Validating quantum computers using randomized model circuits

By Andrew W. Cross, Lev S. Bishop, S. Sheldon, Paul D. Nation, Jay M. Gambetta. Physical Review A, 2018

698 citations recorded by the source corpus.

We introduce a single-number metric, quantum volume, that can be measured using a concrete protocol on near-term quantum computers of modest size ($n\ensuremath{\lesssim}50$), and measure it on several state-of-the-art transmon devices, finding values as high as 16. The quantum volume is linked to system error rates, and is empirically reduced by uncontrolled interactions within the system. It quantifies the largest random circuit of equal width and depth that the computer successfully implements. Quantum computing systems with high-fidelity operations, high connectivity, large calibrated gate sets, and circuit rewriting toolchains are expected to have higher quantum volumes. The quantum volume is a pragmatic way to measure and compare progress toward improved system-wide gate error rates for near-term quantum computation and error-correction experiments.

Subfields: Artificial Intelligence, Atomic and Molecular Physics, and Optics

View Paper: https://www.semanticscholar.org/paper/3a9f7dda6264e4827e78230d88fea893a9d81b09

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Citations in the corpus

Materials challenges and opportunities for quantum computing hardware (2021, background): “As QIP systems mature, various groups are working to identify new metrics to capture complex systems; for example, the metric of quantum volume param-eterizes computational capacity in a scalar value that incorporates qubit number, fidel-ities, connectivity, and cross-talk ( 34 ).”
Quantum simulation and computing with Rydberg-interacting qubits (2020, methodology): “) introduced by IBM as a way to benchmark the performance of quantum computers 70 .”
Quantum certification and benchmarking (2019, background): “Among them, the notion of a quantum volume [26] is particularly prominent as a meaningful measure of quality in its combination of connectivity, number of qubits, average gate ﬁdelity and number of gates.”
Random Quantum Circuits (2022, background): “Random circuits are also important in quantum information as ingredients of proofs or algorithms (121), and for benchmarking (non-random) quantum circuits (122, 123).”
Emergent Quantum State Designs from Individual Many-Body Wave Functions (2021, background): “Therefore, this approach to generating random states is extremely hardware efficient and could lead to various useful applications in quantum information science [14,15,17,18,32,33].”
t|ket⟩: a retargetable compiler for NISQ devices (2020, background): “Recent results on quantum volume [58] suggest that available qubit numbers already surpass the gate fidelity by such a margin that a large fraction of the qubits cannot effectively be exploited.”
Distributed Quantum Computing: a Survey (2022, neutral): “More specifically, a quantum network utility metric is presented, which applies the Quantum Volume12 proposed in [68] to theUQN framework.”
The Computational Limits of Deep Learning (2020, background): “Of these, quantum computing is the approach with perhaps the most long-term upside, since it might offer a potential for sustained exponential increases in computing power [21, 39].”
Error mitigation with Clifford quantum-circuit data (2020, background): “The (recently retired) device had quantum volume [15] QV = 8 and connectivity enabling simulation of Q = 16 Ising chain without use of qubit swap gates.”
The hardware lottery (2020, background): “…include biological hardware (Tan et al., 2007; Macía & Sole, 2014; Kriegman et al., 2020), analog hardware with in-memory computation (Ambro-gio et al., 2018), neuromorphic computing (Davies, 2019), optical computing (Lin et al., 2018), and quantum computing based approaches (Cross et al., 2019).”
Demonstration of the trapped-ion quantum CCD computer architecture (2020, methodology): “Using up to six qubits, the system operates with low error rates even for deep circuits comprising many transport and gating operations achieving the maximal quantum volume[7] allowed by six qubits without being limited by errors.”
A Race-Track Trapped-Ion Quantum Processor (2023, methodology): “Here we present results from four system-level benchmarks: (A) mirror benchmarking [51, 52], (B) quantum volume (QV) [53, 54], (C) linear cross-entropy measurements for random 2D circuits [55], and (D) creation/certification of N-partite entanglement in GHZ states.”
Demonstration of quantum volume 64 on a superconducting quantum computing system (2020, methodology): “Red: cumulative HOP with shaded region ±2σ as calculated per appendix C of [1].”