An em algorithm for quantum Boltzmann machines
This work addresses challenges in quantum machine learning, such as non-commutativity or vanishing gradients, by introducing an information-geometric optimization method, though it appears incremental as an extension of existing algorithms to a quantum context.
The paper tackled the problem of training quantum Boltzmann machines by developing a quantum version of the em algorithm, which achieved stable learning and outperformed gradient-based methods in several cases on benchmark datasets.
We develop a quantum version of the em algorithm for training quantum Boltzmann machines. The em algorithm is an information-geometric extension of the well-known expectation-maximization (EM) algorithm, offering a structured alternative to gradient-based methods with potential advantages in stability and convergence. We implement the algorithm on a semi-quantum restricted Boltzmann machine, where quantum effects are confined to the hidden layer. This structure enables analytical update rules while preserving quantum expressivity. Numerical experiments on benchmark datasets show that the proposed method achieves stable learning and outperforms gradient-based training in several cases. These results demonstrate the potential of information-geometric optimization for quantum machine learning, particularly in settings where standard methods struggle due to non-commutativity or vanishing gradients.