Note: this is a fork of the upstream bosonic, ported to Python 3.
Bosonic is a library developed for the simulation of photonic systems whose inputs are indistinguishable bosons (in the case of the authors' interest, photons). In particular, it focuses on the rapid computation of the multi-particle transfer functions for these systems and supports computation of the gradient of a cost function with respect to the system parameters. It was originally developed for the devleopment of our Quantum Optical Neural Networks [1] and contains specialized functionality for their simulation and optimization.
Key focuses of this library were two-fold:
- Speed: Key functionality is written in optimized Cython with support for OpenMP threading
- Pervasive autograd support: We rely heavily on the use of the Autograd [1] library for gradient computation and efficient optimization of system parameters. Wherever optimized forward-pass functions are written in Cython, there should be explicit support for autograd coded as well. This is not currently universally true, but there is support for all major functions.
The core motivation for this package was the rapid computation of the
multi-particle unitary transform as a function of the single particle
unitary and the number of bosonic inputs. That is, if we have a four
dimensional unitary U, and we know there are 3 photons at the input,
we want to know the transformation over the binom(4+3-1, 3)dimensional basis [3,0,0,0], [2,1,0,0], [2,0,1,0], ... etc.
This is supported by the function bosonic.aa_phi, which is named
after Aaronson and Arkhipov, who specified the form of this function
that we use as their Φ(U) function in [2]. For example, we can
demonstrate the famous Hong-Ou-Mandel effect with a beamsplitter:
>>> import bosonic as b
>>> from numpy import array
>>> U = array([[1, 1], [1, -1]], dtype=complex) / np.sqrt(2)
>>> phiU = b.aa_phi(U, 2)
>>> phiU
array([[ 0.5 +0.j, 0.70710678+0.j, 0.5 +0.j],
[ 0.70710678+0.j, 0. +0.j, -0.70710678+0.j],
[ 0.5 +0.j, -0.70710678+0.j, 0.5 +0.j]])
>>> b.fock.basis(2,2)
[[2, 0], [1, 1], [0, 2]]
>>> inp = array([[0], [1], [0]], dtype=complex)
>>> phiU@inp
array([[ 0.70710678+0.j],
[ 0. +0.j],
[-0.70710678+0.j]])
>>> abs(phiU@inp)**2
array([[0.5],
[0. ],
[0.5]])Here, we build the unitary corresponding to a 50/50 beamsplitter in U. As shown the line after we print phiU, the basis here is [2, 0], [1, 1], and [0, 2]. So the state corresponding to one photon incident at each of the inputs is [0, 1, 0]. In the final line, two lines, we see that the output is an equal superposition over two photons at one output and two photons at the other, with no probability of the photons leaving by different ports.
As described in [1], we've developed a proposed architecture for quantum optical neural networks that involves tiling arbitrary unitary transformations with single-site nonlinearities. See the paper for more details, but here's a visual summary of the architecture:
Installing Bosonic should be done as follows (using your preferred
python package manager instead of pip, if desired):
$ pip install Cython numpy scipy numba
$ pip install git+https://github.com/flaport/bosonicOn Mac, you'll need gcc from homebrew and libopenmp as well:
$ brew install gcc
$ brew install libomp
$ pip install Cython numpy scipy numba
$ CC=gcc-8 pip install git+https://github.com/flaport/bosonicYou can check if bosonic is installed correctly by running pytest:
$ pip install pytest
$ pytest tests/[1] Steinbrecher, G. R., Olson, J. P., Englund, D., & Carolan, J. (2018). Quantum optical neural networks. arXiv preprint arXiv:1808.10047. https://arxiv.org/abs/1808.10047
[2] Aaronson, Scott, and Alex Arkhipov. "The computational complexity of linear optics." Proceedings of the forty-third annual ACM symposium on Theory of computing. ACM, 2011. https://arxiv.org/pdf/1011.3245.pdf