The Heisenberg uncertainty principle puts a limit on how precisely we can measure certain properties of quantum objects. But researchers may have found a way to bypass this limitation using a quantum ...

Learn how Network in Network (NiN) architectures work and how to implement them using PyTorch. This tutorial covers the concept, benefits, and step-by-step coding examples to help you build better ...

"For the EstimatorQNN, the expected output shape for the forward pass is (1, num_qubits * num_observables)” In practice, the forward pass returns an array of shape (batch_size, num_observables)—one ...

Researchers have devised a way to make computer vision systems more efficient by building networks out of computer chips’ logic gates. Networks programmed directly into computer chip hardware can ...

Abstract: Unitary Evolution Recurrent Neural Networks (uRNNs) have three attractive properties: (a) the unitary property, (b) the complex-valued nature, and (c) their efficient linear operators [1].

This is my journey to implement NNs from first principles, one neuron at a time. In this notebook we build a neural network with 2 neurons in layer 1, and 1 neuron in layer 2. We then visualize how it ...