Software

Error-mitigated quantum computing SEMO Python dynamic module

Commonwealth Scientific and Industrial Research Organisation

Yang, Sam ; Chu, Clement ; Tyson, Peter

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ctx_ver=Z39.88-2004&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Adc&rfr_id=info%3Asid%2FANDS&rft_id=info:doi10.25919/y926-6d92&rft.title=Error-mitigated quantum computing SEMO Python dynamic module&rft.identifier=https://doi.org/10.25919/y926-6d92&rft.publisher=Commonwealth Scientific and Industrial Research Organisation&rft.description=Qubit noise is one of the major barriers limiting broad adoption of quantum computing in solving complex real-word problems, such as optimisation. The qubit noise and the local minima in the underlying optimisation objective function often make the probability of obtaining the true optimal solution (global optimal rather than local minima) decrease exponentially with increasing underlying problem complexity. A quantum computing error mitigation invention has been filed by CSIRO with IP Australia in Feb 2024. Based on the invention, a Python SEMO (spin-error mitigation for optimisation) software module has been developed. The SEMO software module is available for collaboration for selected parties with a SEMO digital token issued from the CSIRO applied quantum computing project team. The token is authenticated online against the registration server at the beginning of each usage session. The SEMO software module can be evaluated without a digital token for small problems up to 100 spin variables and 200 couplings. Information about CSIRO applied quantum computing and SEMO is available at http://research.csiro.au/aqc. Enquiries about accessing the SEMO software module, please email [email protected]. &rft.creator=Yang, Sam &rft.creator=Chu, Clement &rft.creator=Tyson, Peter &rft.date=2026&rft.edition=v25&rft.relation=http://doi.org/10.1002/apxr.202500216&rft_rights=CSIRO Binary Software Licence https://research.csiro.au/dap/licences/csiro-binary-software-licence-agreement/&rft_rights=Data is accessible online and may be reused in accordance with licence conditions&rft_rights=All Rights (including copyright) CSIRO 2025.&rft_subject=spin-error mitigation for optimisation&rft_subject=SEMO&rft_subject=quantum computing&rft_subject=quantum annealing&rft_subject=qubit error&rft_subject=data-constrained modelling&rft_subject=DCM&rft_subject=Python dynamic module&rft_subject=Quantum computation&rft_subject=Theory of computation&rft_subject=INFORMATION AND COMPUTING SCIENCES&rft.type=Computer Program&rft.language=English Access the software

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Qubit noise is one of the major barriers limiting broad adoption of quantum computing in solving complex real-word problems, such as optimisation. The qubit noise and the local minima in the underlying optimisation objective function often make the probability of obtaining the true optimal solution (global optimal rather than local minima) decrease exponentially with increasing underlying problem complexity. A quantum computing error mitigation invention has been filed by CSIRO with IP Australia in Feb 2024. Based on the invention, a Python SEMO (spin-error mitigation for optimisation) software module has been developed. The SEMO software module is available for collaboration for selected parties with a SEMO digital token issued from the CSIRO applied quantum computing project team. The token is authenticated online against the registration server at the beginning of each usage session. The SEMO software module can be evaluated without a digital token for small problems up to 100 spin variables and 200 couplings.
Information about CSIRO applied quantum computing and SEMO is available at http://research.csiro.au/aqc. Enquiries about accessing the SEMO software module, please email [email protected].

Available: 2026-03-30

Data time period: 2023-05-01 to 2025-09-29

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Subjects

DCM | Information and Computing Sciences | Python dynamic module | Quantum Computation | SEMO | Theory of Computation | data-constrained modelling | quantum annealing | quantum computing | qubit error | spin-error mitigation for optimisation |

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Identifiers

DOI : 10.25919/Y926-6D92
Handle : 102.100.100/614516
URL : data.csiro.au/collection/csiro:62180