Basis Adaptive Algorithm for Quantum Many-Body Systems on Quantum Computers

TL;DR

This paper introduces a basis adaptive hybrid quantum-classical algorithm that efficiently finds ground states of quantum many-body systems, outperforming existing methods on near-term quantum hardware.

Contribution

It presents a novel symmetry-filtered, real-time sampling approach that improves accuracy and efficiency over prior algorithms like VQE and SKQD.

Findings

Achieves sub-percent accuracy in ground-state energies for 24-qubit systems.

Outperforms the Sampling Krylov Quantum Diagonalization (SKQD) method.

Demonstrates robustness and efficiency on IBM's quantum hardware.

Abstract

A new basis adaptive algorithm for hybrid quantum-classical platforms is introduced to efficiently find the ground-state (gs) properties of quantum many-body systems. The method addresses limitations of many algorithms, such as Variational Quantum Eigensolver (VQE) and Quantum Phase Estimation (QPE) etc by using shallow Trotterized circuits for short real-time evolution on a quantum processor. The sampled basis is then symmetry-filtered by using various symmetries of the Hamiltonian which is then classically diagonalized in the reduced Hilbert space. We benchmark this approach on the spin-1/2 XXZ chain up to 24 qubits using the IBM Heron processor. The algorithm achieves sub-percent accuracy in ground-state energies across various anisotropy regimes. Crucially, it outperforms the Sampling Krylov Quantum Diagonalization (SKQD) method, demonstrating a substantially lower energy error for comparable reduced-space dimensions. This work validates symmetry-filtered, real-time sampling as a robust and efficient path for studying correlated quantum systems on current near-term hardware.