| Autor: |
Wright, L., Barratt, F., Dborin, J., Wimalaweera, V., Coyle, B., Green, A. G. |
| Rok vydání: |
2022 |
| Předmět: |
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| Druh dokumentu: |
Working Paper |
| Popis: |
We present tensor networks for feature extraction and refinement of classifier performance. These networks can be initialised deterministically and have the potential for implementation on near-term intermediate-scale quantum (NISQ) devices. Feature extraction proceeds through a direct combination and compression of images amplitude-encoded over just $\log N_{\text{pixels}}$ qubits. Performance is refined using `Quantum Stacking', a deterministic method that can be applied to the predictions of any classifier regardless of structure, and implemented on NISQ devices using data re-uploading. These procedures are applied to a tensor network encoding of data, and benchmarked against the 10 class MNIST and fashion MNIST datasets. Good training and test accuracy are achieved without any variational training. |
| Databáze: |
arXiv |
| Externí odkaz: |
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