Re-examining the quantum volume test: Ideal distributions, compiler optimizations, confidence intervals, and scalable resource estimations
Quantinuum, 303 S. Technology Ct, Broomfield, Colorado 80021, USA
| Published: | 2022-05-09, volume 6, page 707 |
| Eprint: | arXiv:2110.14808v3 |
| Doi: | https://doi.org/10.22331/q-2022-05-09-707 |
| Citation: | Quantum 6, 707 (2022). |
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Abstract
The quantum volume test is a full-system benchmark for quantum computers that is sensitive to qubit number, fidelity, connectivity, and other quantities believed to be important in building useful devices. The test was designed to produce a single-number measure of a quantum computer's general capability, but a complete understanding of its limitations and operational meaning is still missing. We explore the quantum volume test to better understand its design aspects, sensitivity to errors, passing criteria, and what passing implies about a quantum computer. We elucidate some transient behaviors the test exhibits for small qubit number including the ideal measurement output distributions and the efficacy of common compiler optimizations. We then present an efficient algorithm for estimating the expected heavy output probability under different error models and compiler optimization options, which predicts performance goals for future systems. Additionally, we explore the original confidence interval construction and show that it underachieves the desired coverage level for single shot experiments and overachieves for more typical number of shots. We propose a new confidence interval construction that reaches the specified coverage for typical number of shots and is more efficient in the number of circuits needed to pass the test. We demonstrate these savings with a $QV=2^{10}$ experimental dataset collected from Quantinuum System Model H1-1. Finally, we discuss what the quantum volume test implies about a quantum computer's practical or operational abilities especially in terms of quantum error correction.
Featured image: Steps in the quantum volume test. Upper left: Circuits are generated by a random construction method. Upper right: Circuits are run on a quantum computer (green histograms) and an ideal classical simulator (grey histograms). Lower right: Circuit outputs are sorted based on the classical simulation and the heavy output probability is measured. Lower left: The procedure is repeated for a random set of circuits and the mean heavy output probability is extracted and compared to the passing threshold of 2/3. We analyze all steps in this procedure to gain a better understanding of the quantum volume test.
Repository of code used in simulation and analysis: https://github.com/CQCL/qvtsim
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The above citations are from Crossref's cited-by service (last updated successfully 2024-11-14 22:39:29) and SAO/NASA ADS (last updated successfully 2024-11-14 22:39:30). The list may be incomplete as not all publishers provide suitable and complete citation data.
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