https://learn-xpro.mit.edu/quantum-computing
Quantum computing uses quantum mechanical phenomena such as superposition and entanglement to perform calculations. Unlike classical computers that use bits (0 or 1), quantum computers use quantum bits or "qubits" that can exist in multiple states simultaneously, potentially solving certain problems exponentially faster than classical computers. Resources above provide introductory courses on these fundamental concepts.
Quantum mechanics is the theoretical framework that describes nature at the atomic and subatomic scale. It forms the foundation of quantum computing by explaining how particles can exist in multiple states simultaneously (superposition) and how particles can be correlated regardless of distance (entanglement). The resources in this section explore these physics principles essential for understanding how quantum computers function.
https://www.youtube.com/playlist?list=PLsedzcQz4wyVRQkPTGRj1d91gU5W9PKSx
Qiskit is IBM's open-source quantum computing software development kit that allows users to create and run quantum programs on IBM's quantum processors and simulators. It provides tools for creating quantum circuits, optimizing them for specific hardware, and analyzing results. The resources listed provide hands-on tutorials and courses for developing quantum applications with Qiskit.
https://www.youtube.com/@qiskit/playlists
TensorFlow Quantum (TFQ) and Cirq are Google's quantum programming frameworks. Cirq is a Python library for writing, manipulating, and optimizing quantum circuits, while TFQ integrates quantum computing capabilities with machine learning using TensorFlow. These tools enable researchers and developers to create hybrid quantum-classical models. The resources above focus on programming with these Google frameworks.
https://www.youtube.com/playlist?list=PLpO2pyKisOjLVt_tDJ2K6ZTapZtHXPLB4
https://www.tensorflow.org/quantum
PennyLane is an open-source software framework for quantum machine learning, quantum chemistry, and quantum computing, with the ability to run on all hardware. Built by Xanadu.
https://pennylane.ai/codebook/learning-paths
Quantum Machine Learning combines quantum computing with machine learning techniques to potentially improve computational efficiency and capability. QML explores quantum versions of classical machine learning algorithms and develops new approaches that leverage quantum phenomena. This emerging field aims to achieve quantum advantages for data analysis, pattern recognition, and prediction tasks. The resources provided cover both theoretical foundations and practical implementations.