{"publication":"Physical Review Research","issue":"1","date_created":"2024-03-17T23:00:59Z","month":"03","publication_status":"published","license":"https://creativecommons.org/licenses/by/4.0/","publisher":"American Physical Society","external_id":{"arxiv":["2307.14427"]},"status":"public","article_type":"original","_id":"15122","file":[{"date_created":"2024-03-19T07:16:38Z","access_level":"open_access","creator":"dernst","date_updated":"2024-03-19T07:16:38Z","success":1,"file_size":2777593,"content_type":"application/pdf","file_id":"15123","relation":"main_file","checksum":"274c9f1b15b3547a10a03f39e4ccc582","file_name":"2024_PhysicalReviewResearch_Sack.pdf"}],"file_date_updated":"2024-03-19T07:16:38Z","acknowledgement":"S.H.S. acknowledges support from the IBM Ph.D. fellowship 2022 in quantum computing. The authors also thank M. Serbyn, R. Kueng, R. A. Medina, and S. Woerner for fruitful discussions.","department":[{"_id":"MaSe"}],"doi":"10.1103/PhysRevResearch.6.013223","oa_version":"Published Version","project":[{"_id":"bd660c93-d553-11ed-ba76-fb0fb6f49c0d","name":"Quantum_Quantum Circuits and Software_Variational quantum algorithms on NISQ devices"}],"user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","date_updated":"2024-03-19T07:24:03Z","type":"journal_article","day":"01","language":[{"iso":"eng"}],"author":[{"full_name":"Sack, Stefan","id":"dd622248-f6e0-11ea-865d-ce382a1c81a5","first_name":"Stefan","last_name":"Sack","orcid":"0000-0001-5400-8508"},{"last_name":"Egger","first_name":"Daniel J.","full_name":"Egger, Daniel J."}],"oa":1,"abstract":[{"lang":"eng","text":"Quantum computers are increasing in size and quality but are still very noisy. Error mitigation extends the size of the quantum circuits that noisy devices can meaningfully execute. However, state-of-the-art error mitigation methods are hard to implement and the limited qubit connectivity in superconducting qubit devices restricts most applications to the hardware's native topology. Here we show a quantum approximate optimization algorithm (QAOA) on nonplanar random regular graphs with up to 40 nodes enabled by a machine learning-based error mitigation. We use a swap network with careful decision-variable-to-qubit mapping and a feed-forward neural network to optimize a depth-two QAOA on up to 40 qubits. We observe a meaningful parameter optimization for the largest graph which requires running quantum circuits with 958 two-qubit gates. Our paper emphasizes the need to mitigate samples, and not only expectation values, in quantum approximate optimization. These results are a step towards executing quantum approximate optimization at a scale that is not classically simulable. Reaching such system sizes is key to properly understanding the true potential of heuristic algorithms like QAOA."}],"year":"2024","quality_controlled":"1","volume":6,"date_published":"2024-03-01T00:00:00Z","scopus_import":"1","article_processing_charge":"Yes","title":"Large-scale quantum approximate optimization on nonplanar graphs with machine learning noise mitigation","intvolume":"         6","citation":{"apa":"Sack, S., &#38; Egger, D. J. (2024). Large-scale quantum approximate optimization on nonplanar graphs with machine learning noise mitigation. <i>Physical Review Research</i>. American Physical Society. <a href=\"https://doi.org/10.1103/PhysRevResearch.6.013223\">https://doi.org/10.1103/PhysRevResearch.6.013223</a>","ama":"Sack S, Egger DJ. Large-scale quantum approximate optimization on nonplanar graphs with machine learning noise mitigation. <i>Physical Review Research</i>. 2024;6(1). doi:<a href=\"https://doi.org/10.1103/PhysRevResearch.6.013223\">10.1103/PhysRevResearch.6.013223</a>","chicago":"Sack, Stefan, and Daniel J. Egger. “Large-Scale Quantum Approximate Optimization on Nonplanar Graphs with Machine Learning Noise Mitigation.” <i>Physical Review Research</i>. American Physical Society, 2024. <a href=\"https://doi.org/10.1103/PhysRevResearch.6.013223\">https://doi.org/10.1103/PhysRevResearch.6.013223</a>.","short":"S. Sack, D.J. Egger, Physical Review Research 6 (2024).","ieee":"S. Sack and D. J. Egger, “Large-scale quantum approximate optimization on nonplanar graphs with machine learning noise mitigation,” <i>Physical Review Research</i>, vol. 6, no. 1. American Physical Society, 2024.","ista":"Sack S, Egger DJ. 2024. Large-scale quantum approximate optimization on nonplanar graphs with machine learning noise mitigation. Physical Review Research. 6(1), 013223.","mla":"Sack, Stefan, and Daniel J. Egger. “Large-Scale Quantum Approximate Optimization on Nonplanar Graphs with Machine Learning Noise Mitigation.” <i>Physical Review Research</i>, vol. 6, no. 1, 013223, American Physical Society, 2024, doi:<a href=\"https://doi.org/10.1103/PhysRevResearch.6.013223\">10.1103/PhysRevResearch.6.013223</a>."},"ddc":["530"],"publication_identifier":{"issn":["2643-1564"]},"has_accepted_license":"1","tmp":{"name":"Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)","legal_code_url":"https://creativecommons.org/licenses/by/4.0/legalcode","short":"CC BY (4.0)","image":"/images/cc_by.png"},"article_number":"013223"}