Efficient Large-Scale Quantum Optimization via Counterdiabatic Ansatz

TL;DR

This paper compares counterdiabatic and standard QAOA for large-scale quantum optimization, showing that the counterdiabatic approach offers significant advantages in circuit complexity, robustness, and performance for sufficiently large problems.

Contribution

It provides a comprehensive comparison of DC-QAOA(NC) and QAOA, demonstrating the conditions under which counterdiabatic methods outperform standard approaches and introducing an efficient training method.

Findings

DC-QAOA(NC) reduces circuit complexity for problems with more than 16 qubits.

DC-QAOA(NC) maintains higher fidelity under noise for large problems.

One-layer DC-QAOA(NC) outperforms three-layer QAOA with the same CNOT count.

Abstract

Quantum Approximate Optimization Algorithm (QAOA) is one of the fundamental variational quantum algorithms, while a version of QAOA that includes counterdiabatic driving, termed Digitized Counterdiabatic QAOA (DC-QAOA), is generally considered to outperform QAOA for all system sizes when the circuit depth for the two algorithms are held equal. Nevertheless, DC-QAOA introduces more CNOT gates per layer, so the overall circuit complexity is a tradeoff between the number of CNOT gates per layer and the circuit depth and must be carefully assessed. In this paper, we conduct a comprehensive comparison of DC-QAOA and QAOA on MaxCut problem with the total number of CNOT gates held equal, and we focus on one implementation of counterdiabatic terms using nested commutators in DC-QAOA, termed as DC-QAOA(NC). We have found that DC-QAOA(NC) reduces the overall circuit complexity as compared to QAOA only for sufficiently large problems, and for MaxCut problem the number of qubits must exceed 16 for DC-QAOA(NC) to outperform QAOA. Additionally, we benchmark DC-QAOA(NC) against QAOA on the Sherrington-Kirkpatrick model under realistic noise conditions, finding that DC-QAOA(NC) exhibits significantly improved robustness compared to QAOA, maintaining higher fidelity as the problem size scales. Notably, in a direct comparison between one-layer DC-QAOA(NC) and three-layer QAOA where both use the same number of CNOT gates, we identify an exponential performance advantage for DC-QAOA(NC), further signifying its suitability for large-scale quantum optimization tasks. Moreover, based on our finding, we have devised an instance-sequential training method for DC-QAOA(NC) circuits, which, compared to traditional methods, offers performance improvement while using even fewer quantum resources.