A neural network implementation can be a nice addition to a Python programmer's skill set. If you're new to Python, examining a neural network implementation is a great way to learn the language.

Data scientist Dr. James McCaffrey begins a series on presenting and explaining the most common modern techniques used for neural networks, for which over the past couple of years there have been many ...

Quantum machine learning is a highly promising application for quantum computing. The hybrid quantum-classical convolutional neural networks (QCCNN) employs parameter quantum circuit to enhance ...

Italian researchers recently developed the first functioning quantum neural network by running a special algorithm on an actual quantum computer. The team, lead by Francesco Tacchino of the ...

Neural network trained to solve quantum mechanical problems Trained on quantum mechanics, the network handles multi-body wavefunctions.

For example, when using the quantum processor to reconstruct lightning data, they found it did a better job at lower altitudes but was generally comparable to the classical neural network.

The core of HOLO CV-QNN lies in achieving affine transformations and nonlinear mappings in neural networks through layered continuously parameterized quantum gates and nonlinear activation functions.