Designing Minimalistic Variational Quantum Ansatz Inspired by Algorithmic Cooling

TL;DR

This paper presents a minimalistic variational quantum ansatz inspired by algorithmic cooling, enabling efficient quantum state preparation and problem-solving on NISQ devices with improved accuracy and applicability to quantum many-body physics.

Contribution

Introduces the Heat Exchange ansatz inspired by algorithmic cooling, demonstrating superior performance and scalability for quantum optimization and ground state calculations on noisy quantum hardware.

Findings

Achieves better approximation ratios for Maxcut problem.

Sub-1% error in ground-state energy of 1D Heisenberg chain.

Successfully simulates edge effects in impure spin chains.

Abstract

This study introduces a novel minimalistic variational quantum ansatz inspired by algorithmic cooling principles. The proposed Heat Exchange algorithmic cooling ansatz (HE ansatz) facilitates efficient population redistribution without requiring bath resets, simplifying implementation on noisy intermediate-scale quantum (NISQ) devices. The HE ansatz achieves superior approximation ratios with the complete network \textsc{Maxcut} optimization problem compared to the conventional Hardware efficient and QAOA ansatz. We also proposed a new variational algorithm that utilize HE ansatz to compute the ground state of impure dissipative-system variational quantum eigensolver (dVQE) which achieved a sub-$1\%$ error in ground-state energy calculations of the 1D Heisenberg chain with impurity and successfully simulates the edge effect of impure spin chain, highlighting its potential for applications in quantum many-body physics. These results underscore the compatibility of the ansatz with hardware-efficient implementations, offering a scalable approach for solving complex quantum problems in disordered and open quantum systems.