Quantum process tomography with unsupervised learning and tensor networks

Giacomo Torlai (), Christopher J. Wood, Atithi Acharya, Giuseppe Carleo, Juan Carrasquilla and Leandro Aolita
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Giacomo Torlai: AWS Center for Quantum Computing
Christopher J. Wood: IBM T.J. Watson Research Center
Atithi Acharya: Center for Computational Quantum Physics, Flatiron Institute
Giuseppe Carleo: Center for Computational Quantum Physics, Flatiron Institute
Juan Carrasquilla: Vector Institute, MaRS Centre
Leandro Aolita: Quantum Research Centre, Technology Innovation Institute

Nature Communications, 2023, vol. 14, issue 1, 1-10

Abstract: Abstract The impressive pace of advance of quantum technology calls for robust and scalable techniques for the characterization and validation of quantum hardware. Quantum process tomography, the reconstruction of an unknown quantum channel from measurement data, remains the quintessential primitive to completely characterize quantum devices. However, due to the exponential scaling of the required data and classical post-processing, its range of applicability is typically restricted to one- and two-qubit gates. Here, we present a technique for performing quantum process tomography that addresses these issues by combining a tensor network representation of the channel with a data-driven optimization inspired by unsupervised machine learning. We demonstrate our technique through synthetically generated data for ideal one- and two-dimensional random quantum circuits of up to 10 qubits, and a noisy 5-qubit circuit, reaching process fidelities above 0.99 using several orders of magnitude fewer (single-qubit) measurement shots than traditional tomographic techniques. Our results go far beyond state-of-the-art, providing a practical and timely tool for benchmarking quantum circuits in current and near-term quantum computers.

Date: 2023
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DOI: 10.1038/s41467-023-38332-9

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