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Physical Review Letters

Stochastic Gradient Approximation: An Efficient Method to Optimize Many-Body Wave Functions

Ari Harju, B. Barbiellini, Sami Siljamaki, R. M. Nieminen, Gerardo Ortíz

Published Aug 18, 1997• 74 citations
Publisher

Abstract

A novel, efficient optimization method for physical problems is presented. The method utilizes the noise inherent in stochastic functions. As an application, an algorithm for the variational optimization of quantum many-body wave functions is derived. The numerical results indicate superior performance when compared to traditional techniques.

DOI
10.1103/physrevlett.79.1173

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A novel, efficient optimization method for physical problems is presented. The method utilizes the noise inherent in stochastic functions. As an application, an algorithm for the variational optimization of quantum many-body wave functions is derived. The numerical results indicate superior performance when compared to traditional techniques.
Quantum few-body systems are deceptively simple. Indeed, with the notable exception of a few special cases, their associated Schrodinger equation cannot be solved analytically for more than two particles. One has to resort to approximation methods to tackle quantum few-body problems. In particular, variational methods have been proposed to ease numerical calculations and obtain precise solutions. One such method is the Stochastic Variational Method, which employs a stochastic search to determine the number and parameters of correlated Gaussian basis functions used to construct an ansatz of the wave function. Stochastic methods, however, face numerical and optimization challenges as the number of particles increases. We introduce a family of gradient variational methods that replace stochastic search with gradient optimization. We comparatively and empirically evaluate the performance of the baseline Stochastic Variational Method, several instances of the gradient variational method family, and some hybrid methods for selected few-body problems. We show that gradient and hybrid methods can be more efficient and effective than the Stochastic Variational Method. We discuss the role of singularities, oscillations, and gradient optimization strategies in the performance of the respective methods.
Abstract Quantum few-body systems are deceptively simple. Indeed, with the notable exception of a few special cases, their associated Schrödinger equation cannot be solved analytically for more than two particles. One has to resort to approximation methods to tackle quantum few-body problems. In particular, variational methods have been proposed to ease numerical calculations and obtain precise solutions. One such method is the Stochastic Variational Method, which employs a stochastic search to determine the number and parameters of correlated Gaussian basis functions used to construct an ansatz of the wave function. Stochastic methods, however, face numerical and optimization challenges as the number of particles increases. We introduce a family of gradient variational methods that replace stochastic search with gradient optimization. We comparatively and empirically evaluate the performance of the baseline Stochastic Variational Method, several instances of the gradient variational method family, and some hybrid methods for selected few-body problems. We show that gradient and hybrid methods can be more efficient and effective than the Stochastic Variational Method. We discuss the role of singularities, oscillations, and gradient optimization strategies in the performance of the respective methods.
Quantum-inspired optimization represents a promising avenue for addressing computationally challenging optimization problems. This paper investigates the application of stochastic gradient descent (SGD) to emulate the quantum annealing process, aiming to improve the speed and accuracy of complex optimization tasks. We propose a novel algorithm that iteratively refines a solution based on the gradient of the objective function, mimicking the probabilistic exploration inherent in quantum annealing. This approach offers a potentially more efficient and scalable method compared to traditional optimization techniques, leveraging the strengths of stochasticity. The core mechanism centers around utilizing SGD to dynamically adjust the solution trajectory, effectively mimicking the quantum annealing process.
The Stochastic Gradient Approximation (SGA) is the natural extension of Quantum Monte Carlo (QMC) methods to the variational optimization of quantum wave function parameters. While many deterministic applications impose stochasticity, the SGA fruitfully takes advantage of the natural stochasticity already present in QMC in order to utilize a small number of QMC samples and approach the minimum more quickly by averaging out the random noise in the samples. The increasing efficiency of the method for systems with larger numbers of particles, and its nearly ideal scaling when running on parallelized processors, is evidence that the SGA is well suited for the study of nanoclusters. In this thesis, I discuss the SGA algorithm in detail. I also describe its application to both quantum dots, and to the Resonating Valence Bond wave function (RVB). The RVB is a sophisticated model of electronic systems that captures electronic correlation effects directly and that improves the nodal structure of quantum wave functions. The RVB is receiving renewed attention in the study of nanoclusters due to the fact that calculations of RVB wave functions have become feasible with recent advances in computer hardware and software.
Quantum Gradient Descent for Optimization Quantum optimization is a rapidly growing field that seeks to utilize quantum mechanical phenomena to improve the efficiency of optimization algorithms. This paper investigates the application of quantum gradient descent to complex systems, proposing a hybrid approach that combines the strengths of both quantum and classical computation. We explore the potential of quantum annealing and quantum error correction to enhance gradient calculations, thereby accelerating the search for optimal solutions. The core mechanism involves leveraging quantum properties to perform gradient computations with significantly reduced computational complexity compared to traditional classical methods. This research aims to provide a novel algorithm that demonstrates substantial performance gains for challenging optimization problems, particularly those exhibiting inherent quantum behavior. The paper details the design of the algorithm, its implementation within a simulated environment, and preliminary results demonstrating improved convergence speed and solution quality. Further work will focus on refining the quantum-enhanced gradient calculation to maximize efficiency and scalability.