Boltzmann Classifier: A Thermodynamic-Inspired Approach to Supervised Learning

We propose a novel classification algorithm, the Boltzmann Classifier, inspired by the thermodynamic principles underlying the Boltzmann distribution. Our method computes a probabilistic estimate for each class based on an energy function derived from feature-wise deviations between input samples and class-specific centroids. The resulting probabilities are proportional to the exponential negative energies, normalized across classes, analogous to the Boltzmann distribution used in statistical mechanics. In addition, the KT variable can be used to allow the high energy states to be more accessible, which allows the tuning of their probabilities as needed. We evaluate the model performance on several datasets from different applications. The model achieves a high accuracy, which indicates that the Boltzmann Classifier is competitive with standard models like logistic regression and k-nearest neighbors while offering a thermodynamically motivated probabilistic interpretation. our classifier does not require iterative optimization or backpropagation and is thus computationally efficient and easy to integrate into existing workflows. This work demonstrates how ideas from physics can inform new directions in machine learning, providing a foundation for interpretable, energy-based decision-making systems.