Controlling Unknown Quantum States via Data-Driven State Representations

TL;DR

This paper introduces a machine-learning approach that uses limited measurement data to construct state representations and control unknown quantum states effectively, advancing quantum control techniques.

Contribution

It presents a novel data-driven machine-learning algorithm for controlling unknown quantum states without prior state knowledge.

Findings

Achieves accurate control of unknown many-body quantum states

Effective control of non-Gaussian continuous-variable states

Uses limited measurement data for state representation

Abstract

Accurate control of quantum states is crucial for quantum computing and other quantum technologies. In the basic scenario, the task is to steer a quantum system towards a target state through a sequence of control operations. Determining the appropriate operations, however, generally requires information about the initial state of the system. When the initial state is not {\em a priori} known, gathering this information is generally challenging for quantum systems of increasing size. To address this problem, we develop a machine-learning algorithm that uses a small amount of measurement data to construct a representation of the system's state. The algorithm compares this data-driven representation with the representation of the target state, and uses reinforcement learning to output the appropriate control operations.We illustrate the effectiveness of the algorithm showing that it achieves accurate control of unknown many-body quantum states and non-Gaussian continuous-variable states using data from a limited set of quantum measurements.