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Superstaq: Deep Optimization of Quantum Programs

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arxiv 2309.05157 v1 pith:GSZH7IDI submitted 2023-09-10 quant-ph

Colin Campbell , Frederic T. Chong , Denny Dahl , Paige Frederick , Palash Goiporia , Pranav Gokhale , Benjamin Hall , Salahedeen Issa
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Eric Jones Stephanie Lee Andrew Litteken Victory Omole David Owusu-Antwi Michael A. Perlin Rich Rines Kaitlin N. Smith Noah Goss Akel Hashim Ravi Naik Ed Younis Daniel Lobser Christopher G. Yale Benchen Huang Ji Liu
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classification quant-ph
keywords quantumhardwareprogramsdeepexecutionoptimizationperformanceplatforms
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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.

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Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Sphere Packing on a Quantum Computer for Chromatography Modeling

    quant-ph 2024-11 conditional novelty 4.0 of 10

    A proof-of-concept that maps circle packing for chromatography to a maximum independent set problem and runs QAOA on 18 qubits, with resource estimates for harder sphere packing variants.

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