Quantum process tomography with unsupervised learning and tensor
networks
- URL: http://arxiv.org/abs/2006.02424v1
- Date: Wed, 3 Jun 2020 17:54:23 GMT
- Title: Quantum process tomography with unsupervised learning and tensor
networks
- Authors: Giacomo Torlai, Christopher J. Wood, Atithi Acharya, Giuseppe Carleo,
Juan Carrasquilla and Leandro Aolita
- Abstract summary: We present a new technique for performing quantum process tomography.
We combine a tensor network representation of the channel with a data-driven optimization inspired by unsupervised machine learning.
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.
- Score: 0.0
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- 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 new 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 only a
limited set of single-qubit measurement samples and input states. 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.
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