Coding sample for the quantum machine learning course
https://github.com/nonlinearxwaves/quantummachinelearning
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Category: Quantum Machine Learning
Ph.D. course Quantum Machine Learning
Duration 20h (3CFU)
Scheduled at February or March 2022
Goals
1) introduction to phase space methods in quantum optics
2) introduction to quantum machine learning
Program
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
Entanglement
Gaussian Boson sampling
Neural networks variational ansatz for quantum many-body
Exam (two options)
1) Colloquium on theoretical aspects
2) Coding examples
References
Barnett and Radmore, Methods in Theoretical Quantum Optics
ArXiv:2110.12379
ArXiv:2102.12142
Boson sampling solitons by quantum machine learning
https://arxiv.org/abs/2110.12379
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.