Abstract
We investigate the robustness of quantum neural networks (QNNs) trained as binary classifiers against bit flip and depolarizing noise, two channels that emulate a subset of noise present in noisy intermediate-scale quantum (NISQ) devices. The presence of decoherence and other forms of noise poses significant challenges to classification accuracy. To assess the impact of noise on label extraction, and to evaluate the performance and robustness of our models, we trained three different label assignment methods in the presence of various levels of noise. Each QNN model uses 2 qubits to implement a parameterized model and either the qubits of the QNN, or a set of ancilla qubits are used for label extraction. These three different methods project the state of either a single qubit or multiple qubits onto the computational basis. This generates a probability distribution that is post-processed to assign a class label and a class probability. Each QNN is trained using noisy numerical simulation on a dataset with 2 features. Depolarizing noise channels are interleaved with the parameterized gates of the QNN and we include bit flip error prior to extracting the labels. The noise levels for the depolarizing noise and bit flip error are swept over large ranges of values. We deploy QNNs trained at various noise levels on IBM quantum computers- this introduces an additional source of noise due to SWAP gate noise. We observe that the QNNs trained with simulated noise can have good performance on NISQ hardware.
Original language | English |
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Title of host publication | Proceedings - 2023 IEEE International Conference on Quantum Computing and Engineering, QCE 2023 |
Editors | Hausi Muller, Yuri Alexev, Andrea Delgado, Greg Byrd |
Publisher | Institute of Electrical and Electronics Engineers Inc. |
Pages | 395-396 |
Number of pages | 2 |
ISBN (Electronic) | 9798350343236 |
DOIs | |
State | Published - 2023 |
Event | 4th IEEE International Conference on Quantum Computing and Engineering, QCE 2023 - Bellevue, United States Duration: Sep 17 2023 → Sep 22 2023 |
Publication series
Name | Proceedings - 2023 IEEE International Conference on Quantum Computing and Engineering, QCE 2023 |
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Volume | 2 |
Conference
Conference | 4th IEEE International Conference on Quantum Computing and Engineering, QCE 2023 |
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Country/Territory | United States |
City | Bellevue |
Period | 09/17/23 → 09/22/23 |
Funding
This manuscript has been authored by UT-Battelle, LLC, under contract DEAC05-00OR22725 with the US Department of Energy (DOE).
Keywords
- quantum machine learning
- quantum subspaces