An error-mitigated photonic quantum circuit Born machine

• URL: http://arxiv.org/abs/2405.02277v2
• Date: Tue, 08 Oct 2024 16:05:46 GMT
• Title: An error-mitigated photonic quantum circuit Born machine
• Authors: Alexia Salavrakos, Tigran Sedrakyan, James Mills, Shane Mansfield, Rawad Mezher,
• Abstract summary: Generative machine learning models aim to learn the underlying distribution of the data in order to generate new samples. Quantum circuit Born machines (QCBMs) are a popular choice of quantum generative models which can be implemented on shallow circuits. We show that a new error mitigation technique, called recycling mitigation, greatly improves the training of QCBMs in realistic scenarios with photon loss.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Generative machine learning models aim to learn the underlying distribution of the data in order to generate new samples. Quantum circuit Born machines (QCBMs) are a popular choice of quantum generative models which can be implemented on shallow circuits. Within the framework of photonic quantum computing, we present a QCBM designed for linear optics. We show that a new error mitigation technique, called recycling mitigation, greatly improves the training of QCBMs in realistic scenarios with photon loss, both through simulations and an experiment on a quantum photonic integrated processor.

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