Machine Learning in Quantum Science and Computing

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Related lectures (31)

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Covers the principles of quantum simulation with cold atoms, including optical dipole potentials and Feshbach resonances.

Quantum and Nanocomputing

Explores the fundamentals and history of quantum computing, including qubit realization and the first and second quantum revolutions.

Quantum and Nanocomputing

Explores quantum computing fundamentals, quantum Fourier transform, RSA encryption, Shor's algorithm, and experimental implementations.

Quantum Learning: Complexity and Predictive Models

Covers the complexity and learnability in complex quantum systems, focusing on quantum advantages in learning and predicting properties of quantum states.

Quantum Natural Gradient: Optimization Methods in Quantum Computing

Explores Quantum Natural Gradient optimization in quantum computing and the importance of choosing the right ansatz.

Quantum Computing and Error Correction

Explores quantum computing, error correction, quantum gates, and hybrid quantum-classical algorithms.

Quantum and Nanocomputing

Explores quantum computing fundamentals, including qubits, gates, and entanglement, as well as the impact of quantum computing on society.

Quantum Entanglement: From Einstein to Bell

Delves into the journey from Einstein and Bell's debates to the practical applications of quantum entanglement, including quantum teleportation and key distribution.

Quantum Cryptography: Optical Qubits and Teleportation

Explores quantum cryptography, focusing on optical qubits, entanglement, and teleportation principles.

Quantum Information and Computing

Covers quantum information, computing, algorithms, error correction, and challenges in the field.