Enhanced Qubit Readout via Reinforcement Learning

TL;DR

This paper uses reinforcement learning to optimize qubit measurement in superconducting quantum devices, achieving faster, more accurate readout with robust waveforms that outperform standard methods.

Contribution

It introduces a model-free RL approach for quantum measurement optimization, producing high-performance, robust waveforms with minimal computational overhead.

Findings

Achieved an assignment error of (4.6 ± 0.4)×10^{-3}

Readout and reset are nearly three times faster than default methods

Learned waveforms are robust and analytically interpretable

Abstract

Measurement is an essential component of robust and practical quantum computation. For superconducting qubits, the measurement process involves the effective manipulation of the joint qubit-resonator dynamics, and it should ideally provide the highest quality for qubit state discrimination with the shortest readout pulse and resonator reset time. Here, we harness model-free reinforcement learning (RL), together with a tailored training environment, to achieve this multi-pronged optimization task. Using the IBM quantum device, we demonstrate that the pulse obtained by the RL agent not only successfully achieves state-of-the-art performance, with an assignment error of $(4.6 \pm 0.4)\times10^{-3}$, but also executes the readout and the subsequent resonator reset almost three times faster than the system's default process. Furthermore, the learned waveforms are robust against realistic parameter drifts and follow a generalized analytical form, making them readily implementable in practice with no significant computation overhead. Our results provide an effective readout strategy to boost the performance of superconducting quantum processors and demonstrate the prowess of RL in providing optimal and experimentally informed solutions for complex quantum information processing tasks.