MPS-JuliQAOA: User-friendly, Scalable MPS-based Simulation for Quantum Optimization

TL;DR

MPS-JuliQAOA is an open-source, user-friendly simulator leveraging MPS techniques in Julia to efficiently simulate large-scale QAOA for optimization problems, with built-in parameter optimization features.

Contribution

The paper introduces MPS-JuliQAOA, a scalable, easy-to-use MPS-based simulator for QAOA that handles large qubit systems and simplifies parameter finding.

Findings

Scales to 512 qubits and 20 rounds on standard benchmarks.

Provides built-in parameter optimization capabilities.

Demonstrates scalability and accuracy tradeoffs.

Abstract

We present the MPS-JuliQAOA simulator, a user-friendly, open-source tool to simulate the Quantum Approximate Optimization Algorithm (QAOA) of any optimization problem that can be expressed as diagonal Hamiltonian. By leveraging Julia-language constructs and the ITensor package to implement a Matrix Product State (MPS) approach to simulating QAOA, MPS-Juli-QAOA effortlessly scales to 512 qubits and 20 simulation rounds on the standard de-facto benchmark 3-regular MaxCut QAOA problem. MPS-JuliQAOA also has built-in parameter finding capabilities, which is a crucial performance aspect of QAOA. We illustrate through examples that the user does not need to know MPS principles or complex automatic differentiation techniques to use MPS-JuliQAOA. We study the scalability of our tool with respect to runtime, memory usage and accuracy tradeoffs. Code available at https://github.com/lanl/JuliQAOA.jl/tree/mps.