Enhancing qubit readout with Bayesian Learning

TL;DR

This paper presents a Bayesian inference-based readout scheme for quantum bits that improves accuracy and reduces errors by accounting for system imperfections, demonstrated on superconducting qubits with promising results.

Contribution

The paper introduces a Bayesian learning approach for qubit readout that enhances measurement accuracy by modeling detector response and system noise.

Findings

Significant reduction in readout error rates.

Effective for single and multi-qubit states.

Applicable to quantum algorithms like Bernstein-Vazirani.

Abstract

We introduce an efficient and accurate readout measurement scheme for single and multi-qubit states. Our method uses Bayesian inference to build an assignment probability distribution for each qubit state based on a reference characterization of the detector response functions. This allows us to account for system imperfections and thermal noise within the assignment of the computational basis. We benchmark our protocol on a quantum device with five superconducting qubits, testing initial state preparation for single and two-qubit states and an application of the Bernstein-Vazirani algorithm executed on five qubits. Our method shows a substantial reduction of the readout error and promises advantages for near-term and future quantum devices.