Accurate Numerical Simulations of Open Quantum Systems Using Spectral Tensor Trains

TL;DR

This paper introduces Q-ASPEN, a spectral tensor train-based numerical method for accurately simulating open quantum systems with noise, enabling efficient analysis of decoherence in quantum computing.

Contribution

It develops a novel spectral tensor train approach combined with neural network training methods to simulate quantum relaxation with high accuracy and scalability.

Findings

Q-ASPEN achieves linear memory scaling with system size.

It accurately simulates spin-boson models with intrinsic noise.

It handles quantum chains of up to 32 sites efficiently.

Abstract

Decoherence between qubits is a major bottleneck in quantum computations. Decoherence results from intrinsic quantum and thermal fluctuations as well as noise in the external fields that perform the measurement and preparation processes. With prescribed colored noise spectra for intrinsic and extrinsic noise, we present a numerical method, Quantum Accelerated Stochastic Propagator Evaluation (Q-ASPEN), to solve the time-dependent noise-averaged reduced density matrix in the presence of intrinsic and extrinsic noise. Q-ASPEN is arbitrarily accurate and can be applied to provide estimates for the resources needed to error-correct quantum computations. We employ spectral tensor trains, which combine the advantages of tensor networks and pseudospectral methods, as a variational ansatz to the quantum relaxation problem and optimize the ansatz using methods typically used to train neural networks. The spectral tensor trains in Q-ASPEN make accurate calculations with tens of quantum levels feasible. We present benchmarks for Q-ASPEN on the spin-boson model in the presence of intrinsic noise and on a quantum chain of up to 32 sites in the presence of extrinsic noise. In our benchmark, the memory cost of Q-ASPEN scales linearly with the system size once the number of states is larger than the number of basis functions.