Benchmarking Quantum Processor Performance through Quantum Distance Metrics Over An Algorithm Suite

Conference · Mon Aug 01 04:00:00 EDT 2022

DOI:https://doi.org/10.1109/IPDPSW55747.2022.00106· OSTI ID:1894585

Stein, Samuel A. ^[1]; Wiebe, Nathan ^[2]; Ang, James A. ^[1]; Li, Ang ^[1]

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1. BATTELLE (PACIFIC NW LAB)
2. University of Toronto

Quantum computing is poised to solve computational paradigms that classical computing could never feasibly reach. Tasks such as prime factorization to Quantum Chemistry are examples of classically difficult problems that have analogous algorithms that are sped up on quantum computers. To attain this computational advantage, we must first traverse the noisy intermediate scale quantum (NISQ) era, in which quantum processors suffer from compounding noise factors that can lead to unreliable algorithm induction producing noisy results. We describe QASMBench, a suite of QASM-level (Quantum assembly language) benchmarks that challenge all realisable angles of quantum processor noise. We evaluate a large portion of these algorithms by performing density matrix tomography on 14 IBMQ Quantum devices.

• Research Organization: Pacific Northwest National Laboratory (PNNL), Richland, WA (United States)
• Sponsoring Organization: USDOE
• DOE Contract Number: AC05-76RL01830
• OSTI ID: 1894585
• Report Number(s): PNNL-SA-172019
• Country of Publication: United States
• Language: English