Fast Quantum Process Tomography via Riemannian Gradient Descent

• URL: http://arxiv.org/abs/2404.18840v1
• Date: Mon, 29 Apr 2024 16:28:14 GMT
• Title: Fast Quantum Process Tomography via Riemannian Gradient Descent
• Authors: Daniel Volya, Andrey Nikitin, Prabhat Mishra,
• Abstract summary: Constrained optimization plays a crucial role in the fields of quantum physics and quantum information science. One specific issue is that of quantum process tomography, in which the goal is to retrieve the underlying quantum process based on a given set of measurement data.
• Score: 3.1406146587437904
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: Constrained optimization plays a crucial role in the fields of quantum physics and quantum information science and becomes especially challenging for high-dimensional complex structure problems. One specific issue is that of quantum process tomography, in which the goal is to retrieve the underlying quantum process based on a given set of measurement data. In this paper, we introduce a modified version of stochastic gradient descent on a Riemannian manifold that integrates recent advancements in numerical methods for Riemannian optimization. This approach inherently supports the physically driven constraints of a quantum process, takes advantage of state-of-the-art large-scale stochastic objective optimization, and has superior performance to traditional approaches such as maximum likelihood estimation and projected least squares. The data-driven approach enables accurate, order-of-magnitude faster results, and works with incomplete data. We demonstrate our approach on simulations of quantum processes and in hardware by characterizing an engineered process on quantum computers.

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