Quantum search by measurements assisted by pre-trained tensor network states for Hamiltonian simulations

TL;DR

This paper introduces a quantum algorithm that leverages pre-trained tensor network states and measurement techniques to efficiently simulate many-body quantum systems and identify their ground states, with potential applications in quantum physics and chemistry.

Contribution

The paper proposes a novel quantum algorithm combining tensor networks and measurement-based methods for Hamiltonian simulations, enhancing efficiency and applicability.

Findings

Algorithm successfully simulates quantum spin systems.

Resource estimation indicates feasible implementation.

Potential for improved electronic structure calculations.

Abstract

We present a quantum algorithm for simulating complex many-body systems and finding their ground states, combining the use of tensor networks and density matrix renormalization group (DMRG) techniques. The algorithm is based on von Neumann's measurement prescription, which serves as a conceptual building block for quantum phase estimation. We describe the implementation and simulation of the algorithm, including the estimation of resources required and the use of matrix product operators (MPOs) to represent the Hamiltonian. We highlight the potential applications of the algorithm in simulating quantum spin systems and electronic structure problems.