Journal
PHYSICAL REVIEW LETTERS
Volume 131, Issue 1, Pages -Publisher
AMER PHYSICAL SOC
DOI: 10.1103/PhysRevLett.131.013601
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In this study, we propose a new quantum classifier for bosonic systems using the data reuploading technique and demonstrate it with a silicon-based photonic integrated circuit. By implementing a programmable optical circuit combined with an interferometer, we achieve a classification success probability of 94.8% in the proof of principle experiment with uncorrelated two photons. This method has the potential for further development in optical quantum classifiers, including extensions to quantum entangled and multiphoton states.
In a single qubit system, a universal quantum classifier can be realized using the data reuploading technique. In this study, we propose a new quantum classifier applying this technique to bosonic systems and successfully demonstrate it using a silicon-based photonic integrated circuit. We established a theory of quantum machine learning algorithm applicable to bosonic systems and implemented a programmable optical circuit combined with an interferometer. Learning and classification using part of the implemented optical quantum circuit with uncorrelated two photons resulted in a classification with a success probability of 94 1 0.8% in the proof of principle experiment. As this method can be applied to an arbitrary two-mode N-photon system, further development of optical quantum classifiers, such as extensions to quantum entangled and multiphoton states, is expected in the future.
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