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Animals, especially insects, navigate their environments using complex strategies that integrate responses to chemical signals such as pheromones. The relative importance an insect places on foraging, finding a mate or other tasks can significantly influence its movement patterns. This study uncovers the distinct exploration strategies that emerge when these priorities vary. A new model is introduced that explicitly treats a pair of insects and their response to each other's pheromones. The strategy results in dynamics that exhibit characteristic anomalous scaling in displacement and non-Gaussian distributions of position; specifically, compressed exponential distributions for the number of encounters and the total duration of encounters. Our model not only elucidates these emergent behaviours but also provides insight into optimizing encounter frequencies. These strategies can be inverted to design pheromone-based traps for luring target species to specific areas for removal. The model is also applicable more broadly to pairs of agents that both respond to the paths traced by themselves and each other., Comment: 22 pages including 7 figures and 2 tables; and 7 additional pages of supplementary information

In response to numerous programs seeking to advance quantum education and workforce development in the United States, experts from academia, industry, government, and professional societies convened for a National Science Foundation-sponsored workshop in February 2024 to explore the benefits and challenges of establishing a national center for quantum education. Broadly, such a center would foster collaboration and build the infrastructure required to develop a diverse and quantum-ready workforce. The workshop discussions centered around how a center could uniquely address gaps in public, K-12, and undergraduate quantum information science and engineering (QISE) education. Specifically, the community identified activities that, through a center, could lead to an increase in student awareness of quantum careers, boost the number of educators trained in quantum-related subjects, strengthen pathways into quantum careers, enhance the understanding of the U.S. quantum workforce, and elevate public engagement with QISE. Core proposed activities for the center include professional development for educators, coordinated curriculum development and curation, expanded access to educational laboratory equipment, robust evaluation and assessment practices, network building, and enhanced public engagement with quantum science.

The dual immaculate and Young quasisymmetric Schur bases of quasisymmetric functions possess analogues in the peak algebra: respectively, the quasisymmetric Schur $Q$-functions and the peak Young quasisymmetric Schur functions. We show elements of the former basis expand into the latter basis with nonnegative coefficients., Comment: 19 pages

Recent developments in high-power ultrafast optical technology and emerging theoretical frameworks in strong-field quantum electrodynamics (SF-QED) are unveiling nuanced differentiations between the semi-classical and full quantum mechanical descriptions of physical systems. Here we present a computational investigation of a novel technique for attosecond optical sensing through time correlation transduction (TCT) by investigating high-harmonic generation (HHG) as a representative SF-QED process. TCT is an experimental method to capture photon-electron interactions at higher harmonic orders by temporarily correlating the emitted and driving photon fields. This approach enables resolving the dynamical behavior of optically-driven strong-field phenomena in quantum materials such as Two-dimensional Materials and Dirac Semimetals down to 10 attosecond temporal resolution to discover a full quantum explanation. Predicting and measuring the transition between perturbative and non-perturbative regimes with attosecond resolution can deepen the understanding of SF-QED such as HHG. As such, we find that TCT is a powerful method to pave the way toward the functional characterization of quantum matter., Comment: 7 pages, 5 figures

We extend the notion of ascent-compatibility from symmetric groups to all Coxeter groups, thereby providing a type-independent framework for constructing families of modules of $0$-Hecke algebras. We apply this framework in type $B$ to give representation-theoretic interpretations of a number of noteworthy families of type-$B$ quasisymmetric functions. Next, we construct modules of the type-$B$ $0$-Hecke algebra corresponding to type-$B$ analogues of Schur functions and introduce a type-$B$ analogue of Schur $Q$-functions; we prove that these shifted domino functions expand positively in the type-$B$ peak functions. We define a type-$B$ analogue of the $0$-Hecke--Clifford algebra, and we use this to provide representation-theoretic interpretations for both the type-$B$ peak functions and the shifted domino functions. We consider the modules of this algebra induced from type-$B$ $0$-Hecke modules constructed via ascent-compatibility and prove a general formula, in terms of type-$B$ peak functions, for the type-$B$ quasisymmetric characteristics of the restrictions of these modules., Comment: 25 pages, 3 figures

Constraining molecules in simulations (such as with constant bond lengths and/or angles) reduces their degrees of freedom (DoF), which in turn affects temperature calculations in those simulations. When local temperatures are measured, e.g. from a set of atoms in a subvolume or from velocities in one Cartesian direction, the result can appear to unphysically violate equipartition of the kinetic energy if the local DoF are not correctly calculated. Here we determine how to correctly calculate local temperatures from arbitrary Cartesian component kinetic energies, accounting for general geometric constraints, by self-consistently evaluating the DoF of atoms subjected to those constraints. The method is validated on a variety of test systems, including systems subject to a temperature gradient and those confined between walls. It is also shown to provide a sensitive test for the breakdown of kinetic energy equipartition caused by the approximate nature of numerical integration or insufficient equilibration times. As a practical demonstration, we show that kinetic energy equipartition between C and H atoms connected by rigid bonds can be violated even at the commonly-used time step of 2 fs, and that this equipartition violation appears to usefully indicate configurational overheating., Comment: 14 pages of main text and 4 pages of Supporting Information. This version includes more details and examples

The Bayesian posterior distribution can only be evaluated up-to a constant of proportionality, which makes simulation and consistent estimation challenging. Classical consistent Bayesian methods such as sequential Monte Carlo (SMC) and Markov chain Monte Carlo (MCMC) have unbounded time complexity requirements. We develop a fully parallel sequential Monte Carlo (pSMC) method which provably delivers parallel strong scaling, i.e. the time complexity (and per-node memory) remains bounded if the number of asynchronous processes is allowed to grow. More precisely, the pSMC has a theoretical convergence rate of Mean Square Error (MSE)$ = O(1/NP)$, where $N$ denotes the number of communicating samples in each processor and $P$ denotes the number of processors. In particular, for suitably-large problem-dependent $N$, as $P \rightarrow \infty$ the method converges to infinitesimal accuracy MSE$=O(\varepsilon^2)$ with a fixed finite time-complexity Cost$=O(1)$ and with no efficiency leakage, i.e. computational complexity Cost$=O(\varepsilon^{-2})$. A number of Bayesian inference problems are taken into consideration to compare the pSMC and MCMC methods., Comment: 23 pages, 17 figures

In this perspective we consider how modern statistical mechanics and response theory can be applied to understand the response of polar molecules to an applied electric field and the fluctuations in these systems. Results that are consistent with thermodynamics and physical expectations are derived, as well as a new fluctuation relation that is tested in molecular simulations. It is demonstrated that a deterministic approach leads to distribution functions that continually evolve, even when system properties have relaxed, which has implications for treatment of nonequilibrium steady states., Comment: 19 pages and 2 ancillary files (animations)

Since about 2003, captchas have been widely used as a barrier against bots, while simultaneously annoying great multitudes of users worldwide. As their use grew, techniques to defeat or bypass captchas kept improving, while captchas themselves evolved in terms of sophistication and diversity, becoming increasingly difficult to solve for both bots and humans. Given this long-standing and still-ongoing arms race, it is important to investigate usability, solving performance, and user perceptions of modern captchas. In this work, we do so via a large-scale (over 3, 600 distinct users) 13-month real-world user study and post-study survey. The study, conducted at a large public university, was based on a live account creation and password recovery service with currently prevalent captcha type: reCAPTCHAv2. Results show that, with more attempts, users improve in solving checkbox challenges. For website developers and user study designers, results indicate that the website context directly influences (with statistically significant differences) solving time between password recovery and account creation. We consider the impact of participants' major and education level, showing that certain majors exhibit better performance, while, in general, education level has a direct impact on solving time. Unsurprisingly, we discover that participants find image challenges to be annoying, while checkbox challenges are perceived as easy. We also show that, rated via System Usability Scale (SUS), image tasks are viewed as "OK", while checkbox tasks are viewed as "good". We explore the cost and security of reCAPTCHAv2 and conclude that it has an immense cost and no security. Overall, we believe that this study's results prompt a natural conclusion: reCAPTCHAv2 and similar reCAPTCHA technology should be deprecated.

We extend the recently-introduced weak Bruhat interval modules of the type A $0$-Hecke algebra to all finite Coxeter types. We determine, in a type-independent manner, structural properties for certain general families of these modules, with a primary focus on projective covers and injective hulls. We apply this approach to recover a number of results on type A $0$-Hecke modules in a uniform way, and obtain some additional results on recently-introduced families of type A $0$-Hecke modules., Comment: 19 pages, 6 figures

In the popular imagination, science and technology are often seen as fields of knowledge production critical to social progress and a cooperative future. This optimistic portrayal of technological advancement also features prominently in internal discourses amongst scientists, industry leaders, and STEM students alike. Yet, an overwhelming body of research, investigation, and first-person accounts highlight the varying ways modern science, technology, and engineering industries contribute to the degradation of our changing environments and exploit and harm global low-income and marginalized populations. By and large, siloed higher-education STEM curricula provide inadequate opportunities for undergraduate and graduate students to critically analyze the historical and epistemological foundations of scientific knowledge production and even fewer tools to engage with and respond to modern community-based cases. Here, we describe the development of a humanities- and social sciences-informed curriculum designed to address the theory, content, and skill-based needs of traditional STEM students considering technoscientific careers. In essence, this course is designed to foster behavior change, de-center dominant ways of knowing in the sciences, and bolster self-reflection and critical-thinking skills to equip the developing STEM workforce with a more nuanced and accurate understanding of the social, political, and economic role of science and technology. This curriculum has the potential to empower STEM-educated professionals to contribute to a more promising, inclusive future. Our framework foregrounds key insights from science and technology studies, Black and Native feminisms, queer theory, and disability studies, alongside real-world case studies using critical pedagogies., Comment: 25 pages, 1 figure, 4 tables

Advanced supercapacitors have great potential to transform how we store and utilize energy, leading to more efficient and sustainable energy systems. This study reveals the structural features influencing the interfacial thermal transport and electrical performances of supercapacitors, using the constant potential and constant charge molecular dynamics simulation techniques. Thermal and electrical properties were calculated for graphene/carbon nanotube composite electrodes and ionic liquid electrolytes with different nanotube diameter, number, layers, and alignments of the nanotubes. The effect of application of a constant potential on the Kapitza resistance is determined for the first time. The vertically aligned CNT structures exhibited higher electrical performance, while the horizontal arrangement showed better thermal performance. Optimum electrode configurations were identified by considering thermal and electrical performance, along with other design factors, such as structural stability, ease of manufacturing, and scalability. After considering all these factors, the horizontally stacked multi-layer CNT arrangement emerged as the optimal electrode structure. The insights gained from this study aid in comprehending the effects of variations in electrode structure, thereby enabling efficient supercapacitor electrode design.

Embedded, smart, and IoT devices are increasingly popular in numerous everyday settings. Since lower-end devices have the most strict cost constraints, they tend to have few, if any, security features. This makes them attractive targets for exploits and malware. Prior research proposed various security architectures for enforcing security properties for resource-constrained devices, e.g., via Remote Attestation (RA). Such techniques can (statically) verify software integrity of a remote device and detect compromise. However, run-time (dynamic) security, e.g., via Control-Flow Integrity (CFI), is hard to achieve. This work constructs an architecture that ensures integrity of software execution against run-time attacks, such as Return-Oriented Programming (ROP). It is built atop a recently proposed CASU -- a low-cost active Root-of-Trust (RoT) that guarantees software immutability. We extend CASU to support a shadow stack and a CFI monitor to mitigate run-time attacks. This gives some confidence that CFI can indeed be attained even on low-end devices, with minimal hardware overhead., Comment: The idea mentioned in the paper is still under development. This is an early version without full results. This version is only as a poster accepted at ACM CCS 2023

For nearly two decades, CAPTCHAs have been widely used as a means of protection against bots. Throughout the years, as their use grew, techniques to defeat or bypass CAPTCHAs have continued to improve. Meanwhile, CAPTCHAs have also evolved in terms of sophistication and diversity, becoming increasingly difficult to solve for both bots (machines) and humans. Given this long-standing and still-ongoing arms race, it is critical to investigate how long it takes legitimate users to solve modern CAPTCHAs, and how they are perceived by those users. In this work, we explore CAPTCHAs in the wild by evaluating users' solving performance and perceptions of unmodified currently-deployed CAPTCHAs. We obtain this data through manual inspection of popular websites and user studies in which 1,400 participants collectively solved 14,000 CAPTCHAs. Results show significant differences between the most popular types of CAPTCHAs: surprisingly, solving time and user perception are not always correlated. We performed a comparative study to investigate the effect of experimental context -- specifically the difference between solving CAPTCHAs directly versus solving them as part of a more natural task, such as account creation. Whilst there were several potential confounding factors, our results show that experimental context could have an impact on this task, and must be taken into account in future CAPTCHA studies. Finally, we investigate CAPTCHA-induced user task abandonment by analyzing participants who start and do not complete the task., Comment: Accepted at USENIX Security 2023

Fluctuation theorems (FTs) quantify the thermodynamic reversibility of a system, and for deterministic systems they are defined in terms of the dissipation function. However, in a nonequilibrium steady state of deterministic dynamics, the phase space distribution is unknown, making the dissipation function difficult to evaluate without extra information. As such, steady state FTs for deterministic systems to date have required either that the trajectory segment of interest is relatively long, or that information is available about the entire trajectory surrounding that segment. In this work, it is shown that a simple machine learning model trained to predict whether a given steady state trajectory segment is being played forward or backward in time calculates a function that satisfies an FT and relies solely on information within the segment of interest. The FT is satisfied even for very short trajectory segments where the approximate relation derived from theory breaks down, for systems far from equilibrium, and for various nonequilibrium dynamics. It is further demonstrated that any function that is a well-calibrated predictor of time's arrow must satisfy a fluctuation theorem, and that a local FT can be derived which depends only on local dissipation and its correlations with the surrounding non-local dissipation.

Photonic systems utilized as components for optical computing promise the potential of enhanced computational ability over current computer architectures. Here, an all-dielectric photonic metastructure is investigated for application as a quantum algorithm emulator (QAE) in the terahertz frequency regime; specifically, we show implementation of the Deustsh-Josza algorithm. The design for the QAE consists of a gradient-index (GRIN) lens as the Fourier transform subblock and silicon as the oracle subblock. First, we detail optimization of the metastructure through numerical analysis. Then, we employed inverse design through a machine learning approach to further optimize the structural geometry. In particular, we improved the lens thickness, in order to enhance the resulting output signal for both balanced and constant functions. We show that by optimizing the thickness of the gradient-index lens through ML, we enhance the interaction of the incident light with the metamaterial leading to a stronger focus of the outgoing wave resulting in more accurate implementation of the desired quantum algorithm in the terahertz.

The ability to prepare systems in specific target states through quantum engineering is essential for realizing the new technologies promised by a second quantum revolution. Here, we recast the fundamental problem of state preparation in high-dimensional Hilbert spaces as ManQala, a quantum game inspired by the West African sowing game mancala. Motivated by optimal gameplay in solitaire mancala, where nested nearest-neighbor permutations and actions evolve the state of the game board to its target configuration, ManQala acts as a pre-processing approach for deterministically arranging particles in a quantum control problem. Once pre-processing with ManQala is complete, existing quantum control methods are applied, but now with a reduced search space. We find that ManQala-type strategies match, or outperform, competing approaches in terms of final state variance even in small-scale quantum state engineering problems where we expect the slightest advantage since the relative reduction in search space is the least. These results suggest that ManQala provides a rich platform for designing control protocols relevant to near-term intermediate-scale quantum technologies.

Constant potential method molecular dynamics simulation (CPM MD) enables the accurate modelling of atomistic electrode charges when studying the electrode-electrolyte interface at the nanoscale. Here we extend the theoretical framework of CPM MD to the case in which the total charge of each conductive electrode is controlled, instead of their potential differences. We show that the resulting thermodynamic ensemble is distinct from that sampled in with a fixed potential difference, but rigorously related as conjugate ensembles. This theoretical correspondence, which we demonstrate using simulations of an ionic liquid supercapacitor, underpins the success of recent studies with fixed total charges on the electrodes. We show that equilibration is usefully sped up in this ensemble and outline some potential applications of these simulations in future.

This Letter reports one of the most precise measurements to date of the antineutrino spectrum from a purely U235-fueled reactor, made with the final dataset from the PROSPECT-I detector at the High Flux Isotope Reactor. By extracting information from previously unused detector segments, this analysis effectively doubles the statistics of the previous PROSPECT measurement. The reconstructed energy spectrum is unfolded into antineutrino energy and compared with both the Huber-Mueller model and a spectrum from a commercial reactor burning multiple fuel isotopes. A local excess over the model is observed in the 5MeV to 7MeV energy region. Comparison of the PROSPECT results with those from commercial reactors provides new constraints on the origin of this excess, disfavoring at 2.2 and 3.2 standard deviations the hypotheses that antineutrinos from U235 are solely responsible and non-contributors to the excess observed at commercial reactors respectively., Comment: The paper has been updated to match publication at PRL

Machine learning (ML) has found broad applicability in quantum information science in topics as diverse as experimental design, state classification, and even studies on quantum foundations. Here, we experimentally realize an approach for defining custom prior distributions that are automatically tuned using ML for use with Bayesian quantum state estimation methods. Previously, researchers have looked to Bayesian quantum state tomography due to its unique advantages like natural uncertainty quantification, the return of reliable estimates under any measurement condition, and minimal mean-squared error. However, practical challenges related to long computation times and conceptual issues concerning how to incorporate prior knowledge most suitably can overshadow these benefits. Using both simulated and experimental measurement results, we demonstrate that ML-defined prior distributions reduce net convergence times and provide a natural way to incorporate both implicit and explicit information directly into the prior distribution. These results constitute a promising path toward practical implementations of Bayesian quantum state tomography., Comment: 9 pages, 4 figures

This paper presents an energy calibration scheme for an upgraded reactor antineutrino detector for the Precision Reactor Oscillation and Spectrum Experiment (PROSPECT). The PROSPECT collaboration is preparing an upgraded detector, PROSPECT-II (P-II), to advance capabilities for the investigation of fundamental neutrino physics, fission processes and associated reactor neutrino flux, and nuclear security applications. P-II will expand the statistical power of the original PROSPECT (P-I) dataset by at least an order of magnitude. The new design builds upon previous P-I design and focuses on improving the detector robustness and long-term stability to enable multi-year operation at one or more sites. The new design optimizes the fiducial volume by elimination of dead space previously occupied by internal calibration channels, which in turn necessitates the external deployment. In this paper, we describe a calibration strategy for P-II. The expected performance of externally deployed calibration sources is evaluated using P-I data and a well-benchmarked simulation package by varying detector segmentation configurations in the analysis. The proposed external calibration scheme delivers a compatible energy scale model and achieves comparable performance with the inclusion of an additional AmBe neutron source, in comparison to the previous internal arrangement. Most importantly, the estimated uncertainty contribution from the external energy scale calibration model meets the precision requirements of the P-II experiment., Comment: 19 pages, 10 figures

Quantum information science and technology (QIST) is a critical and emerging technology with the potential for enormous world impact and is currently invested in by over 40 nations. To bring these large-scale investments to fruition and bridge the lower technology readiness levels (TRLs) of fundamental research at universities to the high TRLs necessary to realize the promise of practical quantum advantage accessible to industry and the public, we present a roadmap for Quantum Technology Demonstration Projects (QTDPs). Such QTDPs, focused on intermediate TRLs, are large-scale public-private partnerships with a high probability of translation from laboratory to practice. They create technology demonstrating a clear 'quantum advantage' for science breakthroughs that are user-motivated and will provide access to a broad and diverse community of scientific users. Successful implementation of a program of QTDPs will have large positive economic impacts., Comment: Updated title and abstract

Atmospheric effects, such as turbulence and background thermal noise, inhibit the propagation of coherent light used in ON-OFF keying free-space optical communication. Here we present and experimentally validate a convolutional neural network to reduce the bit error rate of free-space optical communication in post-processing that is significantly simpler and cheaper than existing solutions based on advanced optics. Our approach consists of two neural networks, the first determining the presence of coherent bit sequences in thermal noise and turbulence and the second demodulating the coherent bit sequences. All data used for training and testing our network is obtained experimentally by generating ON-OFF keying bit streams of coherent light, combining these with thermal light, and passing the resultant light through a turbulent water tank which we have verified mimics turbulence in the air to a high degree of accuracy. Our convolutional neural network improves detection accuracy over threshold classification schemes and has the capability to be integrated with current demodulation and error correction schemes.

Widely applicable, modified Green-Kubo expressions for the local diffusion coefficient ($D_l$) are obtained using linear response theory. In contrast to past definitions in use, these expressions are statistical mechanical results. Molecular simulations of systems with anisotropic diffusion and an inhomogeneous density profile confirm the validity of the results. Diffusion coefficients determined from different expressions in terms of currents and velocity correlations agree in the limit of large systems. Furthermore, they apply to arbitrarily small local regions, making them readily applicable to nanoscale and inhomogeneous systems where knowledge of $D_l$ is important.

We introduce an approach for performing quantum state reconstruction on systems of $n$ qubits using a machine-learning-based reconstruction system trained exclusively on $m$ qubits, where $m\geq n$. This approach removes the necessity of exactly matching the dimensionality of a system under consideration with the dimension of a model used for training. We demonstrate our technique by performing quantum state reconstruction on randomly sampled systems of one, two, and three qubits using machine-learning-based methods trained exclusively on systems containing at least one additional qubit. The reconstruction time required for machine-learning-based methods scales significantly more favorably than the training time; hence this technique can offer an overall savings of resources by leveraging a single neural network for dimension-variable state reconstruction, obviating the need to train dedicated machine-learning systems for each Hilbert space., Comment: 8 pages, 4 figures

The quantum Zeno effect reveals that the continuous observation of a quantum system can result in significant alterations to its evolution. Here, we present a method for establishing polarization entanglement between two initially unentangled photons in coupled waveguides via the quantum Zeno effect. We support our analytical investigation with numerical simulations of the underlying Schrodinger equation describing the system. Further, we extend our technique to three coupled waveguides in a planar configuration and determine the parameter regime required to generate three-qubit W-states. Our findings offer a powerful quantum state engineering approach for photonic quantum information technologies.

We introduce a general method for constructing modules for $0$-Hecke algebras and supermodules for $0$-Hecke-Clifford algebras from diagrams of boxes in the plane, and give formulas for the images of these modules in the algebras of quasisymmetric functions and peak functions under the relevant characteristic map. As initial applications, we resolve a question of Jing and Li (2015), introduce a new basis of the peak algebra analogous to the quasisymmetric Schur functions, uncover a new connection between Schur $Q$-functions and quasisymmetric Schur functions, give a representation-theoretic interpretation of families of tableaux used in constructing certain functions in the peak algebra, and establish a common framework for known $0$-Hecke module interpretations of bases of quasisymmetric functions., Comment: 36 pages

The PROSPECT experiment has substantially addressed the original 'Reactor Antineutrino Anomaly' by performing a high-resolution spectrum measurement from an enriched compact reactor core and a reactor model-independent sterile neutrino oscillation search based on the unique spectral distortions the existence of eV$^2$-scale sterile neutrinos would impart. But as the field has evolved, the current short-baseline (SBL) landscape supports many complex phenomenological interpretations, establishing a need for complementary experimental approaches to resolve the situation. While the global suite of SBL reactor experiments, including PROSPECT, have probed much of the sterile neutrino parameter space, there remains a large region above 1 eV$^2$ that remains unaddressed. Recent results from BEST confirm the Gallium Anomaly, increasing its significance to $\sim 5\sigma$, with sterile neutrinos providing a possible explanation of this anomaly. Separately, the MicroBooNE exclusion of electron-like signatures causing the MiniBooNE low-energy excess does not eliminate the possibility of sterile neutrinos as an explanation. Focusing specifically on the future use of reactors as a neutrino source for beyond-the-standard-model physics and applications, higher-precision spectral measurements still have a role to play. These recent results have created a confusing landscape which requires new data to disentangle the seemingly contradictory measurements. To directly probe $\overline{\nu}_{e}$ disappearance from high $\Delta m^2$ sterile neutrinos, the PROSPECT collaboration proposes to build an upgraded and improved detector, PROSPECT-II. It features an evolutionary detector design which can be constructed and deployed within one year and have impactful physics with as little as one calendar year of data., Comment: contribution to Snowmass 2021

Molecular dynamics (MD) simulations of complex electrochemical systems, such as ionic liquid supercapacitors, are increasingly including the constant potential method (CPM) to model conductive electrodes at specified potential difference, but the inclusion of CPM can be computationally expensive. We demonstrate the computational savings available in CPM MD simulations of ionic liquid supercapacitors when the usual non-periodic slab geometry is replaced with fully periodic boundary conditions. We show how a doubled cell approach, previously used in non-CPM MD simulations of charged interfaces, can be used to enable fully periodic CPM MD simulations. Using either a doubled cell approach, or a finite field approach previously reported by others, fully periodic CPM MD simulations produce comparable results to the traditional slab geometry simulations with a nearly double speed-up in computational time. Indeed, these savings can offset the additional cost of the CPM algorithm, resulting in periodic CPM MD simulations that are faster than the non-periodic, fixed-charge equivalent simulations for the ionic liquid supercapacitors studied here.

We propose a series of data-centric heuristics for improving the performance of machine learning systems when applied to problems in quantum information science. In particular, we consider how systematic engineering of training sets can significantly enhance the accuracy of pre-trained neural networks used for quantum state reconstruction without altering the underlying architecture. We find that it is not always optimal to engineer training sets to exactly match the expected distribution of a target scenario, and instead, performance can be further improved by biasing the training set to be slightly more mixed than the target. This is due to the heterogeneity in the number of free variables required to describe states of different purity, and as a result, overall accuracy of the network improves when training sets of a fixed size focus on states with the least constrained free variables. For further clarity, we also include a "toy model" demonstration of how spurious correlations can inadvertently enter synthetic data sets used for training, how the performance of systems trained with these correlations can degrade dramatically, and how the inclusion of even relatively few counterexamples can effectively remedy such problems.

Chiral nanostructures offer the ability to respond to the vector nature of a light beam at the nanoscale. While naturally chiral materials offer a path towards scalability, engineered structures offer a path to wavelength tunability through geometric manipulation. Neither approach, however, allows for temporal control of chirality. Therefore, in the best of all worlds, it is crucial to realize chiral materials that possess the quality of scalability, tailored wavelength response, and dynamic control at high speeds. Here, a new class of intrinsically chiral phase change nanomaterials (PCNMs) is proposed and explored, based on a scalable bottom-up fabrication technique with a high degree of control in three dimensions. Angular resolved Mueller Matrix and spectroscopic ellipsometry are performed to characterize the optical birefringence and dichroism, and a numerical model is provided to explain the origin of optical activity. This work achieves the critical goal of demonstrating high-speed dynamic switching of chirality over 50,000 cycles via the underlying PCNM., Comment: 21 pages, 10 page supplement, 16 figures