Quantum Deformed Neural Networks
This introduces a new neural network design that bridges quantum and classical computing, though it offers only modest gains.
The authors developed quantum neural network layers that can run efficiently on quantum computers but also be simulated classically with restricted entanglement, showing these layers can be trained on normal data like images and deliver modest improvements over standard architectures.
We develop a new quantum neural network layer designed to run efficiently on a quantum computer but that can be simulated on a classical computer when restricted in the way it entangles input states. We first ask how a classical neural network architecture, both fully connected or convolutional, can be executed on a quantum computer using quantum phase estimation. We then deform the classical layer into a quantum design which entangles activations and weights into quantum superpositions. While the full model would need the exponential speedups delivered by a quantum computer, a restricted class of designs represent interesting new classical network layers that still use quantum features. We show that these quantum deformed neural networks can be trained and executed on normal data such as images, and even classically deliver modest improvements over standard architectures.