Quantum Volume in Practice: What Users Can Expect from NISQ Devices

Abstract

Quantum volume (QV) has become the de-facto standard benchmark to quantify the capability of Noisy Intermediate-Scale Quantum (NISQ) devices. While QV values are often reported by NISQ providers for their systems, we perform our own series of QV calculations on 24 NISQ devices currently offered by IBM Q, IonQ, Rigetti, Oxford Quantum Circuits, and Quantinuum (formerly Honeywell). Our approach characterizes the performances that an advanced user of these NISQ devices can expect to achieve with a reasonable amount of optimization, but without white-box access to the device. In particular, we compile QV circuits to standard gate sets of the vendor using compiler optimization routines where available, and we perform experiments across different qubit subsets. We find that running QV tests requires very significant compilation cycles, QV values achieved in our tests typically lag behind officially reported results and also depend significantly on the classical compilation effort invested.

v5: typo fixes and improved typesetting

References in corpus (8)

• Quantum circuits with many photons on a programmable nanophotonic chip
• Mathematical Foundation of Quantum Annealing
• Resource-Aware Quantum Programming with General Recursion and Quantum Control
• Quantum Volume in Practice: What Users Can Expect from NISQ Devices
• On the qubit routing problem
• Scalable Benchmarks for Gate-Based Quantum Computers
• Stability of noisy quantum computing devices
• Error mitigation increases the effective quantum volume of quantum computers

Cited by in corpus (7)

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• Quantum Volume in Practice: What Users Can Expect from NISQ Devices
• Enhancing the Coherence of Superconducting Quantum Bits with Electric Fields
• Squeezing and quantum approximate optimization
• Quantum Advantage Seeker with Kernels (QuASK): a software framework to speed up the research in quantum machine learning
• Ab-initio tree-tensor-network digital twin for quantum computer benchmarking in 2D
• Navigating the noise-depth tradeoff in adiabatic quantum circuits