Benchmarking

Benchmarking

Our goal is to provide independent, unbiased benchmarking to provide best value to our clients.

  • Hardware capabilities: single and multiple qubit failure rates
  • Performance scaling and error mitigation on NISQ
  • Integration into classical HPC workflows
  • Recommendation for best hardware and algorithm solutions for specific domains

Recent posts:

  • Quantum Economic Development Consortium
    Application-Oriented Performance Benchmarks for Quantum Computing
  • QMC methods- Montanaro
    Classic paper from A. Montanaro on quantum speedup of Monte Carlo methods.
  • On implementing quantum Monte Carlo integration
    Published paper concerning quantum Monte Carlo integration on near-term quantum computers.
  • Google surface code
    Published paper from Google using a surface code to reduce quantum errors. They vary the size of the code and extract error budgets to highlight challenges going forward.
  • SupermarQ
    Published paper from Princeton/University of Chicago about a proposed benchmark suite for quantum hardware.
  • IonQ 11-qubit benchmark
    Published paper from IonQ, Inc about their 11-qubit quantum computer. Single and two qubit fidelities and SPAM errors. Includes two simple algorithms: Bernstein-Vazirani and Hidden Shift.
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