quantum-learning

Fast Differentiable Forest lib with the advantages of both decision trees and neural networks

Learning exact conjugation-symmetry subgroups of low-rank mixed states from independent copies under finite abelian actions and a trace-distance separation promise.

Collision-entropy bounds for simulating noisy quantum examples from classical data, with conditional LWE applications, finite-seed sources, and quantum-channel consequences.

Interactive quantum learning platform that helps students learn, predict, simulate, compare, diagnose misconceptions, and understand quantum concepts with AI-assisted explanations and visualizations.

Exact flat-rank quantum classification and rank-dependent limits on coherent learning and classical compilation.

Copy-complexity separation between quantum conditional-state synthesis and arbitrary classical descriptions, with stabilizer proofs, finite verification, and a Watrous-based learning route.

Weight–memory thresholds for Pauli learning, with a shared no-reset purity obstruction.

All-time purity limits and finite protocols for Pauli learning without resets.

Optimal-order work-loss bounds for sequential learning of unknown pure qubits, contrasting fully returned classical controllers with coherent quantum memory.

Correlated phase channels with matched diagnostics, conditional learning separation, and a five-level cutting task with equal extent.