Awsome Quantum Computing Resources

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Quantum Computing

https://www.quicst.org/

https://learn-xpro.mit.edu/quantum-computing

Quantum Computing Fundamentals

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.

Q-munity

• Video Tutorials

  https://www.youtube.com/playlist?list=PLt_nrfusQeEc-5tBqiQkmt70Aeu_zNiNT

  https://www.youtube.com/playlist?list=PL0ROAEfVIuWU6JAZKa7ohpYscVbwtaaGM

  https://www.youtube.com/playlist?list=PLJk_IGUjQllfCDHYmFNfaCoUgXImxmZIA

  https://youtube.com/playlist?list=PL3zjoJt5EuoE8dDn1wuR7c4wLa8DQOn6a&si=-8auhdDwdL7ftJYu

  https://www.youtube.com/playlist?list=PL4wzlfHhrqQzJfrxDv2nYmLwvDBZmPb9-

  https://www.youtube.com/playlist?list=PL8jELIzOAQWVA1R9cikbBZPduqHQ0pIal

  https://www.youtube.com/playlist?list=PLctkw0a4lXUPpzIjXgHYEo20JocvHDCNS

  https://www.youtube.com/playlist?list=PLnK6MrIqGXsJfcBdppW3CKJ858zR8P4eP

  https://www.youtube.com/playlist?list=PLOFEBzvs-VvqKKMXX4vbi4EB1uaErFMSO

  https://www.youtube.com/watch?v=Rs2TzarBX5I&list=PLOFEBzvs-VvrXTMy5Y2IqmSaUjfnhvBHR

Quantum Mechanics

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

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

https://quantum.ibm.com/

TFQ & Cirq

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

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

Torchquantum

GitHub - mit-han-lab/torchquantum: A PyTorch-based framework for Quantum Classical Simulation, Quantum Machine Learning, Quantum Neural Networks, Parameterized Quantum Circuits with support for easy deployments on real quantum computers. https://hanruiwanghw.wixsite.com/torchquantum https://github.com/mit-han-lab/torchquantum https://torchquantum.readthedocs.io/en/latest/

CUDA-Q

https://github.com/NVIDIA/cuda-quantum https://nvidia.github.io/cuda-quantum/latest/applications/python/quantum_transformer.html

https://docs.classiq.io/latest/user-guide/read/qml_with_classiq_guide/ https://platform.classiq.io/

Quantum Information & Computing

https://qc.stanford.edu/course-pathways https://cs269q.stanford.edu/syllabus.html https://homes.cs.washington.edu/~jrl/teaching/cse599Q/ https://quantumcomputinguk.org/tutorials

QML (Quantum Machine Learning)

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.

https://www.youtube.com/playlist?list=PLwGoMEQCFdADok2An7RrC0yAnA_S7QLyq

https://www.youtube.com/playlist?list=PLmRxgFnCIhaMgvot-Xuym_hn69lmzIokg

https://www.youtube.com/playlist?list=PLOFEBzvs-VvqJwybFxkTiDzhf5E11p8BI

https://youtube.com/playlist?list=PLmRxgFnCIhaMgvot-Xuym_hn69lmzIokg&si=DTKRv9YBlyL6oahD

https://www.youtube.com/playlist?list=PL80kAHvQbh-otq6Qoi6mwSJAWhT3Rhfjq

https://qiskit-community.github.io/qiskit-machine-learning/tutorials/index.html

QDL (Quantum Deep Learning)

Quantum Deep Learning focuses specifically on applying quantum computing principles to enhance deep neural networks and other deep learning architectures. This specialized field investigates how quantum operations might improve feature extraction, reduce training time, or enable more complex model structures beyond classical capabilities. The provided resources explore cutting-edge research in this rapidly evolving domain.

https://www.youtube.com/playlist?list=PL49_lN_MLxXIucECc6lwAy7YQWnHx2Stk

All Quantum Resources sheet - https://docs.google.com/document/d/1ixwS3HMzebIbR7WpuE0UsqhTyAkkaF8yczvjZ2yut5E/edit?usp=sharing

Contact

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