Quantum systems simulations

After Benioff, in the year of 1985, David Deutsch gave a decisively important step towards quantum computers presenting the first example of a quantum algorithm [6]. The Deutsch algorithm shows how quantum superposition can be used to speed up computational processes. Another influent name is Richard Feynman, who was involved about the same time in the discussions of the viability of quantum computers and their use for quantum systems simulations [7]. 

Quantum information processing based on room-temperature liquid-state NMR has been extremely successful in testing quantum logic gates, algorithms, quantum system simulations, quantum correlation phenomena, etc., in small scale systems, up to a few qubits. This is basically due to the following aspects ... 

Gillan M J 1990 Path integral simulations of quantum systems Computer Modeling of Fluids and Polymers ed C R A Catlow et al (Dordrecht Kluwer)... 

Berne B J and Thirumalai D 1986 On the simulation of quantum systems path integral methods Ann. Rev. Rhys. Chem. 37 401... 

In this section we look briefly at the problem of including quantum mechanical effects in computer simulations. We shall only examine tire simplest technique, which exploits an isomorphism between a quantum system of atoms and a classical system of ring polymers, each of which represents a path integral of the kind discussed in [193]. For more details on work in this area, see [22, 194] and particularly [195, 196, 197]. 

While the classical approach to simulation of slow activated events, as described above, has received extensive attention in the literature and the methods are in general well established, the methods for quantum-dynamical simulation of reactive processes in complex systems in the condensed phase are still under development. We briefly consider electron and proton quantum dynamics. 

Handy, N.C. Density functional theory. In Quantum mechanical simulation methods for studying biological systems, D. Bicout and M. Field, eds. Springer, Berlin (1996) 1-35. 

P. Bala, P. Grochowski, B. Lesyng, and J. A. McCammon Quantum-classical molecular dynamics. Models and applications. In Quantum Mechanical Simulation Methods for Studying Biological Systems (M. Fields, ed.). Les Houches, France (1995)... 

The computer simulation of models for condensed matter systems has become an important investigative tool in both fundamental and engineering research [149-153] for reviews on MC studies of surface phenomena see Refs. 154, 155. For the reahstic modeling of real materials at low temperatures it is essential to take quantum degrees of freedom into account. Although much progress has been achieved on this topic [156-166], computer simulation of quantum systems still lags behind the development in the field of classical systems. This holds particularly for the determination of dynamical information, which was not possible until recently [167-176]. 

A successful method to obtain dynamical information from computer simulations of quantum systems has recently been proposed by Gubernatis and coworkers [167-169]. It uses concepts from probability theory and Bayesian logic to solve the analytic continuation problem in order to obtain real-time dynamical information from imaginary-time computer simulation data. The method has become known under the name maximum entropy (MaxEnt), and has a wide range of applications in other fields apart from physics. Here we review some of the main ideas of this method and an application [175] to the model fluid described in the previous section. 

Furthermore, one can infer quantitatively from the data in Fig. 13 that the quantum system cannot reach the maximum herringbone ordering even at extremely low temperatures the quantum hbrations depress the saturation value by 10%. In Fig. 13, the order parameter and total energy as obtained from the full quantum simulation are compared with standard approximate theories valid for low and high temperatures. One can clearly see how the quasi classical Feynman-Hibbs curve matches the exact quantum data above 30 K. However, just below the phase transition, this second-order approximation in the quantum fluctuations fails and yields uncontrolled estimates just below the point of failure it gives classical values for the order parameter and the herringbone ordering even vanishes below... 

Simulation of molecules can be done at the quantum mechanical level, as is necessaiy to determine the electronic properties of molecules, to analyze covalent bonds or simulate bond formation and breaking. However, quantum mechanical simulation is extremely computationally intensive and is too time-consuming for all but the smallest molecular systems. 

A more practical approach for larger systems is molecular dynamics. In this method, the properties of bonds are determined through a combination of quantum-mechanical simulation and physical experiments, and stored in a database called a (semi-empirical) force field. Then a classical (non-quantum) simulation is done where bonds are modeled as spring-like interactions. Molecular... 

St. Amant, A., 1996, Practical Density Functional Approaches in Chemistry and Biochemistry in Quantum Mechanical Simulation Methods for Studying Biological Systems, Bicout, D., Field, M. (eds.), Springer, Heidelberg. 

Andricioaei, I. Straub, J.E. Karplus, M., Simulation of quantum systems using path integrals in a generalized ensemble, Chem. Phys. Lett. 2001, 346, 274—282... 

Straub, J.E. Andricioaei, I., Computational methods inspired by Tsallis statistics Monte Carlo and molecular dynamics algorithms for the simulation of classical and quantum systems, Braz. J. Phys. 1999, 29, 179-186... 

The statistical analysis required for real systems is no different in conception from the treatment of the hypothetical two-state system. The elementary particles from which the properties of macroscopic aggregates may be derived by mechanical simulation, could be chemical atoms or molecules, or they may be electrons and atomic nuclei. Depending on the nature of the particles their behaviour could best be described in terms of either classical or quantum mechanics. The statistical mechanics of classical and quantum systems may have many features in common, but equally pronounced differences exist. The two schemes are therefore discussed separately here, starting with the simpler classical sytems. 

Semiclassical techniques like the instanton approach [211] can be applied to tunneling splittings. Finally, one can exploit the close correspondence between the classical and the quantum treatment of a harmonic oscillator and treat the nuclear dynamics classically. From the classical trajectories, correlation functions can be extracted and transformed into spectra. The particular charm of this method rests in the option to carry out the dynamics on the fly, using Born Oppenheimer or fictitious Car Parrinello dynamics [212]. Furthermore, multiple minima on the hypersurface can be treated together as they are accessed by thermal excitation. This makes these methods particularly useful for liquid state or other thermally excited system simulations. Nevertheless, molecular dynamics and Monte Carlo simulations can also provide insights into cold gas-phase cluster formation [213], if a reliable force field is available [189]. 

In some situations we have performed finite temperature molecular dynamics simulations [50, 51] using the aforementioned model systems. On a simplistic level, molecular dynamics can be viewed as the simulation of the finite temperature motion of a system at the atomic level. This contrasts with the conventional static quantum mechanical simulations which map out the potential energy surface at the zero temperature limit. Although static calculations are extremely important in quantifying the potential energy surface of a reaction, its application can be tedious. We have used ah initio molecular dynamics simulations at elevated temperatures (between 300 K and 800 K) to more efficiently explore the potential energy surface. 

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