Kinetic samplers for neural quantum states

Kinetic theory for quantum plasmas

Quantum plasmas have been studied theoretically for more than four decades. Important early applications include the electron gas in metals and the electron-hole plasma in semiconductors and dimensionally reduced nanostructures. Experiments with lasers and ion beams are now making dense quantum plasmas of electrons and (classical) ions accessible in the laboratory giving rise to increased theor...

Kinetic theory for quantum nanosystems

Thèse présentée en vue de l'obtention du grade académique de Docteur en Sciences Réalisé sous la direction de

Quantum Networks for Generating Arbitrary Quantum States

Many results in quantum information theory require the generation of specific quantum states, such as EPR pairs, or the implementation of specific quantum measurements, such as a von Neumann measurement in a Fourier transformed basis. Some states and measurements can be efficiently implemented using standard quantum computational primitives such as preparing a qubit in the state |0〉 and applyin...

f-GAN: Training Generative Neural Samplers using Variational Divergence Minimization

Generative neural samplers are probabilistic models that implement sampling using feedforward neural networks: they take a random input vector and produce a sample from a probability distribution defined by the network weights. These models are expressive and allow efficient computation of samples and derivatives, but cannot be used for computing likelihoods or for marginalization. The generati...

Kinetic Equations for Quantum Many-Particle Systems