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IEEE Dallas Circuits and Systems Conference (DCAS)

Quantum SGD in Continuous-Time Limit by the Wigner Formulation of Open Quantum Systems

Avery Tovar, Brandon Lippe, José A. Morales Escalante

Published Apr 10, 2026• 0 citations
Publisher

Abstract

This paper studies the continuous-time limit of a quantum analog of the classical Stochastic Gradient Descent (SGD) algorithm by formulating the problem in the Wigner description of quantum mechanics, thereby offering novelty in the description of the corresponding Stochastic Differential Equation (SDE) versus more obscure approaches regarding intuition, such as Lindblad formulations of quantum SGD algorithms. The Wigner formulation is a natural bridge between classical and quantum probabilistic algorithms, which, to our knowledge, has not been applied yet to the study of quantum analogs of SGD. We focus on the particular case of Quantum SGD in its continuous-time limit under a harmonic potential for the optimization of a quadratic function. We present both theoretical and computational studies of its convergence for the aforementioned benchmark problem. We prove, for this benchmark, the independence of the mixing time (convergence rate) from the phase-space dimension by studying the eigenvalues of the steadystate inverse covariance matrix in any dimension.

DOI
10.1109/dcas69364.2026.11544686

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This paper studies the continuous-time limit of a quantum analog of the classical Stochastic Gradient Descent (SGD) algorithm by formulating the problem in the Wigner description of quantum mechanics, thereby offering novelty in the description of the corresponding Stochastic Differential Equation (SDE) versus more obscure approaches regarding intuition, such as Lindblad formulations of quantum SGD algorithms. The Wigner formulation is a natural bridge between classical and quantum probabilistic algorithms, which, to our knowledge, has not been applied yet to the study of quantum analogs of SGD. We focus on the particular case of Quantum SGD in its continuous-time limit under a harmonic potential for the optimization of a quadratic function. We present both theoretical and computational studies of its convergence for the aforementioned benchmark problem. We prove, for this benchmark, the independence of the mixing time (convergence rate) from the phase-space dimension by studying the eigenvalues of the steadystate inverse covariance matrix in any dimension.
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Abstract: Quantum-enhanced Stochastic Gradient Descent (Q-SGD) is a novel optimization algorithm that integrates quantum computation to accelerate exploration of the solution space, offering a significant advantage over classical methods, particularly in complex optimization problems. This paper explores the potential of quantum annealing and gate operations to refine the optimization process, leading to faster convergence and improved results. We present a theoretical framework and preliminary experiments demonstrating the efficacy of this approach.
This paper is concerned with the Coherent Quantum Linear Quadratic Gaussian (CQLQG) control problem of finding a stabilizing measurement-free quantum controller for a quantum plant so as to minimize an infinite-horizon mean square performance index for the fully quantum closed-loop system. In comparison with the observation-actuation structure of classical controllers, the coherent quantum feedback is less invasive to the quantum dynamics and quantum information. Both the plant and the controller are open quantum systems whose dynamic variables satisfy the canonical commutation relations (CCRs) of a quantum harmonic oscillator and are governed by linear quantum stochastic differential equations (QSDEs). In order to correspond to such oscillators, these QSDEs must satisfy physical realizability (PR) conditions, which are organised as quadratic constraints on the controller matrices and reflect the preservation of CCRs in time. The CQLQG problem is a constrained optimization problem for the steady-state quantum covariance matrix of the plant-controller system satisfying an algebraic Lyapunov equation. We propose a gradient descent algorithm equipped with adaptive stepsize selection for the numerical solution of the problem. The algorithm finds a local minimum of the LQG cost over the parameters of the Hamiltonian and coupling operators of a stabilizing PR quantum controller, thus taking the PR constraints into account. A convergence analysis of the proposed algorithm is presented.
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Stochastic Gradient Descent (SGD) is a cornerstone algorithm in deep learning, widely employed for optimizing model parameters. However, SGD is susceptible to getting trapped in local minima, leading to suboptimal solutions and hindering the training process. This paper proposes a novel approach, Quantum-Inspired Stochastic Gradient Descent (QISGD), which leverages principles from quantum walks to enhance exploration and overcome the limitations of traditional SGD. The core idea is to adaptively adjust the learning rate based on the uncertainty associated with the gradient estimate, mirroring the probabilistic nature of quantum walks. Specifically, QISGD employs a stochastic update rule where the step size is dynamically modified, promoting wider exploration of the parameter space. The theoretical framework establishes connections between the proposed method and quantum walk concepts, highlighting its potential to improve training speed and convergence rates. We demonstrate, through analysis and mathematical formulation, that QISGD can effectively mitigate the issues of local minima and enhance the overall optimization performance of deep learning models. The presented formulation offers a new perspective on SGD, blending classical optimization with quantum-inspired concepts for enhanced training.
This paper introduces a novel stochastic gradient descent (SGD) algorithm, termed Quantum-Inspired Stochastic Gradient Descent with Amplitude Control (QISGDA). The core idea is to leverage concepts inspired by quantum mechanics, specifically amplitude control, to dynamically adjust the step size and exploration strategy within the SGD process. The algorithm incorporates a parameter, denoted as 'α', which governs the magnitude of updates, influenced by both the gradient information and a randomly generated noise term. The algorithm's key innovation lies in the adaptive modulation of this amplitude, aiming to achieve a more efficient balance between exploration and exploitation, leading to faster convergence and potentially improved performance compared to traditional SGD methods. This work presents a detailed formulation of QISGDA, including the mathematical expressions for the update rule and the amplitude control mechanism. The algorithm is theoretically analyzed, and its effectiveness is implicitly demonstrated through the design and implementation of a dynamic amplitude control strategy. The primary contribution of this research is the development of a sophisticated approach to SGD that draws inspiration from quantum principles, offering a new perspective on optimization techniques.