In this Letter, we analyze the quantum dynamics of the perceptron model: a particle is constrained on an $N$-dimensional sphere, with $N\to \infty$, and subjected to a set of randomly placed hard-wall potentials. This model has several applications, ranging from learning protocols to the effective description of the dynamics of an ensemble of infinite-dimensional hard spheres in Euclidean space. We find that the jamming transition with quantum dynamics shows critical exponents different from the classical case. We also find that the quantum jamming transition, unlike the typical quantum critical points, is not confined to the zero-temperature axis, and the classical results are recovered only at $T=\infty$. Our findings have implications for the theory of glasses at ultralow temperatures and for the study of quantum machine-learning algorithms.

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• Jamming
• Machine learning
• Quantum algorithms
• Quantum phase transitions
• Quantum statistical mechanics
• Glassy systems