Machine Learning in Quantum Science and Computing

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Related lectures (31)

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Quantum Information Theory: Basics and Quantum States

Covers the basics of quantum information theory, focusing on classical and quantum states, evolutions, and measurements.

NISQ and IBM Q

Explores NISQ devices and IBM Q, covering noisy quantum circuits, qubit technologies, and quantum algorithm development.

Polarisation of Photons

Explores photon polarisation, including linear, circular, and elliptical polarisation concepts.

Quantum Information Processing: Superconducting Circuits

Introduces experimental realizations of quantum information processing, focusing on superconducting circuits and the differences between classical and quantum computing.

Introduction to Quantum Computing: NISQ and IBM Q

Covers the basics of quantum computing, focusing on NISQ and IBM Q, including qubits, superposition, and quantum information processing.

Quantum and nanocomputing

Explores quantum computing fundamentals, qubit realization, control, scalable quantum computers, and spin qubits.

Quantum Qubits: Understanding the Basics

Covers the basics of quantum qubits, including abstract qubits, states, measurement, and evolution in quantum computation.

Quantum Compilation: Synthesis and Decomposition

Covers quantum computing fundamentals, compilation, error correction, unitary synthesis, quantum gates, and FPGA-based ADCs.

Variational Algorithms: Quantum Computing Optimization

Introduces variational algorithms in quantum computing, focusing on optimization problems.

Quantum Computing: Fundamentals and Applications

Covers the fundamentals of quantum computing, including qubit realization, scalable quantum computers, quantum communication, and quantum algorithms.