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Present an outline of important aspects for benchmarking quantum algorithms, in particular quantum heuristics. Using quantum approximate optimization algorithm as an example, we demonstrate how the choice of cost Hamiltonian, of mixer, and of initial states can affect the performance. We also discuss how realistic aspects of near-term quantum hardware will influence the algorithm performance.
Combinatorial optimization is anticipated to be one of the primary use cases for quantum computation in the coming years. The Quantum Approximate Optimization Algorithm (QAOA) and Quantum Annealing (QA) have the potential to demonstrate significant run-time performance benefits over current state-of-the-art solutions. Using existing methods for characterizing classical optimization algorithms, we analyze solution quality obtained by solving Max-Cut problems using a quantum annealing device and gate-model quantum simulators and devices. This is used to guide the development of an advanced benchmarking framework for quantum computers designed to evaluate the trade-off between run-time execution performance and the solution quality for iterative hybrid quantum-classical applications. The framework generates performance profiles through effective visualizations that show performance progression as a function of time for various problem sizes and illustrates algorithm limitations uncovered by the benchmarking approach. The framework is an enhancement to the existing open-source QED-C Application-Oriented Benchmark suite and can connect to the open-source analysis libraries. The suite can be executed on various quantum simulators and quantum hardware systems.
With the onset of quantum confinement in the inversion layer in nanoscale MOSFETs, behavior of the resonant level inevitably determines all device characteristics. While most classical device simulators take quantization into account in some simplified manner, the important details of electrostatics are missing. Our work addresses this shortcoming and provides: (a) a framework to quantitatively explore device physics issues such as the source-drain and gate leakage currents, DIBL, and threshold voltage shift due to quantization, and b) a means of benchmarking quantum corrections to semiclassical models (such as density-gradient and quantum-corrected MEDICI). We have developed physical approximations and computer code capable of realistically simulating 2-D nanoscale transistors, using the non-equilibrium Green's function (NEGF) method. This is the most accurate full quantum model yet applied to 2-D device simulation. Open boundary conditions and oxide tunneling are treated on an equal footing. Electrons in the ellipsoids of the conduction band are treated within the anisotropic effective mass approximation. We present the results of our simulations of MIT 25, 50 and 90 nm "well-tempered" MOSFETs and compare them to those of classical and quantum corrected models. The important feature of quantum model is smaller slope of Id-Vg curve and consequently higher threshold voltage. Surprisingly, the self-consistent potential profile shows lower injection barrier in the channel in quantum case. These results are qualitatively consistent with ID Schroedinger-Poisson calculations. The effect of gate length on gate-oxide leakage and subthreshold current has been studied. The shorter gate length device has an order of magnitude smaller current at zero gate bias than the longer gate length device without a significant trade-off in on-current. This should be a device design consideration.
With the onset of quantum confinement in the inversion layer in nanoscale MOSFETs, behavior of the resonant level inevitably determines all device characteristics. While most classical device simulators take quantization into account in some simplified manner, the important details of electrostatics are missing. Our work addresses this shortcoming and provides: (a) a framework to quantitatively explore device physics issues such as the source-drain and gate leakage currents, DIBL, and threshold voltage shift due to quantization, and b) a means of benchmarking quantum corrections to semiclassical models (such as density- gradient and quantum-corrected MEDICI). We have developed physical approximations and computer code capable of realistically simulating 2-D nanoscale transistors, using the non-equilibrium Green's function (NEGF) method. This is the most accurate full quantum model yet applied to 2-D device simulation. Open boundary conditions, oxide tunneling and phase-breaking scattering are treated on equal footing. Electrons in the ellipsoids of the conduction band are treated within the anisotropic effective mass approximation. Quantum simulations are focused on MIT 25, 50 and 90 nm "well- tempered" MOSFETs and compared to classical and quantum corrected models. The important feature of quantum model is smaller slope of Id-Vg curve and consequently higher threshold voltage. These results are quantitatively consistent with I D Schroedinger-Poisson calculations. The effect of gate length on gate-oxide leakage and sub-threshold current has been studied. The shorter gate length device has an order of magnitude smaller current at zero gate bias than the longer gate length device without a significant trade-off in on-current. This should be a device design consideration.
Two quantum mechanical effects that impact the operation of nanoscale transistors are inversion layer energy quantization and ballistic transport. While the qualitative effects of these features are reasonably understood, a comprehensive study of device physics in two dimensions is lacking. Our work addresses this shortcoming and provides: (a) a framework to quantitatively explore device physics issues such as the source-drain and gate leakage currents, DIBL (Drain Induced Barrier Lowering), and threshold voltage shift due to quantization, and b) a means of benchmarking quantum corrections to semiclassical models (such as density-gradient and quantum-corrected MEDICI).
HybridQ and PySA are open-source tools developed by NASA to support benchmarking, analysis and quantum algorithm development in areas such as simulation, optimization and machine learning. These tools leverage classical hardware acceleration via high-performance computing CPU and GPU architectures and support high-performance computing. HybridQ is a highly extensible platform designed to provide a common framework to integrate multiple state-of-the-art techniques to simulate large scale quantum circuits. PySA is an extensible platform to optimize a classical cost function. We provide an outline of each of these open-source tools and highlight projects using each of these tools in contexts of simulation, optimization and machine learning.
The algorithms employed are computationally intensive and, as a result, increased performance (both algorithmic and architectural) is required to improve accuracy and to treat larger molecular systems. Several benchmark quantum chemistry codes are examined on a variety of architectures. While these codes are only a small portion of a typical quantum chemistry library, they illustrate many of the computationally intensive kernels and data manipulation requirements of some applications. Furthermore, understanding the performance of the existing algorithm on present and proposed supercomputers serves as a guide for future programs and algorithm development. The algorithms investigated are: (1) a sparse symmetric matrix vector product; (2) a four index integral transformation; and (3) the calculation of diatomic two electron Slater integrals. The vectorization strategies are examined for these algorithms for both the Cyber 205 and Cray XMP. In addition, multiprocessor implementations of the algorithms are looked at on the Cray XMP and on the MIT static data flow machine proposed by DENNIS.
A symmetrized version of the recently developed refined Robert-Bonamy formalism [Q. Ma, C. Boulet, and R. H. Tipping, J. Chem. Phys. 139, 034305 (2013)] is proposed. This model takes into account line coupling effects and hence allows the calculation of the off-diagonal elements of the relaxation matrix, without neglecting the rotational structure of the perturbing molecule. The formalism is applied to the isotropic Raman spectra of autoperturbed N2 for which a benchmark quantum relaxation matrix has recently been proposed. The consequences of the classical path approximation are carefully analyzed. Methods correcting for effects of inelasticity are considered. While in the right direction, these corrections appear to be too crude to provide off diagonal elements which would yield, via the sum rule, diagonal elements in good agreement with the quantum results. In order to overcome this difficulty, a re-normalization procedure is applied, which ensures that the off-diagonal elements do lead to the exact quantum diagonal elements. The agreement between the (re-normalized) semi-classical and quantum relaxation matrices is excellent, at least for the Raman spectra of N2, opening the way to the analysis of more complex molecular systems.
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We implemented the ADAPT-VQE algorithm into our recent classical sparse wavefunction circuit simulator to demonstrate that classical resources can (1) efficiently find a physically motivated compact wavefunction ansatz for further refinement on near-term quantum hardware and (2) benchmark expected results of VQE-based algorithms once the quantum hardware is available to study large-scale applications. In particular, we study the role of the ADAPT-VQE operator pool, molecular basis set selection, and variations such as TETRIS-ADAPT-VQE on the performance of our classical circuit simulator. This work demonstrates the promise of using classical resources to generate highly accurate wavefunctions that can be prepared on quantum hardware to initiate other quantum algorithms such as phase estimation. Our approach harnesses the power of high-performance computing resources with the more limited available quantum computers to map a path toward quantum advantage for electronic structure calculations in chemistry and materials science.
Planetary missions continue to grow larger and more complex. Furthermore, the current focus on human exploration of the Moon and Mars, as well as Mars Sample Return(MSR), place increas-ingly stringent requirements on the reliability of the entry, descent, and landing (EDL) system that ensures the safe delivery of payload or crew to their destination. Planetary EDL is an area in which mission designers are critically reliant on modeling and simulation to demonstrate the reliability of the system, as there are no ground facilities that are able to fully test these systems in a flight-relevant environment. NASA’s state-of-the-art modeling and simulation capability must continually evolve to meet the needs of the next generation of planetary EDL. To accomplish this aim, NASA’s Entry Systems Modeling (ESM) Project was formed in 2013and is funded bythe Space Technology Mission Directorate(STMD) and Science Mission Directorate (SMD). ESM is the Agency’s only cross-cutting effort for advancing entry systems modeling and simulation capabilities across a range of technical disciplines and Solar System destinations. ESM is a portfolio project covering a variety of mid-TRL research efforts within four core EDL-related areas of investment: (1) Thermal protection material modeling, (2) Shock layer kinetics and radiation, (3) Aerosciences, and (4) Guidance, navigation, and con-trol. The material modeling group creates detailed material response modelsof thermal protection systems (TPS)from the micro to macro scale, and at the fun-damental and engineering levels. Shock layer kinetics and radiation focuses on radiative heating of space-craft, quantum chemistry and benchmark experiments for validation.Aerosciences is a broad research area that impacts many aspects of entry systems, including parachutes, aerodynamics, and turbulent heating augmentation due to TPS roughness.The guidance, navigation,and control effort under ESM is expanding the capabilities of NASA’s main flight mechanics tool, POST2, for use on high-performance computing architectures and to generalize interoperability with external applications for more detailed end-to-end simula-tion.In addition, several focusedresearch topics have been approvedto augment ESM’s core portfolio. These include efforts for deep post-flight analysis of Mars 2020/MEDLI2 flight data; development ofTPS failure models; improvement of hypersonic wakeflow models; and a recently concluded effort to provide material response models for NuSil-coated PICA heat-shield material. This presentation will discuss each of these investment areas and demonstrate via real mission examples how advances to the state-of-the-art enabled by ESM are directly impacting the missions of today and tomorrow, including InSight, Mars 2020, Mars Sample Return, Orion, and Dragonfly.
Over the last decade, the incidence of wildfires has surged, causing widespread destruction globally. To better comprehend and manage these incidents, remote sensing and aerial missions have been implemented in recent efforts. However, this has resulted in an exponential rise in the amount of remote sensing data utilization, leading to a need for intelligent automation of data extraction in wildfire studies. Machine learning provides an accurate automated approach for detecting these natural anomalies and facilitates decision-makers to take prompt actions. To make insightful decisions in wildfire management, it is imperative to move beyond simple detection and explore the potential of probabilistic generative machine learning for creating "what-if" scenarios for various wildfire conditions. Such models offer improved representation of the stochastic nature of wildfire events. However, the optimization of these models can be computationally expensive, especially when using classical computers. Quantum computers have recently emerged as a promising solution to reduce the computational cost of training such models and improve their performance. In this study, we aim to utilize quantum-compatible machine learning techniques to implement our probabilistic generative approach. To that end, we propose a supervised probabilistic variational model consisting of a U-NET-based image-to-image component along with encoder and decoder networks which work as a variational autoencoder (VAE) component. Additionally, we explore the type of latent distribution type in the VAE component and implement different means for modeling the prior distribution. We further investigate the quantum-compatible versions of the model compared to the classical counterpart and benchmark potential benefits of quantum compatibility over the classical model.
In this paper, we propose a patch planting method for creating arbitrarily large spin glass instances with known ground states. The scaling of the computational complexity of these instances with various block numbers and sizes is investigated and compared with random instances using population annealing Monte Carlo and the quantum annealing DW2X machine. The method can be useful for benchmarking tests for future generation quantum annealing machines, classical and quantum mechanical optimization algorithms.
Quantum computing is one of the most enticing computational paradigms with the potential to revolutionize diverse areas of future-generation computational systems. While quantum computing hardware has advanced rapidly, from tiny laboratory experiments to quantum chips that can outperform even the largest supercomputers on specialized computational tasks, these noisy- intermediate scale quantum (NISQ) processors are still too small and non-robust to be directly useful for any real-world applications. In this paper, we describe NASA’s work in assessing and advancing the potential of quantum computing. We discuss advances in algorithms, both near- and longer-term, and the results of our explorations on current hardware as well as with simulations, including illustrating the benefits of algorithm-hardware codesign in the NISQ era. This work also includes physics-inspired classical algorithms that can be used at application scale today. We discuss innovative tools supporting the assessment and advancement of quantum computing, and describe improved methods for simulating quantum systems of various types on high performance computing systems that incorporate realistic error models. We provide an overview of recent methods for benchmarking, evaluating, and characterizing quantum hardware for error mitigation, computational purposes.
Wildfire occurrences have been increasing for the past decade, leaving devastating traces across the world. In the recent efforts, remote sensing and airborne missions have been utilized to better understand and manage wildfires. This has resulted in an exponential increase in volume of remote sensing data, which has pushed the need for intelligent automation of data extraction for wildfire studies. Machine learning offers accurate automation in detecting such natural anomalies and enable decision-makers to take actions in a timely manner. Recent advances in machine learning algorithms, namely probabilistic generative methods, allow researchers and decisionmakers to step beyond detection and study “what-if” scenarios for wildfire occurrences. Additionally, they offer better imitations to the stochastic behavior of nature, and wildfire events. However, optimizing the performance of these probabilistic generative models is a computationally expensive process, specially using digital computers. On the other hand, quantum computers have recently shown a promise to reduce computationally costly training of such models and provide performance improvements. There is a body of research investigating the potential for improved machine learning methods in which key operations are performed on a quantum computer. In this study, we propose a probabilistic image-toimage segmentation approach combining a very well-known segmentation method, U-NET, with a Conditional Variational Auto-Encoder (CVAE) to not only detect wildfires but also describe the stochasticity of the phenomenon and be capable of running “what-if” scenarios. Our proposed model is compatible with training on quantum computers, which results in a quantum-assisted image-to-image segmentation approach and can be used to benchmark the potential benefit of quantum computing over the classical one.
The variational quantum eigenvalue solver is a powerful hybrid quantum-classical approach that has been suggested as a candidate method to run on near-term quantum hardware for computing ground state electronic energies of molecular systems. However, even for small molecules, the number of variational parameters and qubits required to minimize the electronic energy is beyond the reach of current quantum computers except for small basis sets. We explore a new paradigm for state preparation where we test how much of the optimization can be approximately prepared with classical computers to reduce the number of optimization steps performed using a quantum device. By adapting a recent algorithm for the factorized form of the UCC ansatz, we can study molecular electronic structure problems with up to 64 qubits. In addition, we also test a related approach of using tensor networks to optimize quantum circuits in order to benchmark various lattice models. We present results using these approaches and discuss strategies for incorporating these ideas into variational algorithms involving near-term quantum computers. Our results help demonstrate the strength of the UCC ansatz and address pressing questions about optimal initial parameterizations and circuit construction.