Efficient tensor network simulation of IBM's largest quantum processors

TL;DR

This paper demonstrates that 2D tensor network methods, specifically gPEPS, can efficiently simulate large IBM quantum processors with over 1000 qubits, providing benchmarks and extending previous experimental results.

Contribution

It introduces the use of graph-based PEPS for simulating large-scale quantum processors, achieving high accuracy with low computational resources for systems up to 1121 qubits.

Findings

Successfully simulated IBM's largest quantum processors with over 1000 qubits.

Achieved accurate simulation of a complex quantum many-body system over extended evolution times.

Provided benchmarks for the performance of current IBM quantum hardware.

Abstract

We show how quantum-inspired 2d tensor networks can be used to efficiently and accurately simulate the largest quantum processors from IBM, namely Eagle (127 qubits), Osprey (433 qubits) and Condor (1121 qubits). We simulate the dynamics of a complex quantum many-body system -- specifically, the kicked Ising experiment considered recently by IBM in Nature 618, p. 500-505 (2023) -- using graph-based Projected Entangled Pair States (gPEPS), which was proposed by some of us in PRB 99, 195105 (2019). Our results show that simple tensor updates are already sufficient to achieve very large unprecedented accuracy with remarkably low computational resources for this model. Apart from simulating the original experiment for 127 qubits, we also extend our results to 433 and 1121 qubits, and for evolution times around 8 times longer, thus setting a benchmark for the newest IBM quantum machines. We also report accurate simulations for infinitely-many qubits. Our results show that gPEPS are a natural tool to efficiently simulate quantum computers with an underlying lattice-based qubit connectivity, such as all quantum processors based on superconducting qubits.