Quantum Machine Learning With SQUID

Tóm tắt

In this work we present the Scaled QUantum IDentifier (SQUID), an open-source framework for exploring hybrid Quantum-Classical algorithms for classification problems. The classical infrastructure is based on PyTorch and we provide a standardized design to implement a variety of quantum models with the capability of back-propagation for efficient training. We present the structure of our framework and provide examples of using SQUID in a standard binary classification problem from the popular MNIST dataset. In particular, we highlight the implications for scalability for gradient-based optimization of quantum models on the choice of output for variational quantum models.

Featured image: Pictorial representation of the hybrid classifier model used for MNIST classification. Panel $(A)$ shows the complete network including an encoder with $M_0$ units and a decoder with $M_1$ units. Panel $(B)$ shows the implemented classical classifier composed by two units and panel $(C)$ shows a schematic of the quantum models: input parameters coming from the encoder determine the unitary $W$ while the output is obtained upon measurement of the qubits.

► Dữ liệu BibTeX

@article{Roggero2022quantummachine, doi = {10.22331/q-2022-05-30-727}, url = {https://doi.org/10.22331/q-2022-05-30-727}, title = {Quantum {M}achine {L}earning with {SQUID}}, author = {Roggero, Alessandro and Filipek, Jakub and Hsu, Shih-Chieh and Wiebe, Nathan}, journal = {{Quantum}}, issn = {2521-327X}, publisher = {{Verein zur F{“{o}}rderung des Open Access Publizierens in den Quantenwissenschaften}}, volume = {6}, pages = {727}, month = may, year = {2022} }

► Tài liệu tham khảo

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Trích dẫn

[1] Natalie Klco, Alessandro Roggero và Martin J. Savage, “Vật lý mô hình tiêu chuẩn và cuộc cách mạng lượng tử kỹ thuật số: suy nghĩ về giao diện”, Báo cáo về Tiến bộ trong Vật lý 85 6, 064301 (2022).

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