Branching Quantum Convolutional Neural Networks: A Variational Ansatz with Mid-Circuit Measurements

We introduce the bQCNN, a variation of the quantum convolutional neural network (QCNN) in which outcomes from mid-circuit measurements of subsets of qubits inform subsequent quantum gate operations. This leads to a classical branching structure in which each branch contains its own set of trainable parameterized entangling gates, resulting in significantly more parameters as compared to a standard QCNN circuit of the same depth. We demonstrate classification tasks in which the bQCNN significantly outperforms a comparable QCNN of the same circuit depth. Using results from noisy simulations, we discuss the advantages that mid-circuit-measurement based circuits can offer as variational ansätze in NISQ devices.