Hybrid Real-Imaginary Time Evolution for Low-Depth Hamiltonian Simulation in Quantum Optimization

TL;DR

This paper introduces HAVQDS, a hybrid quantum simulation method combining real-time and imaginary-time evolution, significantly improving low-depth Hamiltonian simulation for quantum optimization tasks.

Contribution

HAVQDS is a novel hybrid adaptive variational approach that enhances quantum optimization efficiency by integrating real-time and imaginary-time evolution techniques.

Findings

HAVQDS outperforms adiabatic and CD methods in approximation ratios for the SK model.

It reduces CNOT counts by 1-2 orders of magnitude, enabling high-fidelity optimization.

Applicable to 6-14 qubits, demonstrating scalability and effectiveness.

Abstract

Counterdiabatic (CD) driving is a powerful technique for accelerating adiabatic quantum computing. However, it becomes self-limiting in complex optimizations like the Sherrington-Kirkpatrick model: long evolution times $T$ needed to traverse crossings force the CD strength to scale as $1/T$, causing it to vanish before convergence and wasting the quantum resources invested in its implementation. We break this trade-off with a Hybrid adaptive variational quantum dynamics simulation (HAVQDS). HAVQDS combines adaptive real-time evolution for circuit compression with imaginary-time steps that suppress excitations at no extra gate cost. For the SK model (6--14 qubits), HAVQDS achieves higher approximation ratios than adiabatic or CD approaches, while reducing CNOT counts by 1--2 orders of magnitude, enabling high-fidelity quantum optimization.