Quantum Machine Learning

Delve into the emerging field of Quantum Machine Learning, a cutting-edge domain that harnesses the power of quantum computing to process and analyze complex data. With QML, you can explore transformative applications like optimizing drug discovery, enhancing encryption, solving complex optimization problems, and simulating quantum systems for scientific research.

Courses

• Quantum machine learning course (opens in a new tab) by IBM Quantum & Qiskit, covers quantum machine learning concepts, parameterized quantum circuits, training, and problem-solving. The course introduces recent developments in supervised and unsupervised learning, concluding with a showcase project.

• University of TorontoX: Quantum Machine Learning (opens in a new tab): Emphasizes hands-on implementation using Python frameworks and features guest lectures from leading researchers. Objectives include understanding quantum states, contrasting quantum computing paradigms, implementing classical-quantum hybrid learning algorithms, and demonstrating coherent quantum machine learning protocols.

Explainers

• The Map of Quantum Computing - Quantum Computing Explained (opens in a new tab): The video succinctly outlines quantum computing, including its mechanics, the excitement surrounding quantum algorithms, their value, potential applications like quantum simulation and AI. It explores various models and physical implementations such as superconducting devices, quantum dots, trapped ions, photons, or neutral atoms, while also addressing the challenges encountered in building quantum computers.

• How Quantum Computers Break The Internet (opens in a new tab): The videos underscore quantum computing's growing significance in cryptography, data security, computational power, and AI. It explains the function of quantum computers, detailing the concept of qubits and their exceptional ability to exist in multiple states simultaneously, thereby enhancing computing power.

• What is Quantum Machine Learning? (opens in a new tab): Delves into the advantages of quantum computing in machine learning, offering insights into ongoing research. It introduces fundamental concepts of quantum computing and machine learning, illustrating their collaborative potential for crafting more robust algorithms. For a straightforward and easy-to-follow explanation, watch Quantum ML Explained.

Articles

• Quantum ML and the Power of Data (opens in a new tab): Explores quantum computers' potential advantages in machine learning. It highlights quantum computers' ability to handle complex input correlations that challenge classical machines. The article discusses data-driven complexity and how classical models can rival quantum ones, concluding that classical methods can match quantum computers' power for certain problems.

• Quantum machine learning: a classical perspective (opens in a new tab): Explores essential quantum computation, compares classical and quantum algorithm performance, considers problem perspectives in machine learning, and delves into quantum sampling methods.

Papers

• PennyLane: Automatic differentiation of hybrid quantum-classical computations (opens in a new tab) (2018): A Python framework, facilitates quantum and hybrid quantum-classical computation optimization and ML. It offers compatibility with qubit and continuous-variable models, enabling gradient calculations for variational quantum circuits, bridging classical and quantum techniques in optimization and machine learning. (code)

• TensorFlow Quantum: A Software Framework for Quantum Machine Learning (opens in a new tab) (2020): An extension of TensorFlow, empowers quantum ML. It supports model construction, training, quantum circuit simulation, and combines classical and quantum computing within a single model, offering a high-level interface for hybrid quantum-classical models. (code)

• A divide-and-conquer algorithm for quantum state preparation (opens in a new tab) (2020): Suggests a quantum state preparation method employing a divide-and-conquer algorithm to minimize the number of quantum gates needed. It involves recursively breaking down the target state into smaller sub-states that require fewer gates for preparation. (code)

• Quantum Neuron: an elementary building block for machine learning on quantum computers (opens in a new tab) (2017): Introduces a quantum neuron as a fundamental element for ML. Utilizing a quantum circuit, it conducts nonlinear input data transformations, exhibiting promise in solving classification problems while addressing scalability challenges for larger models.

• q-means: A quantum algorithm for unsupervised machine learning (opens in a new tab) (2019): Suggests "q-means," a quantum adaptation of the widely-used k-means clustering algorithm. It employs quantum phase estimation to determine eigenvalues from a distance matrix between data points. The study showcases q-means' potential in clustering and outlines challenges in its implementation on near-term quantum hardware.