# Variational Quantum Eigensolver with Fewer Qubits

## TL;DR

This paper introduces a qubit-efficient variational quantum eigensolver that uses fewer qubits to study ground states of quantum many-body systems, enabling high-fidelity results on near-term quantum hardware.

## Contribution

It presents a novel tensor network approach with symmetry-preserving circuits that reduces qubit requirements and enhances training efficiency for quantum simulations.

## Key findings

- Achieved over 97% fidelity in ground state estimation with only 6 qubits.
- Demonstrated the ability to measure long-range correlations after optimization.
- Showed that symmetry-respecting circuits improve training speed and mitigate gradient issues.

## Abstract

We propose a qubit efficient scheme to study ground state properties of quantum many-body systems on near-term noisy intermediate scale quantum computers. One can obtain a tensor network representation of the ground state using a number of qubits smaller than the physical degrees of freedom. By increasing the qubits number, one can exponentially increase the bond dimension of the tensor network variational ansatz on a quantum computer. Moreover, we construct circuits blocks which respect U(1) and SU(2) symmetries of the physical system and show that they can significantly speed up the training process and alleviate the gradient vanishing problem. To demonstrate the feasibility of the qubit efficient variational quantum eigensolver in a practical setting, we perform first principle classical simulation of differentiable programming of the circuits. Using only $6$ qubits one can obtain the ground state of a $4\times 4$ square lattice frustrated Heisenberg model with fidelity over 97%. Arbitrarily long ranged correlations can also be measured on the same circuit after variational optimization.