Benchmarking hybrid digitized-counterdiabatic quantum optimization

Abstract

Hybrid digitized-counterdiabatic quantum computing (DCQC) is a promising approach for leveraging the capabilities of near-term quantum computers, utilizing parameterized quantum circuits designed with counterdiabatic protocols. However, the classical aspect of this approach has received limited attention. In this study, we systematically analyze the convergence behavior and solution quality of various classical optimizers when used in conjunction with the digitized-counterdiabatic approach. We demonstrate the effectiveness of this hybrid algorithm by comparing its performance to the traditional QAOA on systems containing up to 28 qubits. Furthermore, we employ principal component analysis to investigate the cost landscape and explore the crucial influence of parameterization on the performance of the counterdiabatic ansatz. Our findings indicate that fewer iterations are required when local cost landscape minima are present, and the SPSA-based BFGS optimizer emerges as a standout choice for the hybrid DCQC paradigm.

arXiv comment9 pages, 5 figures

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Cited by in corpus (5)

• Exploring Ground States of Fermi-Hubbard Model on Honeycomb Lattices with Counterdiabaticity
• Reversing adiabatic state preparation in few-level quantum systems
• Variational quantum compiling for three-qubit gates design in quantum dots
• Digitized Counter-Diabatic Quantum Optimization for Bin Packing Problem
• Adiabatic Dynamics of Entanglement