{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,2]],"date-time":"2026-04-02T16:00:30Z","timestamp":1775145630840,"version":"3.50.1"},"reference-count":43,"publisher":"Association for Computing Machinery (ACM)","issue":"2","license":[{"start":{"date-parts":[[2022,5,16]],"date-time":"2022-05-16T00:00:00Z","timestamp":1652659200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Transactions on Quantum Computing"],"published-print":{"date-parts":[[2022,6,30]]},"abstract":"<jats:p>\u2018\u2018Qubit routing\u201d refers to the task of modifying quantum circuits so that they satisfy the connectivity constraints of a target quantum computer. This involves inserting SWAP gates into the circuit so that the logical gates only ever occur between adjacent physical qubits. The goal is to minimise the circuit depth added by the SWAP gates.<\/jats:p>\n          <jats:p>\n            In this article, we propose a qubit routing procedure that uses a modified version of the deep Q-learning paradigm. The system is able to outperform the qubit routing procedures from two of the most advanced quantum compilers currently available (Qiskit and t\n            <jats:inline-formula content-type=\"math\/tex\">\n              <jats:tex-math notation=\"LaTeX\" version=\"MathJaX\">\\( | \\)<\/jats:tex-math>\n            <\/jats:inline-formula>\n            ket\n            <jats:inline-formula content-type=\"math\/tex\">\n              <jats:tex-math notation=\"LaTeX\" version=\"MathJaX\">\\( \\rangle \\)<\/jats:tex-math>\n            <\/jats:inline-formula>\n            ), on both random and realistic circuits, across a range of near-term architecture sizes (with up to 50 qubits).\n          <\/jats:p>","DOI":"10.1145\/3520434","type":"journal-article","created":{"date-parts":[[2022,3,25]],"date-time":"2022-03-25T13:06:52Z","timestamp":1648213612000},"page":"1-25","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":46,"title":["Using Reinforcement Learning to Perform Qubit Routing in Quantum Compilers"],"prefix":"10.1145","volume":"3","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2600-4312","authenticated-orcid":false,"given":"Matteo G.","family":"Pozzi","sequence":"first","affiliation":[{"name":"University of Cambridge Computer Laboratory, Cambridge"}]},{"given":"Steven J.","family":"Herbert","sequence":"additional","affiliation":[{"name":"University of Cambridge Computer Laboratory and Cambridge Quantum Computing, Cambridge"}]},{"given":"Akash","family":"Sengupta","sequence":"additional","affiliation":[{"name":"Department of Engineering, University of Cambridge, Trumpington St, Cambridge"}]},{"given":"Robert D.","family":"Mullins","sequence":"additional","affiliation":[{"name":"University of Cambridge Computer Laboratory, Cambridge"}]}],"member":"320","published-online":{"date-parts":[[2022,5,16]]},"reference":[{"key":"e_1_3_2_2_2","unstructured":"2018. IBM Q Devices and Simulators. Retrieved from https:\/\/web.archive.org\/web\/20181203023515\/https:\/\/www.research.ibm.com\/ibm-q\/technology\/devices\/."},{"key":"e_1_3_2_3_2","unstructured":"2018. Quantum Circuit Test Set (Zulehner). Retrieved May 2020 from https:\/\/iic.jku.at\/eda\/research\/ibm_qx_mapping\/."},{"key":"e_1_3_2_4_2","unstructured":"2020. Cirq Documentation (accessed for 0.8.0). Retrieved May 2020 from https:\/\/cirq.readthedocs.io\/en\/stable\/."},{"key":"e_1_3_2_5_2","unstructured":"2020. CQC - Our Technology (accessed for pytket 0.5.4). Retrieved May 2020 from https:\/\/cambridgequantum.com\/technology\/."},{"key":"e_1_3_2_6_2","unstructured":"2020. IBM Qiskit (accessed for 0.20.0). Retrieved May 2020 from https:\/\/qiskit.org."},{"key":"e_1_3_2_7_2","unstructured":"2020. Jandura\u2019s routing method (LookaheadSwap documentation). Retrieved from https:\/\/qiskit.org\/documentation\/stubs\/qiskit.transpiler.passes.LookaheadSwap.html#qiskit.transpiler.passes.LookaheadSwap."},{"key":"e_1_3_2_8_2","doi-asserted-by":"publisher","DOI":"10.23919\/DATE.2017.7927104"},{"key":"e_1_3_2_9_2","doi-asserted-by":"publisher","DOI":"10.1038\/s41586-019-1666-5"},{"key":"e_1_3_2_10_2","doi-asserted-by":"publisher","DOI":"10.4230\/LIPIcs.TQC.2019.3"},{"key":"e_1_3_2_11_2","unstructured":"Jerry M. Chow and Jay Gambetta. 2020. Quantum takes flight: Moving from laboratory demonstrations to building systems. Retrieved May 2020 from https:\/\/www.ibm.com\/blogs\/research\/2020\/01\/quantum-volume-32\/."},{"key":"e_1_3_2_12_2","unstructured":"Alfredo V. Clemente Humberto N. Castej\u00f3n and Arjun Chandra. 2017. Efficient parallel methods for deep reinforcement learning. arXiv:1705.04862. Retrieved from http:\/\/arxiv.org\/abs\/1705.04862."},{"key":"e_1_3_2_13_2","volume-title":"Leibniz International Proceedings in Informatics","author":"Cowtan Alexander","year":"2019","unstructured":"Alexander Cowtan, Silas Dilkes, Ross Duncan, Alexandre Krajenbrink, Will Simmons, and Seyon Sivarajah. 2019. On the qubit routing problem. In Leibniz International Proceedings in Informatics. Vol. 135. 5:1\u20135:32. https:\/\/drops.dagstuhl.de\/opus\/volltexte\/2019\/10397\/."},{"key":"e_1_3_2_14_2","unstructured":"Andrew W. Cross Lev S. Bishop John A. Smolin and Jay M. Gambetta. 2017. Open quantum assembly language. arXiv:1707.03429. Retrieved from http:\/\/arxiv.org\/abs\/1707.03429."},{"key":"e_1_3_2_15_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.micpro.2019.02.006"},{"key":"e_1_3_2_16_2","doi-asserted-by":"crossref","unstructured":"Pranav Gokhale Ali Javadi-Abhari Nathan Earnest Yunong Shi and Frederic T. Chong. 2020. Optimized quantum compilation for near-term algorithms with openpulse. arXiv:2004.11205. Retrieved from http:\/\/arxiv.org\/abs\/2004.11205.","DOI":"10.1109\/MICRO50266.2020.00027"},{"key":"e_1_3_2_17_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.cosrev.2018.11.002"},{"key":"e_1_3_2_18_2","doi-asserted-by":"publisher","DOI":"10.26421\/QIC20.9-10-5"},{"key":"e_1_3_2_19_2","unstructured":"Steven Herbert and Akash Sengupta. 2018. Using reinforcement learning to find efficient qubit routing policies for deployment in near-term quantum computers. arXiv:1812.11619. Retrieved from http:\/\/arxiv.org\/abs\/1812.11619."},{"key":"e_1_3_2_20_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.vlsi.2019.10.004"},{"key":"e_1_3_2_21_2","unstructured":"Alice Karnsund. 2019. DQN Tackling the Game of Candy Crush Friends Saga: A Reinforcement Learning Approach. Retrieved from http:\/\/www.diva-portal.org\/smash\/record.jsf?pid=diva2%3A1368129&dswid=-15%8."},{"key":"e_1_3_2_22_2","doi-asserted-by":"publisher","DOI":"10.22331\/q-2019-05-13-140"},{"key":"e_1_3_2_23_2","doi-asserted-by":"publisher","DOI":"10.1177\/0278364913495721"},{"key":"e_1_3_2_24_2","doi-asserted-by":"publisher","DOI":"10.1109\/T-C.1971.223159"},{"key":"e_1_3_2_25_2","doi-asserted-by":"publisher","DOI":"10.1145\/3297858.3304023"},{"key":"e_1_3_2_26_2","unstructured":"Margaret Martonosi and Martin Roetteler. 2019. Next steps in quantum computing: Computer science\u2019s role. arXiv:1903.10541. Retrieved from http:\/\/arxiv.org\/abs\/1903.10541."},{"key":"e_1_3_2_27_2","volume-title":"Proceedings of the 33rd International Conference on Machine Learning (ICML\u201916)","author":"Mnih Volodymyr","year":"2016","unstructured":"Volodymyr Mnih, Adria Puigdomenech Badia, Lehdi Mirza, Alex Graves, Tim Harley, Timothy P. Lillicrap, David Silver, and Koray Kavukcuoglu. 2016. Asynchronous methods for deep reinforcement learning. In Proceedings of the 33rd International Conference on Machine Learning (ICML\u201916), Vol. 48. 1928\u20131937. https:\/\/proceedings.mlr.press\/v48\/mniha16.html."},{"key":"e_1_3_2_28_2","unstructured":"Volodymyr Mnih Koray Kavukcuoglu David Silver Alex Graves Ioannis Antonoglou Daan Wierstra and Martin Riedmiller. 2013. Playing atari with deep reinforcement learning. arXiv:1312.5602. Retrieved from http:\/\/arxiv.org\/abs\/1312.5602."},{"key":"e_1_3_2_29_2","doi-asserted-by":"publisher","DOI":"10.1007\/s10676-017-9438-0"},{"key":"e_1_3_2_30_2","doi-asserted-by":"publisher","DOI":"10.1145\/3297858.3304075"},{"key":"e_1_3_2_31_2","unstructured":"Arun Nair Praveen Srinivasan Sam Blackwell Cagdas Alcicek Rory Fearon Alessandro De Maria Vedavyas Panneershelvam Mustafa Suleyman Charles Beattie Stig Petersen Shane Legg Volodymyr Mnih Koray Kavukcuoglu and David Silver. 2015. Massively parallel methods for deep reinforcement learning. arXiv:1507.04296. Retrieved from http:\/\/arxiv.org\/abs\/1507.04296."},{"key":"e_1_3_2_32_2","unstructured":"Matteo Pozzi. 2020. Qubit Routing with Reinforcement Learning (GitHub Repository). Retrieved November 2020 from https:\/\/github.com\/Macro206\/qubit-routing-with-rl."},{"key":"e_1_3_2_33_2","unstructured":"John Preskill. 2012. Quantum computing and the entanglement frontier. arXiv:1203.5813. Retrieved from http:\/\/arxiv.org\/abs\/1203.5813."},{"key":"e_1_3_2_34_2","doi-asserted-by":"crossref","DOI":"10.22331\/q-2018-08-06-79","article-title":"Quantum computing in the NISQ era and beyond","author":"Preskill John","year":"2018","unstructured":"John Preskill. 2018. Quantum computing in the NISQ era and beyond. Quantum 2 (2018), 79. https:\/\/arxiv.org\/abs\/1801.00862","journal-title":"Quantum"},{"key":"e_1_3_2_35_2","doi-asserted-by":"publisher","DOI":"10.1017\/S0269888900007724"},{"key":"e_1_3_2_36_2","volume-title":"Proceedings of the 4th International Conference on Learning Representations (ICLR\u201916), Conference Track Proceedings","author":"Schaul Tom","year":"2016","unstructured":"Tom Schaul, John Quan, Ioannis Antonoglou, and David Silver. 2016. Prioritized experience replay. In Proceedings of the 4th International Conference on Learning Representations (ICLR\u201916), Conference Track Proceedings. https:\/\/arxiv.org\/abs\/1511.05952."},{"key":"e_1_3_2_37_2","unstructured":"Eddie Schoute. 2019. Circuit Transformations for Quantum Architectures\u2014Compiler Code (GitLab Repository). Retrieved May 2020 from https:\/\/gitlab.umiacs.umd.edu\/amchilds\/arct\/-\/blob\/master\/arct\/compiler.py."},{"key":"e_1_3_2_38_2","volume-title":"Reinforcement Learning: An Introduction (2nd ed.)","author":"Sutton Richard S.","year":"2018","unstructured":"Richard S. Sutton and Andrew G. Barto. 2018. Reinforcement Learning: An Introduction (2nd ed.). The MIT Press."},{"key":"e_1_3_2_39_2","doi-asserted-by":"publisher","DOI":"10.1109\/TC.2020.3009140"},{"key":"e_1_3_2_40_2","volume-title":"Proceedings of the 30th AAAI Conference on Artificial Intelligence (AAAI\u201916)","author":"Hasselt Hado Van","year":"2016","unstructured":"Hado Van Hasselt, Arthur Guez, and David Silver. 2016. Deep reinforcement learning with double Q-Learning. In Proceedings of the 30th AAAI Conference on Artificial Intelligence (AAAI\u201916). 2094\u20132100."},{"key":"e_1_3_2_41_2","doi-asserted-by":"publisher","DOI":"10.1145\/3316781.3317859"},{"key":"e_1_3_2_42_2","unstructured":"Alwin Zulehner. 2018. Quantum Information Software Kit (QISKit)\u2014Compiler Code (GitHub Repository fork). Retrieved May 2020 from https:\/\/github.com\/azulehner\/qiskit-sdk-py\/blob\/mapping\/qiskit\/mapper\/%_mapping.py."},{"key":"e_1_3_2_43_2","doi-asserted-by":"publisher","DOI":"10.1109\/TCAD.2018.2846658"},{"key":"e_1_3_2_44_2","doi-asserted-by":"publisher","DOI":"10.1145\/3287624.3287704"}],"container-title":["ACM Transactions on Quantum Computing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3520434","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3520434","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T18:09:32Z","timestamp":1750183772000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3520434"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,5,16]]},"references-count":43,"journal-issue":{"issue":"2","published-print":{"date-parts":[[2022,6,30]]}},"alternative-id":["10.1145\/3520434"],"URL":"https:\/\/doi.org\/10.1145\/3520434","relation":{},"ISSN":["2643-6809","2643-6817"],"issn-type":[{"value":"2643-6809","type":"print"},{"value":"2643-6817","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,5,16]]},"assertion":[{"value":"2020-11-01","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2022-02-01","order":1,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2022-05-16","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}