Measurement-based Dynamical Decoupling for Fidelity Preservation on Large-scale Quantum Processors

TL;DR

This paper introduces a measurement-based dynamical decoupling protocol that adaptively controls quantum systems to significantly improve fidelity and success probabilities in large-scale quantum processors, demonstrating scalability and effectiveness.

Contribution

The paper presents a novel measurement-based DD method that scales linearly with system size and achieves maximal entanglement fidelity under certain noise conditions, outperforming traditional DD schemes.

Findings

Up to 450-fold improvement in quantum Fourier transform success probability.

Enhanced accuracy in ground-state energy estimation for large qubits.

Scalable control protocol effective for large quantum processors.

Abstract

Dynamical decoupling (DD) is a key technique for suppressing decoherence and preserving the performance of quantum algorithms. We introduce a measurement-based DD (MDD) protocol that determines control unitary gates from partial measurements of noisy subsystems, with measurement overhead scaling linearly with the number of subsystems. We prove that, under local energy relaxation and dephasing noise, MDD achieves the maximum entanglement fidelity attainable by any DD scheme based on bang-bang operations to first order in evolution time. On the IBM Eagle processor, MDD achieved up to a $450$-fold improvement in the success probability of a $14$-qubit quantum Fourier transform, and improved the accuracy of ground-state energy estimation for $N_2$ in the $56$-qubit sample-based quantum diagonalization compared with the standard XX-pulse DD. These results establish MDD as a scalable and effective approach for suppressing decoherence in large-scale quantum algorithms.