We have demonstrated that higher pre-vaccination frequencies of CD27+IgD- class-switched memory B cells and subsets of CD27-CD24+CD38+ transitional B cells were associated with a robust vaccine response. Post-vaccination, high responders exhibited increased frequencies of IFN-γ+CD4+ T cells with antigen recall and a concurrent decrease in CCR6(+) TH cell subset frequencies compared to low responders. The presence of a γδ T cell subset displaying polyfunctional cytokine responses was also associated with better vaccine response in the elderly. This in-depth profiling sheds light on inherent differences in immune cell frequencies and functions that may offer insights for targeted vaccination strategies in older populations.

Published
2024
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37. Towards validating invalidated knowledge: a discourse analysis of firsthand accounts of hearing voices

Author

Lill Susann Ynnesdal Haugen

Subjects
Hearing voices, Voice-hearing, Auditory hallucinations, Foucauldian discourse analysis, Firsthand experiences, Firsthand knowledge, Psychology, BF1-990
Abstract

Abstract Background As Foucault historically traced, dialogs about madness were silenced with the emergence of biomedical psychiatry. The silence entailed the epistemic violence of invalidating persons who hear voices as knowers, arguably leaving them without validating sensemaking languages for firsthand experiences. This article analyzes five Norwegian firsthand accounts of hearing voices, and how they differed from the predominating biomedical psychiatry discourse, in search of validating languages and knowledge that may facilitate making sense of voice-hearing for persons who hear voices. Methods The text material consisted of four sets of blogs authored by four young women and a short interview with a man, all of whom had firsthand experiences of voice-hearing in a Norwegian context. Ian Parker’s version of Foucauldian discourse analysis was used to analyze the material. Results Six discourses were identified: biomedical psychiatry-discourse, discourse of reason, psychodynamic discourse, discourse of personal characteristics, spiritual discourse and discourse of personal relationships between hearer and voices. Within the discourses of biomedical psychiatry and reason, voice-hearing was rendered as hallucinations, unreason, and as a problem to be solved, preferably by professionals, thus silencing the person who hears voices. In contrast, within the discourses of psychodynamics, personal characteristics, spirituality, and personal relationships between hearer and voices, voice-hearing has diverse meanings, and they grant voice hearers greater freedom concerning voice-hearing. The psychodynamic discourse nonetheless aligns with the discourses of reason and biomedical psychiatry in allowing professionals the prerogative of determining the meaning of voice-hearing. The remaining three discourses appear to facilitate more space for voice-hearers to narrate and engage with their voice-hearing at their discretion, with little to no professional impingement. Conclusions Discursive complexity notwithstanding, I consider that particularly the discourses of personal characteristics, spirituality, and personal relationships facilitate languages that may enable a person to narrate their own experiences and actions at their own discretion, without needing an expert commentator on the side. To have such languages available is argued to entail clear strides towards more empowered positions in one’s life.

Published
2024
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38. Roles of miR-20a-5p in breast cancer based on the clinical and multi-omic (CAMO) cohort and in vitro studies

Author

Eline Sol Tylden, André Berli Delgado, Marko Lukic, Line Moi, Lill-Tove Rasmussen Busund, Mona Irene Pedersen, Ana Paola Lombardi, and Karina Standahl Olsen

Subjects
Breast cancer, miR-20a-5p, microRNA, miRNA, Biomarker, Epidemiology, Medicine, Science
Abstract

Abstract MicroRNAs are involved in breast cancer development and progression, holding potential as biomarkers and therapeutic targets or tools. The roles of miR-20a-5p, a member of the oncogenic miR-17-92 cluster, remain poorly understood in the context of breast cancer. In this study, we elucidate the role of miR-20a-5p in breast cancer by examining its associations with breast cancer risk factors and clinicopathological features, and its functional roles in vitro. Tissue microarrays from 313 CAMO cohort breast cancer surgical specimens were constructed, in situ hybridization was performed and miR-20a-5p expression was semiquantitatively scored in tumor stromal fibroblasts, and in the cytoplasm and nuclei of cancer cells. In vitro analysis of the effect of miR-20a-5p transfection on proliferation, migration and invasion was performed in three breast cancer cell lines. High stromal miR-20a-5p was associated with higher Ki67 expression, and higher odds of relapse, compared to low expression. Compared to postmenopausal women, women who were premenopausal at diagnosis had higher odds of high stromal and cytoplasmic miR-20a-5p expression. Cytoplasmic miR-20a-5p was significantly associated with tumor grade. In tumors with high cytoplasmic miR-20a-5p expression compared to low expression, there was a tendency towards having a basal-like subtype and high Ki67. In contrast, high nuclear miR-20a-5p in cancer cells was associated with smaller tumor size and lower odds of lymph node metastasis, compared to low nuclear expression. Transfection with miR-20a-5p in breast cancer cell lines led to increased migration and invasion in vitro. While the majority of our results point towards an oncogenic role, some of our findings indicate that the associations of miR-20a-5p with breast cancer related risk factors and outcomes may vary based on tissue- and subcellular location. Larger studies are needed to validate our findings and further investigate the clinical utility of miR-20a-5p.

Published
2024
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39. Does Professional Support from Colleagues Influence Supervisory Competencies and Experienced Burnout among Doctoral Supervisors?

Author

Kirsi Pyhältö, Lotta Tikkanen, Milandré van Lill, and Liezel Beatrice Frick

Abstract

Doctoral supervisors conduct supervision in increasingly complex and demanding environments. Professional support from colleagues may enable supervisors to navigate such complexities and overcome demands. This paper reports on supervisory experiences of collegial professional support. The dynamics between the professional support from colleagues, perceived supervisory competencies, and experienced burnout symptoms is explored. Survey data from a total of 474 doctoral supervisors from across the African continent were collected in 2022 with a modified version of a supervisory experience survey. The data were analysed using structural equation modelling. The results showed that informational support from colleagues was positively related to supervisory competencies, while emotional support buffered the development of all burnout symptoms. Also, supervisory competencies were associated with a reduced risk of suffering from exhaustion, cynicism, and inadequacy. The study extends the existing body of knowledge on supervisory experience by explicating the dynamics between the professional support, supervisory competencies, and burnout symptoms, and by introducing a new scale for studying professional support from colleagues.

Published
2024
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40. Global Education Reform and the Swedish CPD Market: Restricted Professional Learning and the Power of Ideology

Author

Lill Langelotz and Dennis Beach

Abstract

Against the backdrop of global convergences in education reforms, a growing focus on teacher competences has emerged in European policy discourses about teacher professionalism and professional learning and development that has driven an expanding international and national CPD market involving both state and private operators. Developments in Sweden are an example, and the aim of this article is to identify and explore the discourses on teacher professional learning that seem to proliferate on this newly emerged and expanding market and their connections with and consequences for teacher professionalism. Two sets of data have fed the analysis. These are (a) CPD invoices and (b) interviews with school principals. The analysis indicates that the dominant discourses discursively shape teacher professionalism in relation to ideas about teachers as learners as collegial consumers of knowledge. Policy recommendations about peer learning become a subordinated element of a dominant discourse that prioritises and privileges the agency of knowledge producers, such as consultancies, compared to other actors as intermediaries. Commodification is a new key intermediary process in professional learning for teacher professional development.

Published
2024
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41. Characterization of the SARS-CoV-2 antibody landscape in Norway in the late summer of 2022: high seroprevalence in all age groups with patterns of primary Omicron infection in children and hybrid immunity in adults

Author

Tunheim, Gro, Fossum, Even, Robertson, Anna Hayman, Rø, Gunnar Øyvind Isaksson, Chopra, Adity, Vaage, John T., Vikse, Elisabeth Lea, Kran, Anne-Marte Bakken, Magnus, Per, Trogstad, Lill, Mjaaland, Siri, Hungnes, Olav, and Lund-Johansen, Fridtjof

Published
2024
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44. Introduction to the 9th International Biometals Webinars, Clostridioides difficile activates an anticipatory iron storage strategy in response to nutritional immunity, Iron-sulfur protein biogenesis in eukaryotes: How all starts

Author

Michaud-Soret, Isabelle, speaker, Skaar, Eric, speaker, Lill, Roland, speaker, and Rensing, Christopher, speaker

Abstract

Introduce speakers All cells require nutrient metal to carry out essential biochemical processes. This requirement is something that the vertebrate immune system has exploited as a strategy to defend against infection by restricting microbial access to nutrient metal. This process of nutrient restriction during infection is called “nutritional immunity”. Bacterial pathogens have evolved elaborate mechanisms to circumvent nutritional immunity and acquire metal during infection. This struggle for nutrient metal impacts both microbial virulence as well as the immune response of the host, profoundly affecting the outcome of host-pathogen interactions. Nutrient metals are acquired through the diet, establishing the gastrointestinal tract as a primary battleground in the struggle for metal between microbes and the vertebrate host. Clostridioides difficile is the leading cause of hospital acquired gastrointestinal infections, and this organism must compete with both the host and other microbes to obtain the critical nutrients that it needs to colonize and cause disease. To study these interactions in more detail, we have combined microbial genetics with murine models of infection and advanced imaging modalities. We have applied this platform to the study of C. difficile infection, leading to the discovery of infection-associated alterations in the distribution and abundance of nutrients in tissue. These data have led to the discovery of C. difficile factors that are required to acquire and metabolize nutrients and have led to unexpected findings regarding how nutrient metals and antibiotic resistance intersect in this organism. Iron-sulfur (Fe-S) proteins play crucial roles in numerous important cellular processes including respiration, metabolism, genome maintenance, protein translation and antiviral response. The synthesis of Fe S clusters and their insertion into apoproteins in (non-green) eukaryotes is a multifaceted process involving over 30 proteins located in mitochondria and cytosol. The biogenesis of mitochondrial [2Fe-2S] and [4Fe-4S] proteins is orchestrated by components of the iron-sulfur cluster assembly (ISC) machinery which was inherited from bacteria during evolution (Braymer et al., 2017; Lill & Freibert, 2020; Lill, 2020). Cytosolic and nuclear Fe-S protein assembly also relies on the function of this machinery, yet additionally requires the mitochondrial ABC exporter ABCB7 and the cytosolic iron-sulfur protein assembly (CIA) machinery (Paul et al., 2015). Interestingly, mitochondrial Fe-S protein biogenesis co-evolved with the existence of the entire organelle, defining this process as both the minimal and essential function of mitochondria (Braymer et al., 2021). A combination of in vivo and in vitro studies have generated a good understanding of the general outline of the sequential steps of Fe-S protein biogenesis. Currently, the detailed molecular mechanisms underlying the individual reaction steps are investigated by using cell biological, biochemical, spectroscopic, and structural approaches. In this seminar, I will present some of our recent structural, spectroscopic, and biochemical insights into the molecular mechanisms underlying [2Fe-2S] cluster assembly in mitochondria. The mechanism may closely resemble that in the bacterial ISC system. These mechanistic insights may eventually help advancing our molecular understanding of the biochemical consequences of numerous “Fe-S diseases” linked to mutations in almost any of the ISC genes (Lill & Freibert, 2020). Specifically, our studies suggest a mechanism for frataxin function, a protein functionally impaired in Friedreich’s ataxia.

Published
2024
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45. Dynamics of magnetization at infinite temperature in a Heisenberg spin chain

Author

Rosenberg, Eliott, Andersen, Trond, Samajdar, Rhine, Petukhov, Andre, Hoke, Jesse, Abanin, Dmitry, Bengtsson, Andreas, Drozdov, Ilya, Erickson, Catherine, Klimov, Paul, Mi, Xiao, Morvan, Alexis, Neeley, Matthew, Neill, Charles, Acharya, Rajeev, Allen, Richard, Anderson, Kyle, Ansmann, Markus, Arute, Frank, Arya, Kunal, Asfaw, Abraham, Atalaya, Juan, Bardin, Joseph, Bilmes, A., Bortoli, Gina, Bourassa, Alexandre, Bovaird, Jenna, Brill, Leon, Broughton, Michael, Buckley, Bob B., Buell, David, Burger, Tim, Burkett, Brian, Bushnell, Nicholas, Campero, Juan, Chang, Hung-Shen, Chen, Zijun, Chiaro, Benjamin, Chik, Desmond, Cogan, Josh, Collins, Roberto, Conner, Paul, Courtney, William, Crook, Alexander, Curtin, Ben, Debroy, Dripto, Barba, Alexander Del Toro, Demura, Sean, Di Paolo, Agustin, Dunsworth, Andrew, Earle, Clint, Farhi, E., Fatemi, Reza, Ferreira, Vinicius, Flores, Leslie, Forati, Ebrahim, Fowler, Austin, Foxen, Brooks, Garcia, Gonzalo, Genois, Élie, Giang, William, Gidney, Craig, Gilboa, Dar, Giustina, Marissa, Gosula, Raja, Dau, Alejandro Grajales, Gross, Jonathan, Habegger, Steve, Hamilton, Michael, Hansen, Monica, Harrigan, Matthew, Harrington, Sean, Heu, Paula, Hill, Gordon, Hoffmann, Markus, Hong, Sabrina, Huang, Trent, Huff, Ashley, Huggins, William, Ioffe, Lev, Isakov, Sergei, Iveland, Justin, Jeffrey, Evan, Jiang, Zhang, Jones, Cody, Juhas, Pavol, Kafri, D., Khattar, Tanuj, Khezri, Mostafa, Kieferová, Mária, Kim, Seon, Kitaev, Alexei, Klots, Andrey, Korotkov, Alexander, Kostritsa, Fedor, Kreikebaum, John Mark, Landhuis, David, Laptev, Pavel, Lau, Kim Ming, Laws, Lily, Lee, Joonho, Lee, Kenneth, Lensky, Yuri, Lester, Brian, Lill, Alexander, Liu, Wayne, Livingston, William P., Locharla, A., Mandrà, Salvatore, Martin, Orion, Martin, Steven, McClean, Jarrod, McEwen, Matthew, Meeks, Seneca, Miao, Kevin, Mieszala, Amanda, Montazeri, Shirin, Movassagh, Ramis, Mruczkiewicz, Wojciech, Nersisyan, Ani, Newman, Michael, Ng, Jiun How, Nguyen, Anthony, Nguyen, Murray, Niu, M., O'Brien, Thomas, Omonije, Seun, Opremcak, Alex, Potter, Rebecca, Pryadko, Leonid, Quintana, Chris, Rhodes, David, Rocque, Charles, Rubin, N., Saei, Negar, Sank, Daniel, Sankaragomathi, Kannan, Satzinger, Kevin, Schurkus, Henry, Schuster, Christopher, Shearn, Michael, Shorter, Aaron, Shutty, Noah, Shvarts, Vladimir, Sivak, Volodymyr, Skruzny, Jindra, Smith, Clarke, Somma, Rolando, Sterling, George, Strain, Doug, Szalay, Marco, Thor, Douglas, Torres, Alfredo, Vidal, Guifre, Villalonga, Benjamin, Heidweiller, Catherine Vollgraff, White, Theodore, Woo, Bryan, Xing, Cheng, Yao, Jamie, Yeh, Ping, Yoo, Juhwan, Young, Grayson, Zalcman, Adam, Zhang, Yaxing, Zhu, Ningfeng, Zobrist, Nicholas, Neven, Hartmut, Babbush, Ryan, Bacon, Dave, Boixo, Sergio, Hilton, Jeremy, Lucero, Erik, Megrant, Anthony, Kelly, Julian, Chen, Yu, Smelyanskiy, Vadim, Khemani, Vedika, Gopalakrishnan, Sarang, Prosen, Tomaž, and Roushan, Pedram

Subjects
Quantum Physics
Abstract

Understanding universal aspects of quantum dynamics is an unresolved problem in statistical mechanics. In particular, the spin dynamics of the 1D Heisenberg model were conjectured to belong to the Kardar-Parisi-Zhang (KPZ) universality class based on the scaling of the infinite-temperature spin-spin correlation function. In a chain of 46 superconducting qubits, we study the probability distribution, $P(\mathcal{M})$, of the magnetization transferred across the chain's center. The first two moments of $P(\mathcal{M})$ show superdiffusive behavior, a hallmark of KPZ universality. However, the third and fourth moments rule out the KPZ conjecture and allow for evaluating other theories. Our results highlight the importance of studying higher moments in determining dynamic universality classes and provide key insights into universal behavior in quantum systems.

Published
2023
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46. Publicly available datasets of breast histopathology H&E whole-slide images: A scoping review

Author

Tafavvoghi, Masoud, Bongo, Lars Ailo, Shvetsov, Nikita, Busund, Lill-Tove Rasmussen, and Møllersen, Kajsa

Subjects
Electrical Engineering and Systems Science - Image and Video Processing, Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning, 68T01 General topics in artificial intelligence, I.2.0
Abstract

Advancements in digital pathology and computing resources have made a significant impact in the field of computational pathology for breast cancer diagnosis and treatment. However, access to high-quality labeled histopathological images of breast cancer is a big challenge that limits the development of accurate and robust deep learning models. In this scoping review, we identified the publicly available datasets of breast H&E stained whole-slide images (WSI) that can be used to develop deep learning algorithms. We systematically searched nine scientific literature databases and nine research data repositories and found 17 publicly available datasets containing 10385 H&E WSIs of breast cancer. Moreover, we reported image metadata and characteristics for each dataset to assist researchers in selecting proper datasets for specific tasks in breast cancer computational pathology. In addition, we compiled two lists of breast H&E patches and private datasets as supplementary resources for researchers. Notably, only 28% of the included articles utilized multiple datasets, and only 14% used an external validation set, suggesting that the performance of other developed models may be susceptible to overestimation. The TCGA-BRCA was used in 52% of the selected studies. This dataset has a considerable selection bias that can impact the robustness and generalizability of the trained algorithms. There is also a lack of consistent metadata reporting of breast WSI datasets that can be an issue in developing accurate deep learning models, indicating the necessity of establishing explicit guidelines for documenting breast WSI dataset characteristics and metadata., Comment: 27 pages (including references), 8 figures, 3 tables, 5 supporting information materials

Published
2023

47. Improving Protein-peptide Interface Predictions in the Low Data Regime

Author

Diamond, Justin and Lill, Markus

Subjects
Quantitative Biology - Biomolecules, Computer Science - Machine Learning
Abstract

We propose a novel approach for predicting protein-peptide interactions using a bi-modal transformer architecture that learns an inter-facial joint distribution of residual contacts. The current data sets for crystallized protein-peptide complexes are limited, making it difficult to accurately predict interactions between proteins and peptides. To address this issue, we propose augmenting the existing data from PepBDB with pseudo protein-peptide complexes derived from the PDB. The augmented data set acts as a method to transfer physics-based contextdependent intra-residue (within a domain) interactions to the inter-residual (between) domains. We show that the distributions of inter-facial residue-residue interactions share overlap with inter residue-residue interactions, enough to increase predictive power of our bi-modal transformer architecture. In addition, this dataaugmentation allows us to leverage the vast amount of protein-only data available in the PDB to train neural networks, in contrast to template-based modeling that acts as a prior, Comment: 5 pages, 5 figures, ICLR Machine Learning in Drug Discovery Accepted paper

Published
2023

48. Stable Quantum-Correlated Many Body States through Engineered Dissipation

Author

Mi, X., Michailidis, A. A., Shabani, S., Miao, K. C., Klimov, P. V., Lloyd, J., Rosenberg, E., Acharya, R., Aleiner, I., Andersen, T. I., Ansmann, M., Arute, F., Arya, K., Asfaw, A., Atalaya, J., Bardin, J. C., Bengtsson, A., Bortoli, G., Bourassa, A., Bovaird, J., Brill, L., Broughton, M., Buckley, B. B., Buell, D. A., Burger, T., Burkett, B., Bushnell, N., Chen, Z., Chiaro, B., Chik, D., Chou, C., Cogan, J., Collins, R., Conner, P., Courtney, W., Crook, A. L., Curtin, B., Dau, A. G., Debroy, D. M., Barba, A. Del Toro, Demura, S., Di Paolo, A., Drozdov, I. K., Dunsworth, A., Erickson, C., Faoro, L., Farhi, E., Fatemi, R., Ferreira, V. S., Forati, L. F. Burgos E., Fowler, A. G., Foxen, B., Genois, E., Giang, W., Gidney, C., Gilboa, D., Giustina, M., Gosula, R., Gross, J. A., Habegger, S., Hamilton, M. C., Hansen, M., Harrigan, M. P., Harrington, S. D., Heu, P., Hoffmann, M. R., Hong, S., Huang, T., Huff, A., Huggins, W. J., Ioffe, L. B., Isakov, S. V., Iveland, J., Jeffrey, E., Jiang, Z., Jones, C., Juhas, P., Kafri, D., Kechedzhi, K., Khattar, T., Khezri, M., Kieferova, M., Kim, S., Kitaev, A., Klots, A. R., Korotkov, A. N., Kostritsa, F., Kreikebaum, J. M., Landhuis, D., Laptev, P., Lau, K. -M., Laws, L., Lee, J., Lee, K. W., Lensky, Y. D., Lester, B. J., Lill, A. T., Liu, W., Locharla, A., Malone, F. D., Martin, O., McClean, J. R., McEwen, M., Mieszala, A., Montazeri, S., Morvan, A., Movassagh, R., Mruczkiewicz, W., Neeley, M., Neill, C., Nersisyan, A., Newman, M., Ng, J. H., Nguyen, A., Nguyen, M., Niu, M. Y., OBrien, T. E., Opremcak, A., Petukhov, A., Potter, R., Pryadko, L. P., Quintana, C., Rocque, C., Rubin, N. C., Saei, N., Sank, D., Sankaragomathi, K., Satzinger, K. J., Schurkus, H. F., Schuster, C., Shearn, M. J., Shorter, A., Shutty, N., Shvarts, V., Skruzny, J., Smith, W. C., Somma, R., Sterling, G., Strain, D., Szalay, M., Torres, A., Vidal, G., Villalonga, B., Heidweiller, C. V., White, T., Woo, B. W. K., Xing, C., Yao, Z. J., Yeh, P., Yoo, J., Young, G., Zalcman, A., Zhang, Y., Zhu, N., Zobrist, N., Neven, H., Babbush, R., Bacon, D., Boixo, S., Hilton, J., Lucero, E., Megrant, A., Kelly, J., Chen, Y., Roushan, P., Smelyanskiy, V., and Abanin, D. A.

Subjects
Quantum Physics
Abstract

Engineered dissipative reservoirs have the potential to steer many-body quantum systems toward correlated steady states useful for quantum simulation of high-temperature superconductivity or quantum magnetism. Using up to 49 superconducting qubits, we prepared low-energy states of the transverse-field Ising model through coupling to dissipative auxiliary qubits. In one dimension, we observed long-range quantum correlations and a ground-state fidelity of 0.86 for 18 qubits at the critical point. In two dimensions, we found mutual information that extends beyond nearest neighbors. Lastly, by coupling the system to auxiliaries emulating reservoirs with different chemical potentials, we explored transport in the quantum Heisenberg model. Our results establish engineered dissipation as a scalable alternative to unitary evolution for preparing entangled many-body states on noisy quantum processors.

Published
2023
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49. Local magnetic and geometric structure in Mn-doped La(Fe,Si)13

Author

Eggert, Benedikt, Lill, Johanna, Günzing, Damian, Pillich, Cynthia, Terwey, Alexandra, Radulov, Ilyia A., Wilhelm, Fabrice, Rogalev, Andrei, Rovezzi, Mauro, Skokov, Konstantin, Ollefs, Katharina, Gutfleisch, Oliver, Gruner, Markus E., and Wende, Heiko

Subjects
Condensed Matter - Materials Science
Abstract

Magnetic cooling has the potential to replace conventional gas compression refrigeration. Materials such as La(Fe,Si)$_{13}$ exhibit a sizeable first-order magnetocaloric effect, and it is possible to tailor the phase transition towards room temperature by Mn-H-doping, resulting in a large temperature range for operation. Within this work, we discuss variations of the electronic and lattice structure in La(Fe,Si)$_{13}$ with increasing Mn content utilizing X-ray magnetic circular dichroism (XMCD) and extended X-ray absorption fine structure spectroscopy (EXAFS). While XMCD shows a decrease of the magnetic polarization at the Fe K edge, low-temperature EXAFS measurements indicate increased positional disorder in the La environment that is otherwise absent for Fe and Mn. First-principles calculations link the positional disorder to an enlarged Mn-Si distance -- explaining the increased positional disorder in the La surrounding., Comment: 23 pages

Published
2023

50. Associations between degree of food processing and all-cause and cause-specific mortality: a multicentre prospective cohort analysis in 9 European countriesResearch in context

Author

Esther M. González-Gil, Michèle Matta, Fernanda Morales Berstein, Manon Cairat, Geneviève Nicolas, Jessica Blanco, Nathalie Kliemann, Renata Bertazzi Levy, Fernanda Rauber, Inarie Jacobs, Aline Al Nahas, Emine Koc Cakmak, Eszter P. Vamos, Kiara Chang, Sahar G. Yammine, Christopher Millett, Mathilde Touvier, Maria Gabriela Matias Pinho, Konstantinos K. Tsilidis, Alicia K. Heath, Christina M. Lill, Valeria Pala, Conchi Moreno-Iribas, Maria Santucci De Magistris, Christina C. Dahm, Niels Bock, Anja Olsen, Anne Tjønneland, Yvonne T. van der Schouw, Pilar Amiano, Franziska Jannasch, Matthias B. Schulze, Francesca Romana Mancini, Chloé Marques, Claire Cadeau, Catalina Bonet, Daniel Redondo-Sánchez, Kristin Benjaminsen Borch, Magritt Brustad, Guri Skeie, Jesús Humberto-Gómez, Alessandra Macciotta, Pietro Ferrari, Laure Dossus, Marc J. Gunter, and Inge Huybrechts

Subjects
Nova classification, Unprocessed/minimally processed foods, Ultra-processed foods, Mortality, EPIC study, Public aspects of medicine, RA1-1270
Abstract

Summary: Background: Ultra-processed food (UPF) consumption has been linked with higher risk of mortality. This multi-centre study investigated associations between food intake by degree of processing, using the Nova classification, and all-cause and cause-specific mortality. Methods: This study analyzed data from the European Prospective Investigation into Cancer and Nutrition. All-cause mortality and cause-specific mortality due to cancer, circulatory diseases, digestive diseases, Parkinson’s disease, and Alzheimer’s disease served as endpoints. Hazard ratios (HRs) and 95% CIs were estimated using multivariable Cox proportional hazards regression models. Substitution analyses were also performed. Findings: Overall, 428,728 (71.7% female) participants were included in the analysis and 40,016 deaths were documented after 15.9 years of follow-up. UPFs (in percentage grams per day [g/d]) were positively associated with all-cause mortality (HRs per 1-SD: 1.04; 95% CI: 1.02,1.05), as well as mortality from circulatory diseases (1.09; 95% CI: 1.07,1.12), cerebrovascular disease (1.11; 95% CI: 1.05,1.17), ischemic heart disease (1.10; 95% CI: 1.06,1.15), digestive diseases (1.12; 95% CI: 1.05,1.20), and Parkinson’s disease (1.23; 95% CI: 1.06,1.42). No associations were found between UPFs and mortality from cancer or Alzheimer’s disease. Replacing processed and UPFs with unprocessed/minimally processed foods was associated with lower mortality risk. Interpretation: In this pan-European analysis, higher UPF consumption was associated with greater mortality from circulatory diseases, digestive diseases, and Parkinson’s disease. The results support growing evidence that higher consumption of UPFs and lower consumption of unprocessed foods may have a negative impact on health. Funding: l’Institut National du Cancer, and World Cancer Research Fund International.

Published
2025
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