We use machine learning to optimise the parameters of an optical circuit towards generating high quality quantum resource states (in particular, we employ the global optimisation algorithm basinhopping). These resource states are needed to perform entanglement purification in a linear-optical system, as discussed in our paper "Achieving the ultimate end-to-end rate of a lossy quantum communication network" [1]. The main notebook with key code and details are given in statefindercode.ipynb. Saved parameter sets that we have found to be optimal are in the statefinderdata folder.
We also acknowledge the papers "Production of photonic universal quantum gates enhanced by machine learning" [2] and "Progress towards practical qubit computation using approximate Gottesman-Kitaev-Preskill codes" [3], whose code we have used and modified here for our own purposes. We also acknowledge the libraries strawberryfields [4] and thewarlus [5], which we used to perform the quantum simulation.
[1] M. S. Winnel, J. J. Guanzon, N. Hosseinidehaj, and T. C. Ralph, "Achieving the ultimate end-to-end rates of lossy quantum communication networks," npj Quantum Information 8, 129 (2022).
[2] K. K. Sabapathy, H. Qi, J. Izaac, and C. Weedbrook, "Production of photonic universal quantum gates enhanced by machine learning," Physical Review A 100, 012326 (2019).
[3] I. Tzitrin, J. E. Bourassa, N. C. Menicucci, and K. K. Sabapathy, "Progress towards practical qubit computation using approximate Gottesman-Kitaev-Preskill codes," Physical Review A 101, 032315 (2020).
[4] N. Killoran, J. Izaac, N. Quesada, V. Bergholm, M. Amy, and C. Weedbrook, "Strawberry Fields: a software platform for photonic quantum computing," Quantum 3, 129 (2019).
[5] B. Gupt, J. Izaac, and N. Quesada, "The Walrus: a library for the calculation of hafnians, Hermite polynomials and Gaussian boson sampling," Journal of Open Source Software 4, 1705 (2019).