Learning error suppression strategies for dynamic quantum circuits

TL;DR

This paper introduces an empirical learning framework to optimize error suppression in dynamic quantum circuits, significantly reducing error rates and improving fidelity for complex quantum operations.

Contribution

It presents a novel empirical approach to optimize dynamical decoupling sequences tailored for dynamic circuits, outperforming traditional methods.

Findings

Achieved a three-fold reduction in average error rates using learned sequences.

Demonstrated high fidelity in quantum Fourier transform with measurement on 20-qubit chains.

Enabled high signal-to-noise quantum Fourier transform after preparing a 10-qubit entangled state.

Abstract

Dynamic quantum circuits integrate unitary evolution with mid-circuit measurement and feedforward, enabling conditional operations essential for efficient quantum algorithms and foundational for fault-tolerant quantum computation. However, such operations introduce measurement-induced errors and control constraints that are not addressed by conventional error-suppression techniques. Here, we introduce an empirical learning framework that optimizes dynamical decoupling (DD) sequences for dynamic circuits at the level of circuit subintervals and qubit subregisters. Applying empirically learned DD sequences, we achieve a three-fold reduction in average dynamic circuit error rates as measured via randomized benchmarking. We apply the learned strategies to the dynamic circuit implementation of the quantum Fourier transform with measurement (QFT+M), demonstrating nontrivial process fidelity on connected chains of up to 20 qubits. Applying the resulting enhancement, we perform a high signal-to-noise QFT immediately following the preparation of a 10-qubit entangled state. Our results demonstrate that empirically optimized DD systematically outperforms theoretically derived sequences for dynamic circuits, establishing it as an efficient approach for error suppression in dynamic quantum circuits, with direct relevance to applications requiring measurement and feedback such as quantum error correction.