Training the parametric interactions in an analog bosonic quantum neural network with Fock basis measurement

• URL: http://arxiv.org/abs/2411.19112v1
• Date: Thu, 28 Nov 2024 12:59:19 GMT
• Title: Training the parametric interactions in an analog bosonic quantum neural network with Fock basis measurement
• Authors: Julien Dudas, Baptiste Carles, Elie Gouzien, Julie Grollier, Danijela Marković,
• Abstract summary: We propose leveraging bosonic modes and performing Fock basis measurements, enabling the extraction of an exponential number of features relative to the number of modes. We demonstrate that these parameters, despite their differing physical dimensions, can be trained cohesively to solve benchmark tasks of increasing complexity.
• Score: 0.9786690381850356
• License:
• Abstract: Quantum neural networks have the potential to be seamlessly integrated with quantum devices for the automatic recognition of quantum states. However, performing complex tasks requires a large number of neurons densely connected through trainable, parameterized weights - a challenging feat when using qubits. To address this, we propose leveraging bosonic modes and performing Fock basis measurements, enabling the extraction of an exponential number of features relative to the number of modes. Unlike qubits, bosons can be coupled through multiple parametric drives, with amplitudes, phases, and frequency detunings serving dual purposes: data encoding and trainable parameters. We demonstrate that these parameters, despite their differing physical dimensions, can be trained cohesively using backpropagation to solve benchmark tasks of increasing complexity. Furthermore, we show that training significantly reduces the number of measurements required for feature extraction compared to untrained quantum neural networks, such as quantum reservoir computing.

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