Effects of Dynamical Decoupling and Pulse-level Optimizations on IBM Quantum Computers

TL;DR

This paper evaluates dynamical decoupling and pulse-level optimizations on IBM quantum computers, showing their benefits vary by application type and providing guidelines for high-fidelity circuit design.

Contribution

It systematically compares DD sequences and pulse-level optimizations, and explores their combined effects on quantum algorithm performance on IBM hardware.

Findings

DD benefits are application-dependent

Combining DD with pulse optimization improves fidelity consistently

Guidelines for noise mitigation in quantum circuit design

Abstract

Currently available quantum computers are prone to errors. Circuit optimization and error mitigation methods are needed to design quantum circuits to achieve better fidelity when executed on NISQ hardware. Dynamical decoupling (DD) is generally used to suppress the decoherence error and different DD strategies have been proposed. Moreover, the circuit fidelity can be improved by pulse-level optimization, such as creating hardware-native pulse-efficient gates. This paper implements all the popular DD sequences and evaluates their performances on IBM quantum chips with different characteristics for various well-known quantum applications. Also, we investigate combining DD with pulse-level optimization method and apply them to QAOA to solve Max-Cut problem. Based on the experimental results, we found that DD can be a benefit for only certain types of quantum algorithms, while the combination of DD and pulse-level optimization methods always has a positive impact. Finally, we provide several guidelines for users to learn how to use these noise mitigation methods to build circuits for quantum applications with high fidelity on IBM quantum computers.