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arxiv: 2508.05883 · v1 · pith:65EQFCZEnew · submitted 2025-08-07 · 🪐 quant-ph · cs.ET· cs.SE

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

Sean Feeney , Reuben Tate , John Golden , Stephan Eidenbenz This is my paper
classification 🪐 quant-ph cs.ETcs.SE
keywords mps-juliqaoaqaoaoptimizationproblemquantumsimulationtooluser-friendly
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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.

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