Learning How to Dynamically Decouple

Abstract

Current quantum computers suffer from noise that stems from interactions between the quantum system that constitutes the quantum device and its environment. These interactions can be suppressed through dynamical decoupling to reduce computational errors. However, the performance of dynamical decoupling depends on the type of the system-environment interactions that are present, which often lack an accurate model in quantum devices. We show that the performance of dynamical decoupling can be improved by optimizing its rotational gates to tailor them to the quantum hardware. We find that compared to canonical decoupling sequences, such as CPMG, XY4, and UR6, the optimized dynamical decoupling sequences yield the best performance in suppressing noise in superconducting qubits. Our work thus enhances existing error suppression methods which helps increase circuit depth and result quality on noisy hardware.

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Cited by in corpus (9)

• Artificial Intelligence for Quantum Computing
• Empirical learning of dynamical decoupling on quantum processors
• Impact of time-retarded noise on dynamical decoupling schemes for qubits
• Realization of Constant-Depth Fan-Out with Real-Time Feedforward on a Superconducting Quantum Processor
• Virtual Z gates and symmetric gate compilation
• Demonstration of High-Fidelity Entangled Logical Qubits using Transmons
• Low-rank optimal control of quantum devices
• Color it, Code it, Cancel it: k-local dynamical decoupling from classical additive codes
• Controlling quantum chaos via Parrondo strategies on noisy intermediate-scale quantum hardware