Quantum Machine Learning course on Github

Ph.D. course Quantum Machine Learning

Duration 20h (3CFU)
Scheduled at February or March 2022

1) introduction to phase space methods in quantum optics
2) introduction to quantum machine learning

1) Methods in the phase space, characteristic function
2) Gaussian states and their transformations
3) Neural network representation of Gaussian states
4) Training of quantum machine learning models
5) Examples
Gaussian Boson sampling
Neural networks variational ansatz for quantum many-body

Exam (two options)
1) Colloquium on theoretical aspects
2) Coding examples

Barnett and Radmore, Methods in Theoretical Quantum Optics

Boson sampling solitons by quantum machine learning


We use a neural network variational ansatz to compute Gaussian quantum discrete solitons in an array of waveguides described by the quantum discrete nonlinear Schroedinger equation. By training the quantum machine learning model in the phase space, we find different quantum soliton solutions varying the number of particles and interaction strength. The use of Gaussian states enables measuring the degree of entanglement and the boson sampling patterns. We compute the probability of generating different particle pairs when varying the soliton features and unveil that bound states of discrete solitons emit correlated pairs of photons. These results may have a role in boson sampling experiments with nonlinear systems and in developing quantum processors to generate entangled many-photon nonlinear states.