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Annalen der Physik

A renormalization approach to quantum thermal annealing

Yong‐ Han Lee, B. J. Berne

Published Oct 1, 2000• 5 citations
Publisher

Abstract

The details of an efficient global optimization approach, quantum thermal annealing with renormalization (QTAR) (Y. H. Lee and B. J. Berne, J. Phys. Chem. A, in press (2000)) are presented in this paper. This method is based on the application of the Migdal-Kadanoff method for decimating Trotter time slices in the staging and primitive algorithms for sampling path integrals using Monte Carlo methods. In a nutshell, one starts in a strong quantum regime where the number of Trotter beads representing each quantum particle and the value of Planck's constant are large, thereby allowing for efficient tunneling through the barriers of a rough energy landscape typical in the folding of proteins, and anneals the system methodically to the classical limit where the values of the aforementioned quantities are 1 and 0, respectively. Global optimization of the system is achieved through the iterative use of such quantum-to-classical annealing cycles. The QTAR algorithm applied to a highly frustrated BLN model protein with 46 residues more efficiently locates the global energy minimum than established methods like simulated annealing.

DOI
10.1002/1521-3889(200010)9:9/10<668::aid-andp668>3.0.co;2-j

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The details of an efficient global optimization approach, quantum thermal annealing with renormalization (QTAR) (Y. H. Lee and B. J. Berne, J. Phys. Chem. A, in press (2000)) are presented in this paper. This method is based on the application of the Migdal-Kadanoff method for decimating Trotter time slices in the staging and primitive algorithms for sampling path integrals using Monte Carlo methods. In a nutshell, one starts in a strong quantum regime where the number of Trotter beads representing each quantum particle and the value of Planck's constant are large, thereby allowing for efficient tunneling through the barriers of a rough energy landscape typical in the folding of proteins, and anneals the system methodically to the classical limit where the values of the aforementioned quantities are 1 and 0, respectively. Global optimization of the system is achieved through the iterative use of such quantum-to-classical annealing cycles. The QTAR algorithm applied to a highly frustrated BLN model protein with 46 residues more efficiently locates the global energy minimum than established methods like simulated annealing.
Abstract The details of an efficient global optimization approach, quantum thermal annealing with renormalization (QTAR) (Y. H. Lee and B. J. Berne, J. Phys. Chem. A, in press (2000)) are presented in this paper. This method is based on the application of the Migdal‐Kadanoff method for decimating Trotter time slices in the staging and primitive algorithms for sampling path integrals using Monte Carlo methods. In a nutshell, one starts in a strong quantum regime where the number of Trotter beads representing each quantum particle and the value of Planck's constant are large, thereby allowing for efficient tunneling through the barriers of a rough energy landscape typical in the folding of proteins, and anneals the system methodically to the classical limit where the values of the aforementioned quantities are 1 and 0, respectively. Global optimization of the system is achieved through the iterative use of such quantum‐to‐classical annealing cycles. The QTAR algorithm applied to a highly frustrated BLN model protein with 46 residues more efficiently locates the global energy minimum than established methods like simulated annealing.
A renormalization approach is introduced in quantum thermal annealing. This new global optimization algorithm (QTAR) is applied to a highly frustrated BLN model protein with 46 residues. The underlying quantum sampling algorithms utilized are primitive and staging path integral Monte Carlo. The current method enables us to achieve significant improvement in the success rate for locating the global minimum of this protein, while using much less computational effort, compared to previous schemes used. Further applications and possible enhancements of the algorithm are also discussed.
Quantum annealing was recently found experimentally in a disordered spin-12\frac{1}{2}21​ magnet to be more effective than its classical, thermal counterpart. We use the random two-dimensional Ising model as a test example and perform on it both classical and quantum (path-integral) Monte Carlo annealing. A systematic study of the dependence of the final residual energy on the annealing Monte Carlo time quantitatively demonstrates the superiority of quantum relative to classical annealing in this system. In order to determine the parameter regime for optimal efficiency of the quantum annealing procedure we explore a range of values of Trotter slice number P and temperature T. This identifies two different regimes of freezing with respect to efficiency of the algorithm, and leads to useful guidelines for the optimal choice of quantum annealing parameters.
A new unbiased global optimization approach is proposed, based on quantum staging path integral Monte Carlo sampling and local minimization of individual imaginary time slices. This algorithm uses the quantum tunneling effect to speed up the crossing of energy barriers. This method differs in important ways from previous work on quantum annealing and is able to find all the global minima of Lennard-Jones clusters of size up to N=100, except for N=76, 77, and 98. The comparison between this new algorithm and several other classes of algorithms is presented.
Finding the global minimum of a potential energy surface is a fundamental challenge in materials science, with applications ranging from protein folding to cluster physics and, more broadly, to systems in which the number of (meta)stable configurations grows prohibitively large. In recent decades, quantum annealing (QA) has emerged as a promising global optimization strategy, exploiting quantum fluctuations in contrast to the thermal fluctuations that drive its classical counterpart. Here, we introduce a novel implementation of QA based on path-integral molecular dynamics, an efficient and well-established framework for sampling the quantum nuclear density without the need to manipulate many-body wavefunctions explicitly. While retaining the flexibility and simplicity of molecular dynamics simulations, this quantum-annealing protocol delivers strong performance across a wide range of atomic systems, simulated by either empirical force fields or machine-learning interatomic potentials. The method can be used either as a global optimizer of the potential-energy surface, or as a quantum-informed structure-search strategy in which nuclear quantum effects are included directly in the optimization workflow -- a feature particularly relevant for materials such as high-pressure hydrides.
Abstract Quantum Monte Carlo has found a new application far afield from the solution of the Schrodinger equation for many-body problems. In this paper the authors report a method for solving multidimensional optimization problems that is superior, in many cases, to other methods such as conjugate gradient methods, the simplex method, direction-set methods, genetic methods, and classical simulated annealing. The new approach, aptly titled “quantum annealing,” is closely related to classical annealing, in which a physical system such as a metal cluster is slowly cooled and finds its lowest-energy configuration as the temperature drops to zero. In classical annealing the system follows classical-mechanical dynamics as the temperature drops. In quantum annealing a collection of configurations evolves in the the same way as walkers undergoing diffusion and multiplication in diffusion QMC with the added feature of a slowly dropping value for Planck’s constant h. Classical annealing allows the escape from local minima with the aid of thermal fluctuations. Quantum annealing allows such escape by delocalization and tunneling. Since a QMC walker can multiply in number on entering a low-energy region, a single walker can populate an entire region.