Cross-Platform Comparison of Arbitrary Quantum Computations

Abstract

As we approach the era of quantum advantage, when quantum computers (QCs) can outperform any classical computer on particular tasks, there remains the difficult challenge of how to validate their performance. While algorithmic success can be easily verified in some instances such as number factoring or oracular algorithms, these approaches only provide pass/fail information for a single QC. On the other hand, a comparison between different QCs on the same arbitrary circuit provides a lower-bound for generic validation: a quantum computation is only as valid as the agreement between the results produced on different QCs. Such an approach is also at the heart of evaluating metrological standards such as disparate atomic clocks. In this paper, we report a cross-platform QC comparison using randomized and correlated measurements that results in a wealth of information on the QC systems. We execute several quantum circuits on widely different physical QC platforms and analyze the cross-platform fidelities.

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• Robust estimation of the Quantum Fisher Information on a quantum processor
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• A randomized measurement toolbox for an interacting Rydberg-atom quantum simulator
• Enhanced estimation of quantum properties with common randomized measurements
• Many-body entropies and entanglement from polynomially-many local measurements
• Quantum Similarity Testing with Convolutional Neural Networks
• Efficient distributed inner product estimation via Pauli sampling
• Cross-Platform Verification in Quantum Networks
• Approximate inverse measurement channel for shallow shadows
• Cross-Platform Comparison of Arbitrary Quantum Processes
• Quantum error detection in qubit-resonator star architecture
• Resource-Efficient Cross-Platform Verification with Modular Superconducting Devices
• Real randomized measurements for analyzing properties of quantum states
• On the connection between least squares, regularization, and classical shadows
• Holographic Classical Shadow Tomography
• Active Learning for Quantum Mechanical Measurements