Robust entanglement renormalization on a noisy quantum computer

TL;DR

This paper introduces a MERA-like tensor network method for studying quantum many-body systems on noisy quantum computers, demonstrating stability to noise and providing bounds on ground state energy with fewer qubits.

Contribution

It proposes a noise-stable tensor network contraction method for quantum computers that efficiently estimates ground state energies of strongly interacting systems.

Findings

Contraction outcome is stable to noise due to positivity of operator scaling dimensions.

The method provides a variational upper bound on ground state energy.

A scaling law relates qubit resources to the accuracy of ground state approximation.

Abstract

A method to study strongly interacting quantum many-body systems at and away from criticality is proposed. The method is based on a MERA-like tensor network that can be efficiently and reliably contracted on a noisy quantum computer using a number of qubits that is much smaller than the system size. We prove that the outcome of the contraction is stable to noise and that the estimated energy upper bounds the ground state energy. The stability, which we numerically substantiate, follows from the positivity of operator scaling dimensions under renormalization group flow. The variational upper bound follows from a particular assignment of physical qubits to different locations of the tensor network plus the assumption that the noise model is local. We postulate a scaling law for how well the tensor network can approximate ground states of lattice regulated conformal field theories in d spatial dimensions and provide evidence for the postulate. Under this postulate, a $O(\log^{d}(1/\delta))$-qubit quantum computer can prepare a valid quantum-mechanical state with energy density $\delta$ above the ground state. In the presence of noise, $\delta = O(\epsilon \log^{d+1}(1/\epsilon))$ can be achieved, where $\epsilon$ is the noise strength.