How much can we learn from quantum random circuit sampling?

2025-10-10

AI Summary

Characterizing large-scale quantum processors in situ is limited to estimating only average fidelity, failing to resolve spatiotemporal error distributions and correlated biases. Method: We propose a novel Random Circuit Sampling (RCS) diagnostic framework based on high-dimensional statistical modeling. Without hardware modification or infeasible full-circuit classical simulation, it jointly infers readout bias, spatiotemporally heterogeneous single- and two-qubit errors, and error correlation structure using only RCS bitstring samples and lightweight side information from reference devices. Contribution/Results: We establish the first information-theoretic limits for this learning task and discover a side-information–driven learnability phase transition. Validated on public RCS data from superconducting quantum processors, our inferred error features qualitatively align with component-level calibration while uncovering systematic biases missed by conventional methods—thereby establishing a new quantum benchmarking paradigm that bridges theoretical rigor and practical utility.

Technology Category

• Machine Learning: Calibration & Uncertainty Quantification
• Reasoning under Uncertainty: Relational Probabilistic Models
• Intelligent Robots: State Estimation

Application Category

• Security and Privacy: Large-scale security measurements
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets

Abstract

Benchmarking quantum devices is a foundational task for the sustained development of quantum technologies. However, accurate in situ characterization of large-scale quantum devices remains a formidable challenge: such systems experience many different sources of errors, and cannot be simulated on classical computers. Here, we introduce new benchmarking methods based on random circuit sampling (RCS), that substantially extend the scope of conventional approaches. Unlike existing benchmarks that report only a single quantity--the circuit fidelity--our framework extracts rich diagnostic information, including spatiotemporal error profiles, correlated and contextual errors, and biased readout errors, without requiring any modifications of the experiment. Furthermore, we develop techniques that achieve this task without classically intractable simulations of the quantum circuit, by leveraging side information, in the form of bitstring samples obtained from reference quantum devices. Our approach is based on advanced high-dimensional statistical modeling of RCS data. We sharply characterize the information-theoretic limits of error estimation, deriving matching upper and lower bounds on the sample complexity across all regimes of side information. We identify surprising phase transitions in learnability as the amount of side information varies. We demonstrate our methods using publicly available RCS data from a state-of-the-art superconducting processor, obtaining in situ characterizations that are qualitatively consistent yet quantitatively distinct from component-level calibrations. Our results establish both practical benchmarking protocols for current and future quantum computers and fundamental information-theoretic limits on how much can be learned from RCS data.

Problem

• Developing scalable benchmarking methods for large quantum devices
• Extracting rich error diagnostics without classical circuit simulations
• Establishing information-theoretic limits for quantum error estimation

Innovation

• Rich diagnostic information extraction without experiment modification
• Classically tractable techniques using reference device bitstring samples
• Advanced high-dimensional statistical modeling of RCS data