Accumulative reservoir construction: Bridging continuously relaxed and periodically refreshed extended reservoirs

TL;DR

This paper introduces an accumulative reservoir construction (ARC) for simulating open quantum systems, unifying continuous and discrete reservoir relaxation methods, and analyzes their error-cost scaling and physical regimes.

Contribution

The paper presents a novel ARC framework that combines continuous and periodic reservoir refreshes, providing a unified approach for open quantum system simulations.

Findings

ARC effectively accumulates reservoir character over partial refreshes.

Distinct relaxation regimes are identified with phase space separation.

Error versus cost scaling differs among reservoir approaches, with ARC decaying fastest.

Abstract

The simulation of open many-body quantum systems is challenging, requiring methods to both handle exponentially large Hilbert spaces and represent the influence of (infinite) particle and energy reservoirs. These two requirements are at odds with each other: Larger collections of modes can increase the fidelity of the reservoir representation but come at a substantial computational cost when included in numerical many-body techniques. An increasingly utilized and natural approach to control the growth of the reservoir is to cast a finite set of reservoir modes themselves as an open quantum system. There are, though, many routes to do so. Here, we introduce an accumulative reservoir construction -- an ARC -- that employs a series of partial refreshes of the extended reservoirs. Through this series, the representation accumulates the character of an infinite reservoir. This provides a unified framework for both continuous (Lindblad) relaxation and a recently introduced periodically refresh approach (i.e., discrete resets of the reservoir modes to equilibrium). In the context of quantum transport, we show that the phase space for physical behavior separates into discrete and continuous relaxation regimes with the boundary between them set by natural, physical timescales. Both of these regimes "turnover" into regions of over- and under-damped coherence in a way reminiscent of Kramers' crossover. We examine how the range of behavior impacts errors and the computational cost, including within tensor networks. These results provide the first comparison of distinct extended reservoir approaches, showing that they have different scaling of error versus cost (with a bridging ARC regime decaying fastest). Exploiting the enhanced scaling, though, will be challenging, as it comes with a substantial increase in (operator space) entanglement entropy.