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Transactions of the Society of Instrument and Control Engineers

Quantum Tunneling Parameter in Global Optimization

Teturo Itami

Published Jan 1, 2010• 1 citation
Publisher

Abstract

Quantum tunneling that helps particles escape from local minima has been applied in “quantum annealing” method to global optimization of nonlinear functions. To control size of kinetic energy of quantum particles, we form a “quantum tunneling parameter” QT≡m/HR2, where HR corresponds to a physical constant h, Planck's constant divided by 2π, that determines the lowest eigenvalue of quantum particles with mass m. Assumptions on profiles of the function V(x) around its minimum point x0, harmonic oscillator type and square well type, make us possible to write down analytical formulae of the kinetic energy K in terms of QT. The formulae tell that we can make quantum expectation value of particle coordinates x approximate to the minimum point x0 in QT→∞. For systems where we have almost degenerate eigenvalues, examination working with our QT, that x→x0 in QT→∞, is analytically shown also efficient. Similar results that x→x0 under QT→∞ are also obtained when we utilize random-walk quantum Monte Carlo method to represent tunneling phenomena according to conventional quantum annealing.

DOI
10.9746/sicetr.46.336

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Quantum tunneling that helps particles escape from local minima has been applied in “quantum annealing” method to global optimization of nonlinear functions. To control size of kinetic energy of quantum particles, we form a “quantum tunneling parameter” QT≡m/HR2, where HR corresponds to a physical constant h, Planck's constant divided by 2π, that determines the lowest eigenvalue of quantum particles with mass m. Assumptions on profiles of the function V(x) around its minimum point x0, harmonic oscillator type and square well type, make us possible to write down analytical formulae of the kinetic energy K in terms of QT. The formulae tell that we can make quantum expectation value of particle coordinates x approximate to the minimum point x0 in QT→∞. For systems where we have almost degenerate eigenvalues, examination working with our QT, that x→x0 in QT→∞, is analytically shown also efficient. Similar results that x→x0 under QT→∞ are also obtained when we utilize random-walk quantum Monte Carlo method to represent tunneling phenomena according to conventional quantum annealing.
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.
In this work we present a novel method for global optimization, exploiting the mathematics of quantum mechanics, and in particular the tunnelling phenomenon. We consider a quantum mechanical system with a single particle in a potential specified by the cost function of our optimization problem. We assume the groundstate of the system is localised to the global minimum of the potential function, a condition which can be assured by choosing the particle mass sufficiently large. We then approximate this groundstate using a variational approach to optimise an expansion in terms of a truncated basis set of harmonic oscillator wavefunctions. We show how to encode integer factoring and travelling salesman problems in terms of finding the global minimum of a quartic polynomial cost function. We demonstrate our quantum algorithm on oneand two-dimensional toy problems, and apply it to factoring biprimes with 10 bits. Introduction The state of the art in global optimization for general problems is simulated annealing and its derivatives. The algorithm can be viewed in the physical context of a classical thermal system. This inspires the novel approach presented here, in which we draw upon intuition from non-classical physical systems which obey quantum mechanics (QM), and in particular exhibit the phenomenon of quantum tunnelling. In QM, a potential function V (x) describes the energy terrain experienced by a particle, which may contain multiple minima separated by barriers. Combined with a term describing the kinetic energy of the particle, it defines the Hamiltonian operator: Ĥ = − 2 2m ∇2 + V (1) QM captures all available information on the state of the particle in a (complex) wave function ψ(x). For instance, |ψ(x)|2 is the probability distribution over the position x of the particle. To obtain ψ(x) one solves the eigenvalue problem Ĥψ = Eψ (2) known as the Schroedinger equation, where E is the energy. The solution is a set of wave functions termed eigenstates, and a matching set of energies. The eigenstate with the lowest energy is the ground state, ψ. By choosing the particle’s mass sufficiently large, the probability density associated with the ground state wave function becomes localized to the global minimum of the potential. Hence, a given optimization problem involving the minimization of a cost function can be restated as the problem of finding the ground state of the QM system with a potential specified by that cost function. In particular, computationally ‘hard’ integer problems, including factoring [1] and the travelling salesman problem, can be cast as QM problems involving a quartic polynomial potential, [3]. However, solving the Schroedinger equation exactly for these potentials is generally an analytically intractable problem. In our approach we approximate the groundstate of such a system using a method based on perturbation theory.
Both reduction in computational load and setting parameters with ease are necessary in calculation of global optimization. For this we propose a new quantum mechanical method of the problem. Global optimization is achieved by a quantum particle with energy dissipation due to frictional force. Canonical quantization is applied to a system that consists of one mass point under friction proportional to its velocity. Realistic motion of a particle is abstracted from a resultant quantum system according to a causal interpretation. By simulation of the quantum particle motion under potential functions V(x) with local minima and one global minimum, it is clarified that the particle with its initial position at the local minimum at rest necessarily arrives at the global minimum. Only one particle is needed in the new algorithm. When we clarify mathematical structure of our algorithm, difficulties found in quantum annealing or conventional method for global optimization will thereby be solved in the new method.
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 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.