Interpret Quantum Intelligence
Gain the ability to interpret superposition, interference, measurement, and noise not as abstract concepts but as drivers of learning, pattern formation, and decision processes.
Application Closure Date : 31st July, 2025
Course Duration :
6 months
Course Fees :
₹ 1,30,000 + GST
Schedule :
Sunday - 10:00 AM to 1:00 PM
Course Eligibility :
Graduation in any of these disciplines: B.Tech/BE; BCA/MCA; B.Sc/M. Sc. in all streams; BA/MA in mathematics
Campus Immersion :
A one-day in-person session at IIT Delhi toward the end of the programme
(Travel and accommodation will be borne by the learners only.)
working in AI, ML, signal processing, and emerging quantum technologies
exploring quantum-enhanced AI systems
from technical and mathematical backgrounds
seeking upskilling in quantum computation and intelligent systems
Core principles of quantum information science
Understand quantum states, operators, rotation gates, measurement actions, entanglement, decoherence,
and how they underpin quantum computation.
Quantum data encoding strategies and circuit construction
Learn angle, basis, and amplitude encoding; parameterised gates; circuit layering; and mapping
classical information into quantum formats.
Foundations of quantum machine learning architectures
Study quantum kernels, variational algorithms, generative models, quantum sequence networks, and
architectures inspired by quantum information structures.
Quantum optimisation and logical modelling frameworks
Explore QAOA methodology, QUBO/Ising formulations, annealing principles, and how combinatorial and
decision-based problems are structured for quantum execution.
Hybrid quantum-classical model design pipelines
Combine quantum components with classical ML tools to create integrated systems with specialised
learning, inference, or optimisation abilities.
Experimentation, evaluation & interpretation through simulators
Run and analyse circuits, monitor probability distributions, understand sampling behaviour, visualise
state evolution, and interpret simulation outputs using research-grade tools.
Gain the ability to interpret superposition, interference, measurement, and noise not as abstract concepts but as drivers of learning, pattern formation, and decision processes.
Develop the capability to assemble and test quantum circuits, feature maps, kernels, and hybrid ML architectures that reflect the mathematical principles of quantum information.
Understand how classification, optimisation, and modelling tasks can be translated into quantum-ready representations suited for quantum circuits or annealing workflows.
Integrate quantum operations into established ML systems, enabling enhanced expressivity, richer feature representation, and accelerated convergence in selected tasks.
Analyse outputs from simulators to study circuit behaviour, sampling distributions, phase effects, and performance deviations under noise, depth, and gate choices.
Translate learning into practical use cases across analytics, computational modelling, optimisation, logic design, and emerging intelligent systems research.
Pre-course / Bridge Module
|
| Case Study Classical-to-Quantum Readiness Assessment Participants 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. |
Note: The tools and topics listed are indicative and may be modified as per the programme requirements and at the discretion of the Programme Coordinator.
Quantum AI Systems Foundations
|
| Case Study Quantum Feature Encoding for Multimodal Data Design a quantum encoding pipeline for image or sensor data and study the impact of noise and measurement on learning performance. |
Learners will pick one these problems to solve
|
Quantum ML for Scalable Intelligence
|
| Case Study Quantum Kernel Advantage in Classification Compare 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. |
Quantum Optimization & Decision Systems
|
| Case Study Resource Allocation via QAOA Formulate 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
|
Secure & Deployable Quantum-AI Systems
|
| Case Study Federated Quantum Learning for Sensitive Data Design 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 |
| Description Participants work on an end-to-end quantum AI problem integrating encoding, learning, optimization, and deployment considerations. |
Example Capstone Themes
|
Sourcing and Procurement Strategies
|
Supply Chain Disruption and Resilience
|
Financial and Costing Decisions
|
Financial and Costing Decisions
|
Sustainability, Ethics, and Leadership
|
Pre-programme Support:
✪ A Bridge Module is provided to revise essential mathematical concepts and introduce the ML
basics needed for later modules.
✪ Webinars will be organized by the faculty 2 months before the program to help students revise
or learn these concepts to help students revise or learn these concepts along with pre-program
preparatory content.
These webinars are optional but highly recommended, particularly for students from non-technical
backgrounds.
Evaluation & Practical Learning Components
Internal Assessments: Participants will be evaluated through periodic quizzes, concept checks,
circuit-building exercises, and simulation-based tasks conducted within each module. These assessments
ensure that learners develop clarity in quantum fundamentals, encoding techniques, variational
circuits, kernel behaviour, and optimisation workflows as they progress.
Final Evaluation: The programme concludes with a structured assessment designed to measure both
conceptual understanding and applied skills. This may include an MCQ component, analysis of quantum
simulation outputs, and review of quantum/ML assignments submitted as part of the module activities.
Case Applications
Throughout the Programme
The programme includes practical case applications within each module to demonstrate how quantum
principles behave in real computational tasks. Learners work with circuit simulations, data-encoding
examples, variational models, kernel behaviour, and optimisation routines using tools such as Qiskit,
PennyLane, and VisualQuantum.
| Tool to Be Used | Purpose | Rationale |
|---|---|---|
| Qiskit (IBM Quantum) | Quantum Circuit Design & Simulation | Provides a complete environment to construct, visualize, and simulate quantum circuits; supports gate operations, measurement, noise models, and foundational quantum algorithm exploration. |
| PennyLane | Hybrid Quantum Classical Machine Learning | Enables implementation of variational circuits and hybrid ML models; integrates with classical ML frameworks for gradient-based optimisation and advanced quantum ML workflows. |
| D-Wave (Simulation + Conceptual) | Quantum Annealing & Optimization Concepts | Introduces quantum annealing principles and QUBO/Ising formulations; allows learners to test optimization workflows in a simulated setting without requiring hardware access. |
| PyTorch / TensorFlow (Hybrid Setup) | Quantum-Integrated ML Training Pipelines | Used to combine classical neural networks with quantum layers for training hybrid models; supports backpropagation, optimisation loops, and model evaluation. |
| VisualQuantum™ | Quantum State Visualization & Experimental Insight | Offers intuitive, real-time visualisation of quantum states, Bloch sphere trajectories, measurement statistics, phase behaviour, and tomography; helps convert abstract theory into observable, interactive experiments. |
Note: The tools and topics listed are indicative and may be modified as per the programme requirements and at the discretion of the Programme Coordinator.
| Particulars | Amount (₹) |
| Programme Fees | ₹ 1,30,000 |
| GST @ 18% | ₹ 23,400 |
| Total Fees | ₹ 1,53,400 |
All fees should be submitted in the IITD CEP account only, and the details will be shared post-selection.
The receipt will be issued by the IIT Delhi CEP account and can be downloaded from the CEP Portal for your records.
Easy EMI options available.
Loan and EMI Options are services offered by Teamlease Edtech. IIT Delhi is not responsible for the same.
Candidates can withdraw within 15 days from the programme start date. A total of 80% of the total fee received will be refunded. However, the applicable tax amount paid will not be refunded on the paid amount.
Candidates withdrawing after 15 days from the start of the programme session will not be eligible for any refund.
If you wish to withdraw from the programme, you must email cepaccounts@admin.iitd.ac.in and cepdelhi@digivarsity.com, stating your intent to withdraw. The refund, if applicable, will be processed within 30 working days from the date of receiving the withdrawal request.
| Installment | Installment Date | Amount (₹) |
| I | Due within 3 Days of offer letter. | ₹ 65,000+GST |
| II | 15th April, 2026 | ₹ 65,000+GST |
| Total fees – 1,30,000+GST [No cost EMI available] | ||
GST @ 18% will be charged extra in addition to the fee.
Loan and EMI Options are services offered by TeamLease EdTech. IIT Delhi is not responsible for the same.
*Only e-Certificates will be issued by CEP, IIT Delhi for this programme.
Prof. Monika
Aggarwal
Professor
Centre for Applied Research in Electronics,
IIT
DELHI
Prof. Monika Aggarwal is a Professor at IIT Delhi with a strong
background in signal proceing image processing and communication with
growing research focus on quantum computing. She brings decades of
experience in modeling, analysis, and computation across both classical
and emerging computational paradigms.
Her academic and industrial journey includes working at Hughes Software
Systems, Gurgaon, and at Uppsala University, Sweden. Her research spans
diverse signal domains—ranging from biomedical and underwater acoustic
signals, snow acoustic to medical imaging, multidimensional image data,
and high-dimensional spatial and vector signals. Her work addresses
fundamental challenges such as direction-of-arrival estimation,
beamforming, inverse problems, and radar/sonar target detection, and
many applications across engineering and healthcare domain.
Building on this strong foundation in high-dimensional systems, and
signal representations, Prof. Aggarwal’s current passion lies in quantum
computation, with particular interest in quantum information processing,
quantum algorithms, and the convergence of signal processing, medical
imaging, and quantum system.
SUPPLY CHAIN
MANAGEMENT
STRATEGY, PLANNING, AND OPERATION
7th Revised Edition by Pearson (Pearson Education,
Sunil Chopra, Dharam Vir Kalra, Gourav
Dwivedi)
Learners are assessed through quizzes, projects, and practical assignments designed to measure understanding of both conceptual and applied aspects of quantum computing and artificial intelligence.
Participants who meet the evaluation requirements will be awarded an e-Certificate of Successful Completion from CEP, IIT Delhi. Those who fulfil the minimum attendance requirement but do not meet the evaluation criteria will receive a Certificate of Participation.
