GaussianBosonSamplingMPS.jl

This is the documentation for the GaussianBosonSamplingMPS.jl package.

This package implements the tensor-network-based algorithm for simulating Gaussian boson sampling experiments proposed in [1], and of the algorithm in [2] for computing elements of Gaussian operations in the Fock number basis.

The functions for computing hafnians and loop hafnians of square matrices are ported from The Walrus [3].

Package features

  • Simulate a linear optical quantum computer with matrix-product states (MPS) through the application of common operations such as one- and two-mode squeezing, beam splitters, etc. and also simulate losses through an attenuator channel.
  • Manipulate MPS (and sample from them) representing mixed states in the superboson formalism [4].
  • Find an approximate MPS representation of a Gaussian state in the Fock basis.
  • Sample from the outcome of a lossy Gaussian boson sampling experiment with the classical MPS-based algorithm described in [1].

Bibliography

[1]
C. Oh, M. Liu, Y. Alexeev, B. Fefferman and L. Jiang. Classical algorithm for simulating experimental Gaussian boson sampling. Nature Physics 20, 1461–1468 (2024).
[2]
N. Quesada. Franck-Condon factors by counting perfect matchings of graphs with loops. The Journal of Chemical Physics 150, 164113–164113 (2019).
[3]
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).
[4]
M. Schmutz. Real-time Green's functions in many body problems. Zeitschrift für Physik B Condensed Matter 30, 97–106 (1978).
[5]
[6]
A. Serafini. Quantum Continuous Variables (CRC Press, 2023).
[7]
L. Madsen, F. Laudenbach, M. Askarani, F. Rortais, T. Vincent, J. Bulmer, F. Miatto, L. Neuhaus, L. Helt, M. Collins, A. Lita, T. Gerrits, S. Nam, V. Vaidya, M. Menotti, I. Dhand, Z. Vernon, N. Quesada and J. Lavoie. Quantum computational advantage with a programmable photonic processor. Nature 606, 75–81 (2022).