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arXiv:2404.19659

Regularization of Riemannian optimization: Application to process tomography and quantum machine learning

Felix Soest, Konstantin Beyer, Walter T. Strunz

Published Apr 30, 2024• 0 citations
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Abstract

Gradient descent algorithms on Riemannian manifolds have recently been proposed as a practical tool for optimizing quantum channels. We add rank-penalizing regularization terms to the cost functions of these methods (in the spirit of Lasso), driving the optimization towards channels with as few Kraus operators as possible. For quantum process tomography of rank-deficient channels, regularization both accelerates convergence and improves the fidelity of the solution compared to the unregularized case. For quantum classification tasks, it reduces the channel rank while maintaining classification accuracy, thereby revealing the minimum rank required by the given input data.

DOI
10.48550/arxiv.2404.19659

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Gradient descent algorithms on Riemannian manifolds have recently been proposed as a practical tool for optimizing quantum channels. We add rank-penalizing regularization terms to the cost functions of these methods (in the spirit of Lasso), driving the optimization towards channels with as few Kraus operators as possible. For quantum process tomography of rank-deficient channels, regularization both accelerates convergence and improves the fidelity of the solution compared to the unregularized case. For quantum classification tasks, it reduces the channel rank while maintaining classification accuracy, thereby revealing the minimum rank required by the given input data.
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We propose a non-convex optimization algorithm, based on the Burer-Monteiro (BM) factorization, for the quantum process tomography problem, in order to estimate a low-rank process matrix χ for near-unitary quantum gates. In this work, we compare our approach against state of the art convex optimization approaches based on gradient descent. We use a reduced set of initial states and measurement operators that require 2 · 8ncircuit settings, as well as O(4n)\mathcal{O}(4^n)O(4n) measurements for an underdetermined setting. We find our algorithm converges faster and achieves higher fidelities than state of the art, both in terms of measurement settings, as well as in terms of noise tolerance, in the cases of depolarizing and Gaussian noise models.
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.
Reconstructing the state of quantum many-body systems is of fundamental importance in quantum information tasks, but extremely challenging due to the curse of dimensionality. In this work, we present an efficient quantum tomography protocol that combines the state-factored with eigenvalue mapping to address the rank-deficient issue and incorporates a momentum-accelerated gradient descent algorithm to speed up the optimization process. We implement extensive numerical experiments to demonstrate that our factored gradient descent algorithm efficiently mitigates the rank-deficient problem and admits orders of magnitude better tomography accuracy and faster convergence. We also find that our method can accomplish the full-state tomography of random 11-qubit mixed states within one minute. Published by the American Physical Society 2024
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We analyze convergence of gradient-descent methods on Riemannian manifolds. In particular, we study randomization of Riemannian gradient algorithms for minimizing smooth cost functions (of Morse-Bott type). We prove that randomized gradient descent methods, where the Riemannian gradient is replaced by a random projection of it, converge to a single local optimum almost surely despite the existence of saddle points. We consider both uniformly distributed and discrete random projections. We also discuss the time required to pass a saddle point. As a major application, we consider ground-state preparation through quantum optimization over the unitary group. In mathematical terms our randomized algorithm applied to the trace function U→tr⁡(AUρU∗)U \to \operatorname{tr}(AUρU^*)U→tr(AUρU∗) almost surely converges to its global minimum. The minimum corresponds to the smallest eigenvalue (ground state) of the selfadjoint operator AAA (Hamiltonian) if ρρρ is a rank-one projector (pure state). In this setting, one can efficiently replace the uniform random projections by implementing so-called discrete unitary 2-designs.