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arXiv:1910.13207 · Physical Review A

Quantum simulation of the dephasing Anderson model

Max Hunter Gordon, Zsolt Szabó, Robert A. Nyman, Florian Mintert

Published Aug 11, 2020• 2 citations
arXivHTMLPDF

Abstract

The interplay of Anderson localization and decoherence results in intricate dynamics that is notoriously difficult to simulate on classical computers. We develop the framework for a quantum simulation of such an open quantum system making use of time-varying randomized gradients, and show that even an implementation with limited experimental resources results in accurate simulations.

DOI
10.1103/physreva.102.022407

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•Jan 1, 2020•0 citations
The interplay of Anderson localization and decoherence results in intricate dynamics that is notoriously difficult to simulate on classical computers. We develop the framework for a quantum simulation of such an open quantum system making use of time-varying randomized gradients, and show that even an implementation with limited experimental resources results in accurate simulations.
Engineering, controlling, and simulating quantum dynamics is a strenuous task. However, these techniques are crucial to develop quantum technologies, preserve quantum properties, and engineer decoherence. Earlier results have demonstrated reservoir engineering, construction of a quantum simulator for Markovian open systems, and controlled transition from Markovian to non-Markovian regime. Dephasing is an ubiquitous mechanism to degrade the performance of quantum computers. However, all-purpose quantum simulator for generic dephasing is still missing. Here, we demonstrate full experimental control of dephasing allowing us to implement arbitrary decoherence dynamics of a qubit. As examples, we use a photon to simulate the dynamics of a qubit coupled to an Ising chain in a transverse field and also demonstrate a simulation of nonpositive dynamical map. Our platform opens the possibility to simulate dephasing of any physical system and study fundamental questions on open quantum systems.
A quantum system may be efficiently simulated by a quantum information processor as suggested by Feynman and developed by Lloyd, Wiesner, and Zalka. Within the limits of the experimental implementation, simulation permits the design and control of the kinematic and dynamic parameters of a quantum system. Extension to the inclusion of the effects of decoherence, if approached from a full quantum-mechanical treatment of the system and the environment, or from a semiclassical fluctuating field treatment (Langevin), requires the difficult access to dynamics on the time scale of the environment correlation time. Alternatively, a quantum-statistical approach may be taken which exploits the natural decoherence of the experimental system, and requires a more modest control of the dynamics. This is illustrated for quantum simulations of a four-level quantum system by a two-spin NMR ensemble quantum information processor.
Generic open quantum systems are notoriously difficult to simulate unless one looks at specific regimes. In contrast, classical dissipative systems can often be effectively described by stochastic processes, which are generally less computationally expensive. Here, we use the paradigmatic case of a dissipative quantum oscillator to give a pedagogic introduction to the modeling of open quantum systems using quasiclassical methods, i.e., classical stochastic methods that use a "quantum" noise spectrum to capture the influence of the environment on the system. Such quasiclassical methods have the potential to offer insights into the impact of the quantum nature of the environment on the dynamics of the system of interest while still being computationally tractable.
Dephasing decoherence induced by interaction of one qubit with a quantum bath can be simulated classically by random unitary evolution without the need for a bath and this random unitary evolution is equivalent to the quantum case. For a general dephasing model and a single-qubit system, we explicitly construct the noise functional and completely specify the random unitary evolution. To demonstrate the technique, we applied our results to three paradigmatic models: spin-boson, central spin, and quantum impurity. For multiple qubits, we identify a class of generalized quantum dephasing models that can be simulated classically. Finally we show that depolarizing quantum models can be simulated classically for all dimensionalities of the principal system.
This paper discusses the dynamics of interference structures in the Anderson model with decoherence. Due to an intricate interplay of interference, disorder, and decoherence there is a rise and decay of interference peaks on transient time scales, that is the transition from a quantum mechanical interference pattern to a classical probability distribution is of non-monotonic character. An analytic solution in the limit of weak dephasing supplemented with a numerical analysis beyond this limiting case provides an intuitive understanding for the observed phenomena.
Simulating the Hamiltonian dynamics of quantum systems is one of the most promising applications of digital quantum computers. In this dissertation, we develop an understanding of quantum simulation algorithms concerning their design, analysis, implementation, and application. We implement three leading simulation algorithms, employing diverse techniques to tighten their error analyses and optimize circuit implementations. We produce concrete resource estimates for simulating a Heisenberg spin system, a problem arising in condensed matter physics that is otherwise difficult to solve on a classical computer. The resulting circuits are orders of magnitude smaller than those for the simplest classically-infeasible instances of factoring and quantum chemistry, suggesting the simulation of spin systems as a promising candidate for an early demonstration of practical quantum computation. We design new simulation algorithms by using classical randomness. We show that by simply randomizing how the terms in the Hamiltonian are ordered, one can prove stronger bounds for product formulas and thereby give more efficient quantum simulations. We also develop a classical sampler for time-dependent Hamiltonians, using which we give a simulation algorithm that substantially improves over previous approaches when the Hamiltonian varies significantly with time. We propose a general theory to analyzing product formulas, an approach to quantum simulation widely used in experimental demonstrations but whose error scaling was poorly understood. Our approach directly exploits the commutativity of Hamiltonian, overcoming the limitations of prior error analyses. We prove new speedups of product formulas for simulating many quantum systems, including simulations of nearest-neighbor lattice systems, second-quantized plane-wave electronic structure, kkk-local Hamiltonians, rapidly decaying power-law interactions, and clustered Hamiltonians, nearly matching or even outperforming the best previous results in quantum simulation. We accompany our analysis with numerical calculation, which suggests that the bounds also have nearly tight constant prefactors. We identify applications of quantum simulation to designing other quantum algorithms and improving quantum Monte Carlo methods. We develop an algorithmic framework ``quantum singular value transformation'' using techniques from quantum simulation and apply it to implement principal component regression. We also apply our new analysis of product formulas and obtain improved quantum Monte Carlo simulations of the transverse field Ising model and quantum ferromagnets.