Utilizing the non-interacting bionic particle sampling to solve image classification tasks

The sampling of non-interacting boson particles has been shown to be a hard classical problem to solve. It is known as the #P class in computational complexity theory and tells us that simple systems may be used as a computational resource. It however does not tell us how to utilize them. Doing so is nontrivial. Using non-interacting boson particles, we introduce a new approach based on the quantum neural network model. The presentation discusses the design of the encoder, reservoir, and measurement process. The best performance with our models achieves a 96.6% accuracy rate for testing of hand-written digit images (MNIST) with 4 photons and 16 waveguides, which is within the reach of the current technology.