Data-driven learning of non-Markovian quantum dynamics

TL;DR

This paper introduces a data-driven method to learn and characterize non-Markovian quantum dynamics, enabling detailed reconstruction of quantum evolution including complex noise effects, which enhances quantum gate diagnostics.

Contribution

The authors develop a novel learning protocol based on the NMZ formulation to reconstruct non-Markovian quantum dynamics from time series data, applicable to both simulations and experimental data.

Findings

Successfully learned quantum generators in simulated and experimental systems.

Identified the timescale where non-Markovian effects become significant.

Complemented existing gate characterization methods by capturing non-Markovianity.

Abstract

Fault-tolerant quantum computing requires extremely precise knowledge and control of qubit dynamics during the application of a gate. We develop a data-driven learning protocol for characterizing quantum gates that builds off previous work on learning the Nakajima-Mori-Zwanzig (NMZ) formulation of open system dynamics from time series data, which allows detailed reconstruction of quantum evolution, including non-Markovian dynamics. We demonstrate this learning technique on three different systems: a simulation of a qubit whose dynamics are purely Markovian, a simulation of a driven qubit coupled to stochastic noise produced by an Ornstein-Uhlenbeck process, and trapped-ion experimental data of a driven qubit whose noise environment is not characterized ahead of time. Our technique is able to learn the generators of time evolution, or the NMZ operators, in all three cases and can learn the timescale in which the qubit dynamics can no longer be accurately described by a purely Markovian model. Our technique complements existing quantum gate characterization methods such as gate set tomography by explicitly capturing non-Markovianity in the gate generator, thus allowing for more thorough diagnosis of noise sources.