Deep Reinforcement Learning for Efficient Measurement of Quantum Devices

TL;DR

This paper introduces a deep reinforcement learning method that automates the measurement of quantum devices, specifically identifying bias triangles in double quantum dots efficiently, reducing measurement time significantly.

Contribution

It presents a novel deep reinforcement learning approach using dueling deep Q-networks for automated quantum device measurement, enabling rapid identification of transport features.

Findings

Bias triangles identified in under 30 minutes, sometimes as fast as 1 minute.

The method is adaptable to various quantum devices and features.

Demonstrates the utility of deep reinforcement learning in quantum measurement automation.

Abstract

Deep reinforcement learning is an emerging machine learning approach which can teach a computer to learn from their actions and rewards similar to the way humans learn from experience. It offers many advantages in automating decision processes to navigate large parameter spaces. This paper proposes a novel approach to the efficient measurement of quantum devices based on deep reinforcement learning. We focus on double quantum dot devices, demonstrating the fully automatic identification of specific transport features called bias triangles. Measurements targeting these features are difficult to automate, since bias triangles are found in otherwise featureless regions of the parameter space. Our algorithm identifies bias triangles in a mean time of less than 30 minutes, and sometimes as little as 1 minute. This approach, based on dueling deep Q-networks, can be adapted to a broad range of devices and target transport features. This is a crucial demonstration of the utility of deep reinforcement learning for decision making in the measurement and operation of quantum devices.