Luis Mantilla

In this work we introduce the quantum springs and sticks (QSS) model, a quantum machine learning model that performs supervised learning with an exponential amount of degrees of freedom. The model is based on the springs and sticks model, a classical mechanical system that uses coupled oscillators and dissipation to perform non-linear regression. We map the dynamics to a time-dependent Hamiltonian, which uses the Caldirola–Kanai parametrization to simulate dissipation through unitary evolution. Then, the solution to the supervised learning problem can be extracted via a spectral decomposition. Finally, we simulate our algorithm on simple regression tasks using time-dependent Trotterization, and compare it to the classical Langevin-dynamics version of such algorithm.