Exhaustive search for optimal molecular geometries using imaginary-time evolution on a quantum computer

TL;DR

This paper introduces a quantum computing method using imaginary-time evolution to find optimal molecular geometries, demonstrating potential for scalable quantum chemistry calculations with reduced circuit depth.

Contribution

The paper presents a nonvariational quantum scheme for molecular geometry optimization using probabilistic imaginary-time evolution, with scalable circuit depth and applicability to NISQ devices.

Findings

Circuit depth scales as O(n_e^2 poly(log n_e))

Numerical simulations validate the scheme's effectiveness

Potential for quantum advantage in molecular geometry optimization

Abstract

We propose a nonvariational scheme for geometry optimization of molecules for the first-quantized eigensolver, a recently proposed framework for quantum chemistry using the probabilistic imaginary-time evolution (PITE) on a quantum computer. While the electrons in a molecule are treated in the scheme as quantum mechanical particles, the nuclei are treated as classical point charges. We encode both electronic states and candidate molecular geometries as a superposition of many-qubit states, leading to quantum advantage. The histogram formed by outcomes of repeated measurements gives the global minimum of the energy surface. We demonstrate that the circuit depth scales as O (n_e^2 poly(log n_e)) for the electron number n_e, which can be reduced to O (n_e poly(log n_e)) if extra O (n_e log n_e) qubits are available. We corroborate the scheme via numerical simulations. The new efficient scheme will be helpful for achieving scalability of practical quantum chemistry on quantum computers. As a special case of the scheme, a classical system composed only of charged particles is admitted. We also examine the scheme adapted to variational calculations that prioritize saving circuit depths for noisy intermediate-scale quantum (NISQ) devices.