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Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE

Algorithmic approach to quantum theory 2: method of collective behavior and Monte-Carlo method

Igor Semenihin, Yuri Ozhigov

Published May 24, 2006• 3 citations
Publisher

Abstract

The method of collective behavior is based on the representation of real quantum particle by the swarm of classical particles which have all properties of the initial particle but have classical states like coordinates and impulse. Simulation with swarms can be more flexible and powerful than analytical methods because it preserves the methodology of classical description of dynamics. The method of collective behavior is illustrated on the diffusion Monte Carlo way of calculating stationary states of electrons.

DOI
10.1117/12.683113

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The method of collective behavior is based on the representation of real quantum particle by the swarm of classical particles which have all properties of the initial particle but have classical states like coordinates and impulse. Simulation with swarms can be more flexible and powerful than analytical methods because it preserves the methodology of classical description of dynamics. The method of collective behavior is illustrated on the diffusion Monte Carlo way of calculating stationary states of electrons.
With the help of a collective behavior method, quantum dynamics is represented as a result of mutual transformations of a swarm of classical samples of quantum particles. The method used is shown to generalize the Monte Carlo diffusion method to the case of many-body quantum dynamics. The method is illustrated by the example of the Schrödinger equation. It is shown that the diffusion representation of the Coulomb field linearly simulates a multiparticle system compared to the traditional method of quadratic complexity. A simple method of description of entangled states of any complexity with the help of association reactions of samples of quantum particles is given.
The simple algorithm for the simulation and visualization of non relativistic quantum dynamics is proposed that is based on a collective behavior of classical particles. Any quantum particle is represented as the swarm of its classical samples which interact by simple rules including emission and absorption of samples of tied photons. The quantum dynamics results from the collective behavior of such a swarm where the eigenstates are treated as the equilibrium states relatively to emission-absorption of photons. The entanglement is treated as a correlation between samples of the different swarms that is stored in the space-time part of the model inaccessible for a user. The amplitude is always grained. The Coulomb field between quantum particles is simulated, analogously to free flow of quantum package, by the point wise interaction between its samples and scalar photon samples which propagate by diffusion. It gives square root speedup in comparison to each-with-each method. This method obviously includes decoherence and admits the natural generalization on the QED of many particles with the linear computational cost.
A straightforward algorithm is proposed for simulating and visualizing nonrelativistic quantum dynamics in terms of the collective behavior of classical particles. In this approach, a quantum particle is treated as a swarm (collection) of its classical samples interacting with one another by simple rules involving the emission and absorption of associated photons. The quantum dynamics is regarded as resulting from the collective behavior of the swarm, and the eigenstates are treated as equilibrium states of the process of photon emission and absorption. Entanglement is seen as classical correlation between samples from distinct swarms; the information on this correlation is stored in a spatiotemporal section of the model inaccessible to the user. Amplitudes are invariably treated as consisting of amplitude quanta. A Coulomb field experienced by quantum particles is represented in a similar manner to the free spreading of a wave packet, by point interaction between the particle samples and the samples of scalar photons propagating by diffusion. This provides a square-root gam in computational speed against the direct method involving item-by-item examination. The approach presented covers decoherence and allows natural generalization to multiparticle quantum electrodynamics, the amount of memory required growing linearly.
Introduction In Chapters 1 to 4 we studied methods for solving the Schrödinger equation for many-electron systems. Many of the techniques described there carry over to other quantum many-particle systems, such as liquid helium, and the protons and neutrons in a nucleus. The techniques which we discussed there were, however, all of a mean-field type and therefore correlation effects could not be taken into account without introducing approximations. In this chapter, we consider more accurate techniques, which are similar to those studied in Chapter 10 and are based on using (pseudo-)random numbers – hence the name ‘Monte Carlo’ for these methods. In Chapter 10 we applied Monte Carlo techniques to classical many-particle systems; here we use these techniques for studying quantum problems involving many particles. In the next section we shall see how we can apply Monte Carlo techniques to the problem of calculating the quantum mechanical expectation value of the ground state energy. This is used in order to optimise this expectation value by adjusting a trial wave function in a variational type of approach, hence the name variational Monte Carlo (VMC). In the following section we use the similarity between the Schrödinger equation and the diffusion equation in order to calculate the properties of a collection of interacting quantum mechanical particles by simulating a classical particle diffusion process. The resulting method is called diffusion Monte Carlo (DMC).
Quantum Monte Carlo methods represent a powerful and broadly applicable computational tool for finding very accurate solutions of the stationary Schrödinger equation for atoms, molecules, solids and a variety of model systems. The algorithms are intrinsically parallel and are able to take full advantage of present-day high-performance computing systems. This review paper concentrates on the fixed-node/fixed-phase diffusion Monte Carlo method with emphasis on its applications to the electronic structure of solids and other extended many-particle systems.
… a general method, suitable for electronic computing machines, of calculating the properties of any substance which may be considered as composed of interacting individual molecules. N. Metropolis et al. , 1953 Summary Quantum Monte Carlo methods have been very useful in providing exact results, or, at least, exact constraints on properties of electronic systems, in particular for the homogeneous electron gas. The results are, in many cases, more accurate than those from other quantum many-body methods, and provide unique capabilities and insights. In this chapter we introduce the general properties of stochastic methods and motivate their use on the quantum many-body problem. In particular, we discuss Markov chains and the computation of error estimates. The methods that we introduce in the next four chapters are quite different from those in Parts II and III: stochastic or quantum Monte Carlo methods. In stochastic methods, instead of solving deterministically for properties of the quantum many-body system, one sets up a random walk that samples for the properties. Historically the most important role of QMC for the electronic structure field has been to provide input into the other methods, most notably the QMC calculation of the HEG [109], used for the exchange– correlation functional in DFT. A second important role has been as benchmarks for other methods such asGW. There are systems for which QMC is uniquely suited, for example the Wigner transition in the low-density electron gas, see Sec. 3.1. In this chapter we introduce general properties of simulations, in particular Markov chains, and error estimates. In the following chapters we will apply this theory to three general classes of quantum Monte Carlo algorithms, namely variational (Ch. 23), projector (Ch. 24), and path-integral Monte Carlo (Ch. 25); Ch. 18 already introduced the QMC calculation of the impurity Green's function used in the dynamical mean-field method.We note that there are a variety of other QMC methods not covered in this book. Simulations First let us define what we mean by a simulation, since the word has other meanings in applied science. The dimensionality of phase space (i.e., the Hilbert space for a quantum system) is large or infinite. Even a classical system requires the positions and momenta of all particles, and, hence, the phase space for N classical particles has dimensionality 6 N .