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| Quantum Volume | |
|---|---|
| Name | Quantum Volume |
| Field | Quantum computing |
| Introduced | 2017 |
| Developers | IBM |
| Units | dimensionless |
Quantum Volume Quantum Volume is a composite performance metric for quantum computers introduced to quantify the effective computational power of noisy intermediate-scale quantum processors. It summarizes architecture features such as qubit count, gate fidelity, connectivity, and crosstalk into a single dimensionless value, enabling comparisons across devices from organizations like IBM, Google, Rigetti, and IonQ. The metric has influenced benchmarking efforts at institutions including MIT, Stanford University, and University of Oxford.
Quantum Volume was proposed to provide a hardware-agnostic indicator that reflects real-world performance of quantum devices developed by firms and labs such as IBM Research, Google AI Quantum, Rigetti, Microsoft Research, and Alibaba Group. It addresses challenges encountered by teams at Oak Ridge National Laboratory, Los Alamos National Laboratory, and Lawrence Berkeley National Laboratory when comparing devices from vendors including D-Wave Systems and Honeywell. Funding and evaluation activities tied to agencies like the NASA, DARPA, and the European Research Council helped popularize standardized metrics.
Quantum Volume is defined as 2^k where k is the largest integer for which a device can successfully run all random circuits of width k and depth k with a specified heavy-output probability threshold. The formalism builds on concepts used in experiments at IBM Research, Google, and analytical frameworks from researchers at Harvard University, Yale University, and Caltech. It connects to theoretical constructs from Shor's algorithm-era analyses as well as fidelity models developed by teams at Sandia National Laboratories and NIST.
Measuring Quantum Volume requires constructing ensembles of random model circuits that explore device connectivity and native gate sets used by platforms from IonQ, Rigetti, IBM, and Google. Protocols leverage benchmarking techniques related to randomized benchmarking and concepts advanced by groups at UC Berkeley and University of Chicago. Experimental campaigns typically involve compiling circuits with optimizers created by researchers at MIT, Microsoft Research, and Xanadu, executing them on target hardware, and computing heavy-output frequencies following statistical thresholds used by NIST and standards groups. Results reported by entities like IBM Research and Google AI Quantum are often vetted through collaborations with academic partners such as University of Waterloo, University of Maryland, and University of Innsbruck.
Quantum Volume is interpreted as a conservative summary of a device's ability to run square random circuits but is not a measure of suitability for specific algorithms like those developed by teams at Quantum Algorithm Zoo-style collections, or application domains pursued by GlaxoSmithKline and Goldman Sachs in quantum chemistry and finance. Critics from research groups at University of Toronto, ETH Zurich, and Imperial College London emphasize limitations including sensitivity to compiler quality, dependence on chosen gate sets, and lack of direct mapping to algorithmic runtime used in proposals by Alexei Kitaev-inspired work. Standardization discussions have involved bodies such as the IEEE and panels convened by the NIST.
Quantum Volume guides procurement and roadmap planning at companies like IBM, Google, Microsoft, and startups such as Rigetti and IonQ. It informs research prioritization in quantum chemistry projects at BASF and Pfizer and aids experimental design for error-mitigation studies at University of Cambridge and ETH Zurich. Government labs including Los Alamos National Laboratory and Lawrence Livermore National Laboratory use the metric for comparative assessments, while consortia such as the Quantum Economic Development Consortium discuss its role alongside workflow benchmarks pursued by Xanadu and commercial users like Goldman Sachs.
Quantum Volume complements other performance metrics such as circuit depth measures, task-specific benchmarks developed by Benchmarking Quantum Computers initiatives, and fidelity metrics like gate fidelity and state fidelity used by labs such as NIST and Sandia National Laboratories. Alternative proposals from academic and industrial groups include algorithmic benchmarks inspired by VQE workloads, application-level testing promoted by IBM Research and challenge suites developed at University of Maryland and Argonne National Laboratory.
The Quantum Volume concept was articulated in publications and technical notes from IBM Research around 2017, with methodological refinements contributed by collaborators at MIT, University of Oxford, and University of California, Berkeley. Key experimental demonstrations involved teams at IBM, Google AI Quantum, and startups such as Rigetti, while theoretical analysis drew on prior benchmarking literature from NIST, Sandia National Laboratories, and academic groups at Harvard University and Yale University. Subsequent discussion and adoption included standards conversations with organizations like the IEEE and policy engagement with agencies including DARPA and the European Commission.