Fast Differentiable Forest lib with the advantages of both decision trees and neural networks
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Updated
Nov 21, 2021 - Python
Fast Differentiable Forest lib with the advantages of both decision trees and neural networks
Developing an interactive, visualization of quantum teleportation using Qiskit, React, JavaScript. Experience quantum mechanics through a cinematic, 3D interface and get a hands-on experience of quantum computing designed for learning and experimenting for all levels.
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.
Quantum computing portfolio featuring Qiskit implementations, quantum information protocols, noise analysis, and error correction.
Copy complexity of reproducing labeled quantum trajectories, with unknown measurement probabilities and coherent processing.
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.
AI-powered, hands-on quantum computing learning platform for mastering quantum concepts, circuits, algorithms, and practical experiments.
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.
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