Towards adiabatic quantum computing using compressed quantum circuits

TL;DR

This paper introduces tensor network algorithms to optimize quantum circuits for adiabatic quantum computing, incorporating counterdiabatic driving to improve ground state preparation efficiency and outperform traditional Trotter formulas.

Contribution

It presents a novel classical optimization method for fixed-depth quantum circuits that effectively integrates adiabatic evolution and counterdiabatic driving.

Findings

Optimized circuits outperform Trotter product formulas in ground state preparation.

The approach reduces circuit depth while maintaining accuracy.

Potential applications in combinatorial optimization.

Abstract

We describe tensor network algorithms to optimize quantum circuits for adiabatic quantum computing. To suppress diabatic transitions, we include counterdiabatic driving in the optimization and utilize variational matrix product operators to represent adiabatic gauge potentials. Traditionally, Trotter product formulas are used to turn adiabatic time evolution into quantum circuits and the addition of counterdiabatic driving increases the circuit depth per time step. Instead, we classically optimize a parameterized quantum circuit of fixed depth to simultaneously capture adiabatic evolution together with counterdiabatic driving over many time steps. The methods are applied to the ground state preparation of quantum Ising chains with transverse and longitudinal fields. We show that the classically optimized circuits can significantly outperform Trotter product formulas. Additionally, we discuss how the approach can be used for combinatorial optimization.