Abstract
Noise in quantum computers presents a challenge for the users of quantum computing despite the rapid progress we have seen in the past few years in building quantum computers. Existing works have addressed the noise in quantum computers using a variety of mitigation techniques since error correction requires a large number of qubits which is infeasible at present. One of the consequences of quantum computing noise is that users are unable to reproduce similar output from the same quantum computer at different times, let alone from various quantum computers. In this work, we have made initial attempts to visualize quantum basis states for all the circuits that were used in quantum machine learning from various quantum computers and noise-free quantum simulators. We have opened up a pathway for further research into this field where we will be able to isolate noisy states from non-noisy states leading to efficient error mitigation. This is where our work provides an important step in the direction of efficient error mitigation. Our work also provides a ground for quantum noise visualization in the case of large numbers of qubits.
Original language | English |
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Title of host publication | QCCC 2023 - Proceedings of the 2023 International Workshop on Quantum Classical Cooperative Computing |
Publisher | Association for Computing Machinery, Inc |
Pages | 25-28 |
Number of pages | 4 |
ISBN (Electronic) | 9798400701627 |
DOIs | |
State | Published - Aug 10 2023 |
Event | 2023 2nd International Workshop on Quantum Classical Cooperative Computing, QCCC 2023 - Orlando, United States Duration: Jun 16 2023 → … |
Publication series
Name | QCCC 2023 - Proceedings of the 2023 International Workshop on Quantum Classical Cooperative Computing |
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Conference
Conference | 2023 2nd International Workshop on Quantum Classical Cooperative Computing, QCCC 2023 |
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Country/Territory | United States |
City | Orlando |
Period | 06/16/23 → … |
Funding
This work was partially supported by NSF 2212465, 2230111, 2217021 and 2238734. This work was supported in part by the U.S. Department of Energy (DOE) RAPIDS SciDAC project under contract number DE-AC05-00OR22725.
Keywords
- noise visualization
- quantum computing
- quantum machine learning
- quantum noise