Quantum Boltzmann Machines

The qbm Python package is designed for training and analyzing quantum Boltzmann machines (QBMs) using either a simulation or a D-Wave quantum annealer. A QBM is an energy-based generative model built on a quantum mechanical Hamiltonian rather than a classical energy function, allowing it to capture correlations that a classical model can't express. The QBM implemented here is the bound-based quantum restricted Boltzmann machine (BQRBM), which has intra-layer restrictions and is trained via maximization of the log-likelihood lower bound, based on the work in Quantum Boltzman Machine by Amin et al. [1]. This package originated as part of the master's thesis Quantum Boltzmann Machines: Applications in Quantitative Finance by Cameron Perot [2]. An overview of the underlying theory is provided in the documentation.

References

[1] Mohammad H. Amin et al. “Quantum Boltzmann Machine”. In: Phys. Rev. X 8 (2 May 2018), p. 021050. doi: 10.1103/PhysRevX.8.021050. url: https://link.aps.org/doi/10.1103/PhysRevX.8.021050.

[2] Cameron Perot. “Quantum Boltzmann Machines: Applications in Quantitative Finance”. Master's Thesis, RWTH Aachen University, 2022. url: https://arxiv.org/abs/2301.13295.