Programme Content
Topics Covered
Vector spaces, eigenvalues, SVD, tensor operations
Random variables, distributions, expectation, covariance
Bayesian inference and estimation
Supervised vs unsupervised ML, bias–variance trade-off
Optimization basics (gradient descent, convexity)
Case Study
Classical-to-Quantum Readiness AssessmentParticipants analyze a classical ML pipeline (e.g., PCA + SVM for image/audio data) and identify components that can be mapped to quantum feature maps or kernels.
2. Quantum AI Systems FoundationsTopics Covered
Quantum states, Hilbert spaces, operators
Single- and multi-qubit gates
Quantum circuits and measurement theory
Noise models and decoherence
Data encoding: angle, amplitude, basis encoding
Variational Quantum Circuits (VQCs)
Hybrid quantum–classical training loops
Case Study
Quantum Feature Encoding for Multimodal DataDesign a quantum encoding pipeline for image or sensor data and study the impact of noise and measurement on learning performance.
Learners will pick one of these problems to solve
Audio event classification (speech vs noise, music vs speech)
Audio anomaly detection in machinery or environmental sounds
Short audio time-series classification using quantum feature maps
Handwritten digit classification using reduced-resolution images
Binary image classification (object present vs not present)
Topics Covered
Quantum kernels and kernel alignment
QSVM architectures
Quantum generative models (QGAN, QVAE, QBM)
Quantum diffusion concepts
Quantum vision models
Quantum RNNs, LSTMs, and Transformers
Circuit pruning and parameter reduction
Hardware-aware circuit compilation
Case Study
Quantum Kernel Advantage in ClassificationCompare classical SVM and QSVM performance on high-dimensional datasets using quantum kernels and analyze scalability and expressivity.
Problems picked before will continue with these advanced architectures
4. Quantum Optimization & Decision SystemsTopics Covered
Combinatorial optimization basics
QUBO and Ising formulations
QAOA architecture and parameter optimization
Constraint handling in quantum optimization
Quantum annealing principles
Case Study
Resource Allocation via QAOAFormulate a scheduling or routing problem as a QUBO model and solve it using QAOA or quantum annealing simulators.
Learners will pick one of these problems to work
Portfolio optimization problem using quantum optimization methods under risk and budget constraints.
Job scheduling across multiple machines to minimize total completion time using quantum optimization algorithms.
Partition a graph into optimal clusters by minimizing inter-cluster connections using quantum optimization methods.
Vehicle routing to minimize total travel distance under capacity constraints using quantum optimization.
Resource allocation among competing tasks by formulating the problem as a QUBO and solving it using quantum optimization.
Optimal sensor placement to maximize coverage under cost constraints using quantum optimization techniques.
Topics Covered
Hybrid quantum-classical learning workflows
Quantum hardware and noise considerations
Model performance and evaluation
Security, privacy, and trust in quantum-AI systems
Real-world applications and deployment challenges
Case Study
Federated Quantum Learning for Sensitive DataDesign a federated QML workflow where multiple nodes train a shared quantum model without exchanging raw data.
We will start the project which should integrate all the above learning.
Capstone Project
DescriptionParticipants work on an end-to-end quantum AI problem integrating encoding, learning, optimization, and deployment considerations.
Example Capstone Themes
Quantum-inspired multimodal perception systems
Quantum optimization for healthcare or logistics
Secure federated quantum ML architectures
Qiskit | PennyLane | TensorFlow | D-Wave | PyTorch | VisualQuantum™
Purpose | Tool to Be Used | Rationale |
|---|---|---|
Quantum Circuit Design & Simulation | Qiskit (IBM Quantum) | Provides a complete environment to construct, visualize, and simulate quantum circuits; supports gate operations, measurement, noise models, and foundational quantum algorithm exploration. |
Hybrid Quantum–Classical Machine Learning | PennyLane | Enables implementation of variational circuits and hybrid ML models; integrates with classical ML frameworks for gradient-based optimization and advanced quantum ML workflows. |
Quantum Annealing & Optimization Concepts | D-Wave (Simulation + Conceptual) | Introduces quantum annealing principles and QUBO/Ising formulations; allows learners to test optimization workflows in a simulated setting without requiring hardware access. |
Quantum-Integrated ML Training Pipelines | PyTorch / TensorFlow (Hybrid Setup) | Used to combine classical neural networks with quantum layers for training hybrid models; supports backpropagation, optimization loops, and model evaluation. |
Quantum State Visualization & Experimental Insight | VisualQuantum™ | Offers intuitive, real-time visualization of quantum states, Bloch sphere trajectories, measurement statistics, phase behavior, and tomography; helps convert abstract theory into observable, interactive experiments. |
