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arXiv:1905.02860 · Advances in parallel computing

From Ansätze to Z-Gates: A NASA View of Quantum Computing

Eleanor Gilbert Rieffel, Stuart Hadfield, Tad Hogg, Salvatore Mandrà, Jeffrey Marshall, Gianni Mossi, Bryan O’Gorman, Eugeniu Plamadeala, Norm M. Tubman, Davide Venturelli, Walter Vinci, Wang Zhihui, Max Wilson, Filip Wudarski, Rupak Biswas

Published Jan 1, 2019• 12 citations

Abstract

For the last few years, the NASA Quantum Artificial Intelligence Laboratory (QuAIL) has been performing research to assess the potential impact of quantum computers on challenging computational problems relevant to future NASA missions. A key aspect of this research is devising methods to most effectively utilize emerging quantum computing hardware. Research questions include what experiments on early quantum hardware would give the most insight into the potential impact of quantum computing, the design of algorithms to explore on such hardware, and the development of tools to minimize the quantum resource requirements. We survey work relevant to these questions, with a particular emphasis on our recent work in quantum algorithms and applications, in elucidating mechanisms of quantum mechanics and their uses for quantum computational purposes, and in simulation, compilation, and physics-inspired classical algorithms. To our early application thrusts in planning and scheduling, fault diagnosis, and machine learning, we add thrusts related to robustness of communication networks and the simulation of many-body systems for material science and chemistry. We provide a brief update on quantum annealing work, but concentrate on gate-model quantum computing research advances within the last couple of years.

DOI: 10.3233/apc190010

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NASA is constantly confronting massively challenging computational problems. These problems have the potential to limit mission scope and aim. NASA's Quantum Artificial Intelligence Laboratory (QuAIL) Group was formed to determine the potential for quantum computation to enable more ambitious NASA missions in the future. This presentation highlights recent work by the QuAIL team that is focused on physics insights and applications associated with the D-Wave 2000Q quantum annealing hardware that is located at NASA Ames Research Center.

Quantum computing promises an unprecedented ability to solve intractable problems by harnessing quantum mechanical effects such as tunneling, superposition, and entanglement. The Quantum Artificial Intelligence Laboratory (QuAIL) at NASA Ames Research Center is the space agency's primary facility for conducting research and development in quantum information sciences. QuAIL conducts fundamental research in quantum physics but also explores how best to exploit and apply this disruptive technology to enable NASA missions in aeronautics, Earth and space sciences, and space exploration. At the same time, machine learning has become a major focus in computer science and captured the imagination of the public as a panacea to myriad big data problems. In this talk, we will discuss how classical machine learning can take advantage of quantum computing to significantly improve its effectiveness. Although we illustrate this concept on a quantum annealer, other quantum platforms could be used as well. If explored fully and implemented efficiently, quantum machine learning could greatly accelerate a wide range of tasks leading to new technologies and discoveries that will significantly change the way we solve real-world problems.

Many of the most challenging computational problems arising in practical applications are tackled by heuristic algorithms which have not been rigorously proven to outperform other approaches but rather have been empirically demonstrated to be effective. While quantum heuristics have been proposed since the early days of quantum computing, true empirical evaluation of the real-world performance of these algorithms is only becoming possible now as increasingly powerful quantum gate-model devices continue to come online.In this talk, I will give an overview of the NASA QuAIL team's ongoing investigation into quantum gate-model heuristic algorithms for exact and approximate optimization. In particular, we consider the performance of the Quantum Approximate Optimization Algorithm on NP-hard optimization problems, and describe algorithm parameter setting strategies for real-world quantum hardware. We then show a generalization of QAOA circuits, the Quantum Alternating Operator Ansatz, especially suitable for low-resource implementations of QAOA for problems with hard (feasibility) constraints. The talk will conclude with a discussion of research challenges, particularly for optimization and sampling applications of QAOA, and the potential of more general quantum heuristics to give advantages over classical computers.

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Quantum computing is an emerging paradigm that leverages the principles of quantum mechanics to solve computational problems beyond the reach of classical computers. This article provides an overview of the fundamental concepts of qubits, the distinctive features of quantum mechanics such as superposition and entanglement, and the challenges of building scalable, fault-tolerant systems. It surveys key quantum algorithms and their potential applications in fields including cryptography, optimization, finance, chemistry, and machine learning. Additionally, it highlights the importance of verification frameworks for ensuring the reliability of quantum programs. A literature review of significant contributions is presented, drawing insights from recent surveys on quantum algorithms, qubit technologies, and software verification approaches. The article concludes by discussing ongoing challenges, such as error correction overhead, hardware scalability, and verification complexity, and suggests directions for future research.

Quantum computing (QC) holds transformative potential for space science research by addressing computationally intractable problems in optimization, simulation, and data analysis. This paper reviews the integration of quantum algorithms such as Quantum Approximate Optimization Algorithm (QAOA), Variational Quantum Eigensolver (VQE), and Grover’s algorithm into space science applications, including mission planning, cosmic simulations, and quantum communication. Comparative analyses reveal that hybrid quantum-classical algorithms outperform classical counterparts in specific optimization tasks, albeit with current limitations in qubit scalability and error rates. The discussion highlights advancements in noise-resilient algorithms and quantum hardware, while future directions emphasize fault-tolerant systems and space-based quantum experiments. This review underscores QC’s nascent yet promising role in revolutionizing space exploration.