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APL paper files

The University of Queensland
Dr Tyler Jones (Aggregated by) Mr Rohit Navarathna (Aggregated by) Mr Rohit Navarathna (Aggregated by) Mr Tyler Jones (Aggregated by)
Viewed: [[ro.stat.viewed]] Cited: [[ro.stat.cited]] Accessed: [[ro.stat.accessed]]
ctx_ver=Z39.88-2004&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Adc&rfr_id=info%3Asid%2FANDS&rft_id=info:doi10.48610/9d3c2eb&rft.title=APL paper files&rft.identifier=RDM ID: c8d3bd70-0538-11ec-bd66-97be6754fd8a&rft.publisher=The University of Queensland&rft.description=Neural networks have proven to be efficient for a number of practical applications ranging from image recognition to identifying phase transitions in quantum physics models. In this paper we investigate the application of neural networks to state classification in a single-shot quantum measurement. We use dispersive readout of a superconducting transmon circuit to demonstrate an increase in assignment fidelity for both two and three state classification. More importantly, our method is ready for on-the-fly data processing without overhead or need for large data transfer to a hard drive. In addition we demonstrate the capacity of neural networks to be trained against experimental imperfections, such as phase drift of a local oscillator in a heterodyne detection scheme.&rft.creator=Dr Tyler Jones&rft.creator=Mr Rohit Navarathna&rft.creator=Mr Rohit Navarathna&rft.creator=Mr Tyler Jones&rft.date=2021&rft_rights= https://guides.library.uq.edu.au/deposit-your-data/license-reuse-data-agreement&rft_subject=eng&rft_subject=Electronic and Magnetic Properties of Condensed Matter; Superconductivity&rft_subject=PHYSICAL SCIENCES&rft_subject=CONDENSED MATTER PHYSICS&rft_subject=Quantum Information, Computation and Communication&rft_subject=QUANTUM PHYSICS&rft.type=dataset&rft.language=English Access the data
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[email protected]
School of Mathematics and Physics

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Neural networks have proven to be efficient for a number of practical applications ranging from image recognition to identifying phase transitions in quantum physics models. In this paper we investigate the application of neural networks to state classification in a single-shot quantum measurement. We use dispersive readout of a superconducting transmon circuit to demonstrate an increase in assignment fidelity for both two and three state classification. More importantly, our method is ready for on-the-fly data processing without overhead or need for large data transfer to a hard drive. In addition we demonstrate the capacity of neural networks to be trained against experimental imperfections, such as phase drift of a local oscillator in a heterodyne detection scheme.

Issued: 2021

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Subjects
Condensed Matter Physics | Electronic and Magnetic Properties of Condensed Matter; Superconductivity | Physical Sciences | Quantum Physics | Quantum Information, Computation and Communication | eng |

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Identifiers
ACN 633 798 857