MPS-JuliQAOA: User-friendly, Scalable MPS-based Simulation for Quantum Optimization
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arXiv
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| Hauptverfasser: | , , , |
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| Format: | Preprint |
| Veröffentlicht: |
2025
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| _version_ | 1866916887031447552 |
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| author | Feeney, Sean Tate, Reuben Golden, John Eidenbenz, Stephan |
| author_facet | Feeney, Sean Tate, Reuben Golden, John Eidenbenz, Stephan |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_05883 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | MPS-JuliQAOA: User-friendly, Scalable MPS-based Simulation for Quantum Optimization Feeney, Sean Tate, Reuben Golden, John Eidenbenz, Stephan Quantum Physics Emerging Technologies Software Engineering 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. |
| title | MPS-JuliQAOA: User-friendly, Scalable MPS-based Simulation for Quantum Optimization |
| topic | Quantum Physics Emerging Technologies Software Engineering |
| url | https://arxiv.org/abs/2508.05883 |