Integrated Quantum Photonic Neural Networks interfaced with Quantum Computers

The Kron’s model provides a theoretical framework for describing quantum particles as standing energy waves confined within resonant cavities or non-uniform LC circuits [1]. By reformulating the perturbative Coulomb interaction scheme in a photonic representation, it becomes feasible to realize single-electron semiconductor quantum neural networks [2-4, 6], as well as electrostatic quantum and classical logic circuits, within an optical domain. This photonic reformulation offers a substantial reduction in system complexity and fabrication cost. In this work, interface between  quantum photonic neural network and semiconductor-based single-electron electronics is presented. Moreover, the prospective integration of Josephson junctions interfaced to semiconductor quantum dots [7], in conjunction with Gaussian Boson Sampling quantum processors [5] and photonic neural networks, is discussed as a pathway toward hybrid quantum–photonic computational platforms.