Quantum Machine Learning Course with Python [2025]

Explore how quantum feature maps encode classical data into quantum states to access higher dimensional spaces, enabling quantum kernel methods, quantum neural networks, and improved clustering.

Implement a quantum feature map with angle encoding on the default.qubit device for two qubits, using a basic entangled layer and numpy, then run and display the quantum state.

Explore quantum data encoding, comparing amplitude and basic encoding. Map data to states; basic encoding uses computational basis states while amplitude encoding stores vectors in amplitudes, noting efficiency and cost.

Demonstrate basic encoding and amplitude encoding for quantum machine learning, including a 4-element vector example, data normalization, and a note on scalability and data loading costs.

Explore how to implement a quantum feature map using a two-qubit Pennylane simulation, including angle encoding, entangling layers, and producing a quantum state vector for quantum machine learning.

Explore quantum kernel methods, a quantum enhanced approach to classification and regression using quantum feature maps and kernel functions in Hilbert space, compatible with classical SVM.

Implement a quantum kernel in Pennylane by building a quantum feature map, computing the quantum kernel matrix, and training a precomputed-kernel svm on synthetic data with reported accuracy.

Set up a two-qubit quantum device, encode data with a quantum feature map, and compute a kernel from state overlaps to train a quantum-kernel SVM on 20 synthetic samples.

Explore how quantum clustering leverages quantum computing to speed up unsupervised learning, including quantum k-means, fuzzy clustering, affinity propagation, and spectral clustering, with parallelism and high dimensional data handling.

Explore quantum fuzzy clustering by combining fuzzy logic with quantum kernels to map data into high dimensional quantum space and apply fuzzy c-means for cluster membership.

Implement quantum fuzzy clustering using a quantum feature map, construct a quantum kernel matrix, apply fuzzy c means on the kernel, and visualize clusters.

Segment an image into regions by applying a quantum kernel-based fuzzy clustering pipeline, including image preprocessing, quantum feature mapping, kernel matrix construction, fuzzy c-means, and reconstruction from membership.

Explore how quantum support vector machines use quantum feature maps and quantum kernels to map data into a high-dimensional quantum space for improved classification.

Implement a quantum support vector machine by building a two-qubit device, applying a quantum feature map with rotation gates, and computing a quantum kernel for training and evaluation.

Explore a step-by-step code explanation of a quantum support vector machine using a quantum kernel and train test split, built with pennylane, numpy, and scikit-learn, with accuracy score.

Explore the variational quantum classifier (VQC) with quantum feature encoding, a parameterized quantum circuit, measurements and classical post-processing for hybrid classification, using angle or amplitude encoding and gradient optimization.

Implement a variational quantum classifier with Pennylane and torch, building a hybrid quantum-classical model, training on synthetic data using a quantum feature map and entangling layer.

Explore the variational quantum classifier using PennyLane and a two-qubit hybrid quantum-classical model, encoding data with angle embedding, trainable gates, and a classical FC layer for binary classification.

Explore quantum k-means clustering, using quantum distance or kernel matrices to accelerate high-dimensional unsupervised clustering, with applications in image segmentation and genomic data analysis.

Implement quantum k-means clustering by defining a two-qubit quantum device and a quantum feature map that encodes data into states, then compute a quantum kernel distance for clustering.

Implement a 3d optimized quantum k-means with a variational quantum feature map encoding 3d data and trainable parameters. Demonstrate stronger entanglement and an improved quantum kernel that boosts clustering accuracy.

Define a three-qubit variational quantum feature map to encode 3d data and compute a quantum distance matrix for clustering. Apply k-means with three clusters on the optimized quantum kernel.

Explore quantum deep learning, a fusion of quantum computing and deep learning, using quantum neural networks and variational quantum circuits with feature maps to accelerate training and improve models.

Implement quantum deep learning with PennyLane by building a quantum neural network, configuring a four-qubit device, applying entangled layers, and training with PyTorch and the Adam optimizer.

Explore the code explanation for a quantum neural network using PennyLane and PyTorch, detailing quantum feature encoding, entangling layers, and training with Adam to minimize mean squared error.

Understand what a quantum neural network is, using qubits and quantum gates for quantum feature encoding, trained via a variational circuit and classical optimizer.

Implement a simple quantum neural network, build a two-qubit circuit, define a hybrid model, and train a classification dataset with a train/test split.

Set up a two-qubit quantum device, apply angle embeddings and strongly entangling layers to build a hybrid quantum-classical PyTorch model for binary classification, trained on 200 samples with 80/20 split.

Explore quantum convolutional neural networks, a quantum version of classical CNNs that uses entangling gates and quantum pooling for efficient feature extraction and quantum data classification.

Implement a simple quantum convolutional neural network using Pennylane and torch, defining a four-qubit device with quantum convolution and pooling layers, and a hybrid qcnn model for classifying quantum features.

Learn to implement a quantum convolutional neural network by defining a four-qubit device, building quantum convolutional and pooling layers, and linking quantum features to a classical classifier in PyTorch.

Explore quantum generative adversarial networks (qgan) with quantum circuits for generator and discriminator, using superposition, entanglement, and interference to model complex data distributions and enhance learning.

Implement a qgan with a two-qubit quantum generator and a classical discriminator, train on real and generated data, and plot the discriminator and generator loss curves.

Explore a hybrid quantum-classical qgan: a variational quantum circuit generator with two qubits, a classical discriminator, and adversarial training using binary cross entropy and Adam.

Implement a qgan with a four-qubit quantum generator and a classical discriminator to learn a 2D gaussian distribution. Train with binary cross-entropy and visualize real versus generated data.

Develop and train a quantum gan for 3-D data using Pennylane, building a quantum generator and a classical discriminator, and visualize real versus generated samples.

Explains a QGAN with 3-D data by setting up a three-qubit quantum device, a quantum generator, and a classical discriminator, plus training and 3-D data visualization.

Explore quantum transfer learning, a hybrid approach where pre-trained classical models like ResNet vgg16 feed features into a quantum neural network, enabling efficient hybrid training and prediction.

Explore the implementation of quantum transfer learning in a quantum machine learning course with Python, blending quantum models with transfer learning techniques.

Combine ResNet-18 feature extraction with a hybrid four-qubit variational quantum classifier using angle embedding and strongly entangling layers to classify MNIST zero and one.

Develop quantum transfer learning by implementing a ResNet-based approach with PennyLane in Python, illuminating key quantum machine learning concepts for this 2025 course.

Integrates a classical ResNet-18 feature extractor with a quantum variational classifier to perform transfer learning on the cipher ten dataset with three classes, forming a hybrid model.

Practice relentlessly to excel in deep learning and become a successful deep learning engineer. Download the Kaggle and UCI respiratory datasets to develop and share your model.