Exploiting subspace constraints and ab initio variational methods for quantum chemistry

TL;DR

This paper advances quantum chemistry computations by using variational quantum algorithms with subspace constraints and ab initio circuit synthesis, outperforming traditional methods on larger molecular problems.

Contribution

It introduces a novel subspace constraint technique for variational quantum circuits, enhancing performance and circuit compactness in quantum chemistry simulations.

Findings

Outperforms traditional ansätze like coupled-cluster for larger systems

Subspace constraints significantly improve circuit efficiency

Demonstrates effectiveness on systems up to 14 qubits

Abstract

Variational methods offer a highly promising route to exploiting quantum computers for chemistry tasks. Here we employ methods described in a sister paper to the present report, entitled ab initio machine synthesis of quantum circuits, in order to solve problems using adaptively evolving quantum circuits. Consistent with prior authors we find that this approach can outperform human-designed circuits such as the coupled-cluster or hardware-efficient ans\"atze, and we make comparisons for larger instances up to 14 qubits. Moreover we introduce a novel approach to constraining the circuit evolution in the physically relevant subspace, finding that this greatly improves performance and compactness of the circuits. We consider both static and dynamics properties of molecular systems. The emulation environments used is QuESTlink; all resources are open source and linked from this paper.