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From Ans\"atze to Z-gates: a NASA View of Quantum Computing

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arxiv 1905.02860 v2 pith:ZGASGZEQ submitted 2019-05-08 quant-ph

classification quant-ph
keywords quantumcomputingresearchalgorithmshardwarenasaworkcomputational
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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.

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  1. Optimizing quantum heuristics with meta-learning

    quant-ph 2019-08 conditional novelty 5.0 of 10

    A gradient-based LSTM meta-learner outperforms L-BFGS-B, Bayesian optimization, evolutionary strategies, and Nelder-Mead at tuning QAOA and VQE parameters in simulated noisy settings.

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