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Column chromatography is an important process in downstream biopharmaceutical manufacturing that enables high-selectivity separation of proteins through various modalities, such as affinity, ion exchange, hydrophobic interactions, or a combination of the aforementioned modes. Current mechanistic models of column chromatography typically abstract particle-level phenomena, in particular adsorption kinetics. A mechanistic model capable of incorporating particle-level phenomena would increase the value derived from mechanistic models. To this end, we model column chromatography via sphere packing, formulating three versions, each with increasing complexity. The first, homogeneous circle packing, is recast as maximum independent set and solved by the Quantum Approximate Optimization Algorithm on a quantum computer. The second, heterogeneous circle packing, is formulated as a graphical optimization problem and solved via classical simulations, accompanied by a road map to a quantum solution. An extension to the third, heterogeneous sphere packing, is formulated mathematically in a manner suitable to a quantum solution. Finally, detailed resource scaling is conducted to estimate the quantum resources required to simulate the most realistic model, providing a pathway to quantum advantage.

QubitSolve is working on a quantum solution for computational fluid dynamics (CFD). We have created a variational quantum CFD (VQCFD) algorithm and a 2D Software Prototype based on it. By testing the Software Prototype on a quantum simulator, we demonstrate that the partial differential equations that underlie CFD can be solved using quantum computers. We aim to determine whether a quantum advantage can be achieved with VQCFD. To do this, we compare the performance of VQCFD with classical CFD using performance models. The quantum performance model uses data from VQCFD circuits run on quantum computers. We define a key performance parameter Q_{5E7}, the ratio of quantum to classical simulation time for a size relevant to industrial simulations. Given the current state of the Software Prototype and the limited computing resources available, we can only estimate an upper bound for Q_{5E7}. While the estimated Q_{5E7} shows that the algorithm's implementation must improve significantly, we have identified several innovative techniques that could reduce it sufficiently to achieve a quantum advantage. In the next phase of development, we will develop a 3D minimum-viable product and implement those techniques., Comment: 14 pages, 5 figures,2 tables, submitted to 2024 Aviation Forum, Las Vegas, NV, July 29, 2024

Combinatorial optimization problems that arise in science and industry typically have constraints. Yet the presence of constraints makes them challenging to tackle using both classical and quantum optimization algorithms. We propose a new quantum algorithm for constrained optimization, which we call quantum constrained Hamiltonian optimization (Q-CHOP). Our algorithm leverages the observation that for many problems, while the best solution is difficult to find, the worst feasible (constraint-satisfying) solution is known. The basic idea is to to enforce a Hamiltonian constraint at all times, thereby restricting evolution to the subspace of feasible states, and slowly "rotate" an objective Hamiltonian to trace an adiabatic path from the worst feasible state to the best feasible state. We additionally propose a version of Q-CHOP that can start in any feasible state. Finally, we benchmark Q-CHOP against the commonly-used adiabatic algorithm with constraints enforced using a penalty term and find that Q-CHOP performs consistently better on a wide range of problems, including textbook problems on graphs, knapsack, combinatorial auction, as well as a real-world financial use case, namely bond exchange-traded fund basket optimization.

We describe Superstaq, a quantum software platform that optimizes the execution of quantum programs by tailoring to underlying hardware primitives. For benchmarks such as the Bernstein-Vazirani algorithm and the Qubit Coupled Cluster chemistry method, we find that deep optimization can improve program execution performance by at least 10x compared to prevailing state-of-the-art compilers. To highlight the versatility of our approach, we present results from several hardware platforms: superconducting qubits (AQT @ LBNL, IBM Quantum, Rigetti), trapped ions (QSCOUT), and neutral atoms (Infleqtion). Across all platforms, we demonstrate new levels of performance and new capabilities that are enabled by deeper integration between quantum programs and the device physics of hardware., Comment: Appearing in IEEE QCE 2023 (Quantum Week) conference

We develop a hardware-efficient ansatz for variational optimization, derived from existing ansatze in the literature, that parametrizes subsets of all interactions in the Cost Hamiltonian in each layer. We treat gate orderings as a variational parameter and observe that doing so can provide significant performance boosts in experiments. We carried out experimental runs of a compilation-optimized implementation of fully-connected Sherrington-Kirkpatrick Hamiltonians on a 50-qubit linear-chain subsystem of Rigetti Aspen-M-3 transmon processor. Our results indicate that, for the best circuit designs tested, the average performance at optimized angles and gate orderings increases with circuit depth (using more parameters), despite the presence of a high level of noise. We report performance significantly better than using a random guess oracle for circuits involving up to approx 5000 two-qubit and approx 5000 one-qubit native gates. We additionally discuss various takeaways of our results toward more effective utilization of current and future quantum processors for optimization., Comment: v2: extended experimental results, updated references, fixed typos; v3: improved main narration, added new experimental data and analysis, updated references, fixed typos; v4: slightly improved narration, updated references 15+8 pages; 3+5 figures

This paper proposes a perception and path planning pipeline for autonomous racing in an unknown bounded course. The pipeline was initially created for the 2021 evGrandPrix autonomous division and was further improved for the 2022 event, both of which resulting in first place finishes. Using a simple LiDAR-based perception pipeline feeding into an occupancy grid based expansion algorithm, we determine a goal point to drive. This pipeline successfully achieved reliable and consistent laps in addition with occupancy grid algorithm to know the ways around a cone-defined track with an averaging speeds of 6.85 m/s over a distance 434.2 meters for a total lap time of 63.4 seconds., Comment: 5 pages

We present a variational Monte Carlo method that solves the nuclear many-body problem in the occupation number formalism exploiting an artificial neural network representation of the ground-state wave function. A memory-efficient version of the stochastic reconfiguration algorithm is developed to train the network by minimizing the expectation value of the Hamiltonian. We benchmark this approach against widely used nuclear many-body methods by solving a model used to describe pairing in nuclei for different types of interaction and different values of the interaction strength. Despite its polynomial computational cost, our method outperforms coupled-cluster and provides energies that are in excellent agreement with the numerically-exact full configuration interaction values., Comment: 9 pages, 3 figures

In astrophysical scenarios with large neutrino density, like supernovae and the early universe, the presence of neutrino-neutrino interactions can give rise to collective flavor oscillations in the out-of-equilibrium collective dynamics of a neutrino cloud. The role of quantum correlations in these phenomena is not yet well understood, in large part due to complications in solving for the real-time evolution of the strongly coupled many-body system. Future fault-tolerant quantum computers hold the promise to overcome much of these limitations and provide direct access to the correlated neutrino dynamic. In this work, we present the first simulation of a small system of interacting neutrinos using current generation quantum devices. We introduce a strategy to overcome limitations in the natural connectivity of the qubits and use it to track the evolution of entanglement in real-time. The results show the critical importance of error-mitigation techniques to extract meaningful results for entanglement measures using noisy, near term, quantum devices., Comment: 12 pages, 12 figures

A computational model of aquaporin regulation in cancer cells has been constructed as a Qualitative Network in the software BioModelAnalyzer (BMA). The model connects some important aquaporins expressed in human cancer to common phenotypes via a number of fundamental, dysregulated signalling pathways. Based on over 60 publications, this model can not only reproduce the results reported in a discrete, qualitative manner, but also reconcile the seemingly incompatible phenotype with research consensus by suggesting molecular mechanisms accountable for it. Novel predictions have also been made by mimicking real-life experiments in the model., Comment: Data is used without full credit and explicit agreement from another author and fellow experimentalists

Reproducibility of computationally-derived scientific discoveries should be a certainty. As the product of several person-years' worth of effort, results -- whether disseminated through academic journals, conferences or exploited through commercial ventures -- should at some level be expected to be repeatable by other researchers. While this stance may appear to be obvious and trivial, a variety of factors often stand in the way of making it commonplace. Whilst there has been detailed cross-disciplinary discussions of the various social, cultural and ideological drivers and (potential) solutions, one factor which has had less focus is the concept of reproducibility as a technical challenge. Specifically, that the definition of an unambiguous and measurable standard of reproducibility would offer a significant benefit to the wider computational science community. In this paper, we propose a high-level technical specification for a service for reproducibility, presenting cyberinfrastructure and associated workflow for a service which would enable such a specification to be verified and validated. In addition to addressing a pressing need for the scientific community, we further speculate on the potential contribution to the wider software development community of services which automate de novo compilation and testing of code from source. We illustrate our proposed specification and workflow by using the BioModelAnalyzer tool as a running example., Comment: Submitted to the 18th IEEE International Conference on Computational Science and Engineering (CSE 2015); 6 pages, LaTeX. arXiv admin note: substantial text overlap with arXiv:1502.02448

The reproduction and replication of research results has become a major issue for a number of scientific disciplines. In computer science and related computational disciplines such as systems biology, the challenges closely revolve around the ability to implement (and exploit) novel algorithms and models. Taking a new approach from the literature and applying it to a new codebase frequently requires local knowledge missing from the published manuscripts and transient project websites. Alongside this issue, benchmarking, and the lack of open, transparent and fair benchmark sets present another barrier to the verification and validation of claimed results. In this paper, we outline several recommendations to address these issues, driven by specific examples from a range of scientific domains. Based on these recommendations, we propose a high-level prototype open automated platform for scientific software development which effectively abstracts specific dependencies from the individual researcher and their workstation, allowing easy sharing and reproduction of results. This new e-infrastructure for reproducible computational science offers the potential to incentivise a culture change and drive the adoption of new techniques to improve the quality and efficiency -- and thus reproducibility -- of scientific exploration., Comment: Invited submission to Journal of Open Research Software; 12 pages, LaTeX

How many times have you tried to re-implement a past CAV tool paper, and failed? Reliably reproducing published scientific discoveries has been acknowledged as a barrier to scientific progress for some time but there remains only a small subset of software available to support the specific needs of the research community (i.e. beyond generic tools such as source code repositories). In this paper we propose an infrastructure for enabling reproducibility in our community, by automating the build, unit testing and benchmarking of research software., Comment: Submitted to the 27th International Conference on Computer Aided Verification (CAV 2015); 9 pages, LaTeX

The reproduction and replication of reported scientific results is a hot topic within the academic community. The retraction of numerous studies from a wide range of disciplines, from climate science to bioscience, has drawn the focus of many commentators, but there exists a wider socio-cultural problem that pervades the scientific community. Sharing code, data and models often requires extra effort; this is currently seen as a significant overhead that may not be worth the time investment. Automated systems, which allow easy reproduction of results, offer the potential to incentivise a culture change and drive the adoption of new techniques to improve the efficiency of scientific exploration. In this paper, we discuss the value of improved access and sharing of the two key types of results arising from work done in the computational sciences: models and algorithms. We propose the development of an integrated cloud-based system underpinning computational science, linking together software and data repositories, toolchains, workflows and outputs, providing a seamless automated infrastructure for the verification and validation of scientific models and in particular, performance benchmarks., Comment: Accepted for the 1st International Workshop on Recomputability (part of UCC 2014); 5 pages, LaTeX

The reproduction and replication of novel results has become a major issue for a number of scientific disciplines. In computer science and related computational disciplines such as systems biology, the issues closely revolve around the ability to implement novel algorithms and approaches. Taking an approach from the literature and applying it to a new codebase frequently requires local knowledge missing from the published manuscripts and project websites. Alongside this issue, benchmarking, and the development of fair --- and widely available --- benchmark sets present another barrier. In this paper, we outline several suggestions to address these issues, driven by specific examples from a range of scientific domains. Finally, based on these suggestions, we propose a new open platform for scientific software development which effectively isolates specific dependencies from the individual researcher and their workstation and allows faster, more powerful sharing of the results of scientific software engineering., Comment: Accepted for the 2nd Workshop on Sustainable Software for Science: Practice and Experiences (WSSSPE2); 5 pages, LaTeX