Neural Network Quantum State Tomography

Related lectures (32)

Page 3 of 4

Explores the general description of quantum measurements, including post-measurement states and Kraus operators.

Covers the basics of quantum qubits, including abstract qubits, states, measurement, and evolution in quantum computation.

Introduces the fundamentals of statistical learning, covering supervised learning, decision theory, risk minimization, and overfitting.

Covers data representations, Bag of Words, histograms, data pre-processing, and neural networks.

Covers the fundamentals of multi-layer neural networks and the training process of fully connected networks with hidden layers.

Introduces deep learning fundamentals, covering data representations, neural networks, and convolutional neural networks.

Covers the principles of quantum physics, focusing on tensor product spaces and entangled vectors.

Delves into quantum entanglement, exploring entangled particles' state, evolution, and measurement.

Introduces Convolutional Neural Networks, explaining their architecture, training process, and applications in semantic segmentation tasks.

Explores overparameterization, generalization, overfitting, underfitting, and implicit regularization in deep learning models.