Many-Fermion Simulation from the Contracted Quantum Eigensolver without Fermionic Encoding of the Wave Function

TL;DR

This paper introduces a generalized contracted quantum eigensolver (CQE) that avoids fermionic encoding, simplifying quantum simulations of many-fermion systems and demonstrating comparable accuracy with potential computational advantages.

Contribution

The authors develop a fermionic encoding-free CQE method, enabling more efficient quantum simulations of many-fermion systems without sacrificing accuracy.

Findings

Unencoded CQE achieves similar convergence to encoded methods for ground-state energies.

Unencoded approach offers advantages in state preparation and tomography.

Method successfully applied to molecules like HF, O2 dissociation, and hydrogen chains.

Abstract

Quantum computers potentially have an exponential advantage over classical computers for the quantum simulation of many-fermion quantum systems. Nonetheless, fermions are more expensive to simulate than bosons due to the fermionic encoding -- a mapping by which the qubits are encoded with fermion statistics. Here we generalize the contracted quantum eigensolver (CQE) to avoid fermionic encoding of the wave function. In contrast to the variational quantum eigensolver, the CQE solves for a many-fermion stationary state by minimizing the contraction (projection) of the Schr\"odinger equation onto two fermions. We avoid fermionic encoding of the wave function by contracting the Schr\"odinger equation onto an unencoded pair of particles. Solution of the resulting contracted equation by a series of unencoded two-body exponential transformations generates an unencoded wave function from which the energy and two-fermion reduced density matrix (2-RDM) can be computed. We apply the unencoded and the encoded CQE algorithms to the hydrogen fluoride molecule, the dissociation of oxygen O$_{2}$, and a series of hydrogen chains. Both algorithms show comparable convergence towards the exact ground-state energies and 2-RDMs, but the unencoded algorithm has computational advantages in terms of state preparation and tomography.