Quantum Machine Learning by Doing

In this lesson, you’ll learn how classical computers use bits to represent and store all types of data—from text and numbers to images—using binary encoding.

In this lesson, you’ll learn how qubits differ from classical bits and how they’re represented using Dirac notation and the Bloch sphere.

In this lesson, you’ll learn how qubits differ from classical bits and how they’re represented using Dirac notation and the Bloch sphere.

In this lesson, you’ll learn how measurement collapses a qubit’s superposition into a definite state, how measurement outcomes follow quantum probabilities, and how these concepts relate to the geometry of the Bloch sphere.

In this lesson, you’ll learn how measurement collapses a qubit’s superposition into a definite state, how measurement outcomes follow quantum probabilities, and how these concepts relate to the geometry of the Bloch sphere.

In this lesson, you’ll explore quantum superposition and learn how these states are represented mathematically and geometrically on the Bloch sphere.

In this lesson, you’ll explore quantum superposition and learn how these states are represented mathematically and geometrically on the Bloch sphere.

In this lesson, you’ll learn what entanglement is and how multi-qubit states are represented.

In this lesson, you’ll learn what entanglement is and how multi-qubit states are represented.

In this lesson, you’ll learn how to use the Q-sphere to visualize and interpret the probabilities and phases of multi-qubit quantum states, helping you build intuition for entanglement, superposition, and phase variation.

In this lesson, you’ll explore the main computational paradigms of quantum computing, and learn how qubits, wires, and quantum gates come together to build quantum circuits.

Discover the different hardware and cloud-based options for running quantum algorithms.

In this lesson, you’ll learn how to use circuit visualization tools to understand how quantum gates transform qubit states throughout a computation.

In this lesson, you’ll learn how the Pauli-X gate flips a qubit’s state between |0⟩ and |1⟩, and explore its mathematical and visual effects.

In this lesson, you’ll learn how the Pauli-X gate flips a qubit’s state between |0⟩ and |1⟩, and explore its mathematical and visual effects.

In this lesson, you’ll learn how the Pauli-Z gate applies a phase shift to the |1⟩ state and explore how phase affects quantum states.

In this lesson, you’ll learn how the Pauli-Z gate applies a phase shift to the |1⟩ state and explore how phase affects quantum states.

In this lesson, you’ll learn how the Pauli-Y gate combines a bit flip with a phase shift.

In this lesson, you’ll learn how the Pauli-Y gate combines a bit flip with a phase shift.

In this lesson, you’ll learn how the Hadamard gate creates superposition between states.

In this lesson, you’ll learn how the Hadamard gate creates superposition between states.

In this lesson, you’ll learn how rotation gates allow precise control of qubit states by creating uneven superpositions and phase shifts.

In this lesson, you’ll learn how rotation gates allow precise control of qubit states by creating uneven superpositions and phase shifts.

In this lesson, you’ll learn how controlled gates—especially the CNOT gate—are used to create entanglement between qubits.

In this lesson, you’ll learn how controlled gates—especially the CNOT gate—are used to create entanglement between qubits.

In this lesson, you’ll learn how to assess quantum circuits using key metrics like gate count, circuit depth, and qubit number.

In this lesson, you’ll build your first quantum algorithm by modeling a simple Bayesian network with quantum gates, learning how quantum circuits can represent and compute conditional probabilities more efficiently than classical methods.

In this lesson, you’ll learn how quantum computing can enhance machine learning (ML) by offering computational speed-ups, tackling high-dimensional data, and integrating quantum methods into key parts of the ML pipeline.

Quantum machine learning (QML) combines the groundbreaking technologies of quantum computing and machine learning.

Discover the near-term and long-term advantages of QML.

In this lesson, you’ll explore how quantum algorithms like the Quantum Fourier Transform, HHL, and Quantum Phase Estimation promise exponential speed-ups for linear algebra tasks at the core of many classical ML methods, even as their practical use depends on future hardware advances.

In this lesson, you’ll explore QML algorithms that offer advantages like data compression, smoother training, improved explainability, and novel data representations—many of which can already be used on current or quantum-inspired hardware.

In this lesson, you’ll learn how quantum neural networks (QNNs) differ from classical neural networks, and what advantages they offer.

In this lesson, you’ll explore key methods for mapping classical data into quantum states.

In this lesson, you’ll explore how variational circuits extract patterns from data using parameterized gate, and compare common designs used for different tasks and hardware constraints.

In this lesson, you’ll learn how quantum circuits are optimized by using either gradient-free or gradient-based algorithms to train variational parameters effectively.

In this lesson, you’ll explore two essential properties of quantum neural networks—entangling capacity and expressibility—and learn how they impact a circuit’s ability to model complex data while balancing depth, performance, and trainability.

In this lesson, you’ll design, train, and evaluate your first quantum neural network by integrating it into a hybrid classical-quantum model to predict protein-ligand binding affinity—demonstrating real-world impact in drug discovery.

In this lesson, you’ll learn how quantum and quantum-inspired techniques can be used to enhance image analysis, addressing challenges in classical AI and improving diagnostic accuracy and efficiency.

Learn about the framework for Ingenii's unsupervised pipeline for medical imaging segmentation.

In this lesson, you’ll learn about key challenges in applying AI to image processing and explore how quantum and quantum-inspired methods may help address them.

In this lesson, you’ll explore how quantum and quantum-inspired algorithms can enhance image analysis tasks such as data augmentation, filtering, compression, classification, and unsupervised segmentation.

In this lesson, you’ll learn how the Quantum Hadamard Edge Detection (QHED) algorithm can efficiently detect edges in images, offering potential speedups over classical methods for high-resolution data.

In this lesson, you’ll learn how quantum reservoirs can act as quantum filters for image processing, using random quantum circuits to extract features from local image blocks—offering a low-complexity alternative to classical convolutional layers.

In this lesson, you’ll learn how quantum-inspired filters use local contrast and neighborhood intensity to enhance image segmentation—offering an efficient, tunable approach for highlighting structures in medical images.

In this lesson, you’ll explore how quantum and quantum-inspired algorithms can be applied to medical image segmentation and classification.

In this lesson, you’ll learn how tensor networks can be used to reduce model complexity and improve performance in medical image classification. You'll implement a tensor network quantum circuit using the MERA architecture and train it on breast ultrasound images.

In this lesson, you’ll learn how to compress convolutional neural networks using tensor decompositions to reduce parameters and improve computational efficiency in image classification models.

In this lesson, you’ll learn how to formulate image segmentation as a QUBO problem using graph representations and smoothness constraints, enabling the application of quantum and quantum-inspired optimization techniques.

In this lesson, you’ll learn how to solve QUBO-based image segmentation problems using Simulated Annealing, Quantum Annealing, and classical solvers like Gurobi, and compare their performance and execution time.