Researchers Develop Method for Adaptable Photonic Circuits to Power Quantum Convolutional Neural Networks

Machine learning models called convolutional neural networks (CNNs) power technologies like image recognition and language translation. A quantum counterpart—known as a quantum convolutional neural network (QCNN)—could process information more efficiently by using quantum states instead of classical bits. Photons are fast, stable, and easy to manipulate on chips, making photonic systems a promising platform for QCNNs. However, photonic circuits typically behave linearly, limiting the flexible operations that neural networks need. In a studypublishedinAdvanced Photonics, researchers introduced a method to make photonic circuits more adaptable without sacrificing compatibility with current technologies. Their approach adds a controlled step—called adaptive state injection—that lets the circuit adjust its behavior based on a measurement taken during processing. This extra control moves photonic QCNNs closer to practical use. The team built a modular QCNN using single photons from a quantum-dot source and two integrated quantum photonic processors. Like a classical CNN, the network processes information in stages. After the first stage, part of the light signal is measured.