Antonio DELLA CIOPPA | QUANTUM COMPUTING
Antonio DELLA CIOPPA QUANTUM COMPUTING
cod. 0622700138
QUANTUM COMPUTING
| 0622700138 | |
| DEPARTMENT OF INFORMATION AND ELECTRICAL ENGINEERING AND APPLIED MATHEMATICS | |
| EQF7 | |
| COMPUTER ENGINEERING | |
| 2025/2026 |
| YEAR OF COURSE 2 | |
| YEAR OF DIDACTIC SYSTEM 2022 | |
| SPRING SEMESTER |
| SSD | CFU | HOURS | ACTIVITY | |
|---|---|---|---|---|
| ING-INF/05 | 3 | 24 | LESSONS | |
| ING-INF/05 | 3 | 24 | EXERCISES |
| Objectives | |
|---|---|
| QUANTUM COMPUTING IS AN EMERGING FIELD THAT LEVERAGES PRINCIPLES OF QUANTUM MECHANICS TO SOLVE COMPUTATIONAL PROBLEMS MORE EFFICIENTLY THAN CLASSICAL COMPUTERS. THIS COURSE PROVIDES A COMPREHENSIVE INTRODUCTION TO QUANTUM COMPUTING, DIVIDED INTO THEORETICAL CONCEPTS AND PRACTICAL IMPLEMENTATIONS USING PENNYLANE AND QISKIT. THE FIRST HALF COVERS FUNDAMENTAL QUANTUM MECHANICS AND QUANTUM COMPUTING PRINCIPLES, WHILE THE SECOND HALF APPLIES THESE CONCEPTS TO QUANTUM SEARCH ALGORITHMS AND QUANTUM NEURAL NETWORKS. ADDITIONALLY, REFERENCE BOOKS, LECTURE MATERIALS, AND CODE REPOSITORIES ARE PROVIDED FOR FURTHER STUDY. THIS COURSE PROVIDES A BALANCED INTRODUCTION TO BOTH THEORETICAL AND PRACTICAL ASPECTS OF QUANTUM COMPUTING. STUDENTS WILL GAIN HANDS-ON EXPERIENCE WITH QUANTUM PROGRAMMING USING QISKIT AND PENNYLANE, AS WELL AS A DEEP UNDERSTANDING OF QUANTUM ALGORITHMS AND THEIR APPLICATIONS. BY THE END OF THE COURSE, STUDENTS SHOULD HAVE ACQUIRED THE FOLLOWING SKILLS: THEORETICAL UNDERSTANDING - EXPLAIN THE FUNDAMENTAL PRINCIPLES OF QUANTUM MECHANICS RELEVANT TO COMPUTING, INCLUDING SUPERPOSITION, ENTANGLEMENT, AND QUANTUM MEASUREMENT. - DESCRIBE THE MATHEMATICAL FRAMEWORK OF QUANTUM COMPUTING, INCLUDING HILBERT SPACES, TENSOR PRODUCTS, AND UNITARY TRANSFORMATIONS. - UNDERSTAND AND ANALYZE QUANTUM GATES, CIRCUITS, AND ALGORITHMS. - DIFFERENTIATE BETWEEN CLASSICAL AND QUANTUM COMPUTATIONAL COMPLEXITY (E.G., BQP VS. P VS. NP). - EXPLAIN HOW QUANTUM PARALLELISM AND QUANTUM INTERFERENCE CONTRIBUTE TO QUANTUM SPEEDUP. PRACTICAL IMPLEMENTATION SKILLS - DESIGN AND EXECUTE QUANTUM CIRCUITS USING QISKIT AND PENNYLANE. - IMPLEMENT GROVER’S ALGORITHM IN QISKIT AND PENNYLANE. - DEVELOP AND OPTIMIZE VARIATIONAL QUANTUM CIRCUITS (VQCS) FOR QUANTUM MACHINE LEARNING. - IMPLEMENT QUANTUM NEURAL NETWORKS (QNNS) IN PENNYLANE. - TRAIN QNNS USING HYBRID CLASSICAL-QUANTUM APPROACHES. - UNDERSTAND QUANTUM FEATURE MAPS AND THEIR ROLE IN MACHINE LEARNING. - IMPLEMENT QUANTUM APPROXIMATE OPTIMIZATION ALGORITHMS (QAOA) AND VARIATIONAL QUANTUM EIGENSOLVERS (VQE). - SOLVE REAL-WORLD OPTIMIZATION PROBLEMS USING QUANTUM METHODS. CRITICAL THINKING AND RESEARCH ABILITIES - CRITICALLY EVALUATE THE CURRENT LIMITATIONS AND FUTURE CHALLENGES IN QUANTUM COMPUTING. - ANALYZE QUANTUM ERROR CORRECTION TECHNIQUES AND NOISE MITIGATION STRATEGIES. - INTERPRET RECENT SCIENTIFIC PUBLICATIONS AND RESEARCH TRENDS IN QUANTUM COMPUTING. - FORMULATE AND PRESENT A FINAL PROJECT, APPLYING QUANTUM ALGORITHMS TO A REAL-WORLD PROBLEM. |
| Prerequisites | |
|---|---|
| MACHINE LEARNING |
| Contents | |
|---|---|
| PART 1: FUNDAMENTALS OF QUANTUM COMPUTING (24H) TEACHING UNIT 1: INTRODUCTION TO QUANTUM MECHANICS FOR COMPUTING (6H) (LECTURE/PRACTICE/LABORATORY HOURS 6/0/0) 1. (LECTURE 2 HOURS): INTRODUCTION TO QUANTUM COMPUTING. PROBABILISTIC INFORMATION THEORIES. 2. (LECTURE 2 HOURS): QUANTUM STATES AND QUBITS 3. (LECTURE 2 HOURS): QUANTUM GATES AND QUANTUM CIRCUITS TEACHING UNIT 2: QUANTUM COMPUTATION PRINCIPLES (6H) (LECTURE/PRACTICE/LABORATORY HOURS 6/0/0) 1. (LECTURE 2 HOURS): QUANTUM SUPERPOSITION AND ENTANGLEMENT 2. (LECTURE 2 HOURS): QUANTUM MEASUREMENT AND DECOHERENCE 3. (LECTURE 2 HOURS): QUANTUM SPEEDUP AND COMPLEXITY CLASSES (BQP VS. P VS. NP) TEACHING UNIT 3: QUANTUM ALGORITHMS FOUNDATIONS (6H) (LECTURE/PRACTICE/LABORATORY HOURS 6/0/0) 1. (LECTURE 2 HOURS): QUANTUM PARALLELISM AND INTERFERENCE 2. (LECTURE 2 HOURS): DEUTSCH-JOZSA AND BERNSTEIN-VAZIRANI ALGORITHMS 3. (LECTURE 2 HOURS): SIMON’S ALGORITHM AND INTRODUCTION TO GROVER’S ALGORITHM TEACHING UNIT 4: QUANTUM HARDWARE AND PROGRAMMING (6H) (LECTURE/PRACTICE/LABORATORY HOURS 6/0/0) 1. (LECTURE 2 HOURS): QUANTUM HARDWARE OVERVIEW (SUPERCONDUCTING QUBITS, TRAPPED IONS) 2. (LABORATORY 2 HOURS): INTRODUCTION TO QISKIT AND PENNYLANE 3. (LABORATORY 2 HOURS): QUANTUM CIRCUIT DESIGN IN QISKIT PART 2: PRACTICAL APPLICATIONS AND IMPLEMENTATIONS (24H) TEACHING UNIT 5: QUANTUM SEARCH ALGORITHMS (8H) (LECTURE/PRACTICE/LABORATORY HOURS 4/0/4) 1. (LECTURE 2 HOURS): GROVER’S ALGORITHM THEORY AND APPLICATIONS 2. (LABORATORY 2 HOURS): IMPLEMENTATION OF GROVER’S ALGORITHM IN QISKIT 3. (LECTURE 2 HOURS): AMPLITUDE AMPLIFICATION TECHNIQUES IN PENNYLANE 4. (LABORATORY 2 HOURS): VARIATIONS OF GROVER’S ALGORITHM (FIXED-POINT SEARCH, QUANTUM COUNTING) TEACHING UNIT 6: QUANTUM MACHINE LEARNING (12H) (LECTURE/PRACTICE/LABORATORY HOURS 6/0/6) 1. (LECTURE 2 HOURS): QUANTUM OPTIMIZATION ALGORITHMS (QAOA, VQE) 2. (LABORATORY 2 HOURS): IMPLEMENTING QUANTUM OPTIMIZATION IN QISKIT AND PENNYLANE 3. (LECTURE 2 HOURS): INTRODUCTION TO QUANTUM MACHINE LEARNING 4. (LECTURE 2 HOURS): VARIATIONAL QUANTUM CIRCUITS FOR MACHINE LEARNING 5. (LABORATORY 2 HOURS): BUILDING A QUANTUM NEURAL NETWORK WITH PENNYLANE 6. (LABORATORY 2 HOURS): TRAINING AND OPTIMIZATION OF QNNS WITH HYBRID CLASSICAL-QUANTUM APPROACHES TEACHING UNIT 7: FINAL PROJECT (4H) (LECTURE/PRACTICE/LABORATORY HOURS 0/0/4) 1. (LABORATORY 2 HOURS): DESIGN, IMPLEMENTATION AND TESTING OF A QUANTUM MACHINE LEARNING ALGORITHM TOTALE LECTURE/PRACTICE/LABORATORY 30/0/18 |
| Teaching Methods | |
|---|---|
| THE COURSE INCLUDES LECTURES, CLASSROOM PRACTICE, AND LABORATORY ACTIVITIES. DURING CLASSROOM RECITATION, THE MAIN FEATURES OF THE CONSIDERED MODEL IN DEVELOPING THE FINAL PROJECT ARE PRESENTED AND DISCUSSED. IN THE LAB, THE STUDENTS ARE GROUPED INTO TEAMS, AND EACH TEAM MUST DESIGN AND IMPLEMENT A SOLUTION FOR A PROBLEM THE TEAM HAS SELECTED AMONG THOSE PRESENTED DURING RECITATIONS OR PROPOSED BY THE TEAM ITSELF. |
| Verification of learning | |
|---|---|
| THE FINAL EVALUATION IS BY ORAL EXAMINATION AND THE PRESENTATION OF THE PROJECT. THE GRADE IS THE WEIGHTED SUM OF PROJECT CONTENT (50%), PROJECT PRESENTATION (20%) AND ORAL EXAMINATION (30%) |
| Texts | |
|---|---|
| - NIELSEN, M.A. & CHUANG, I.L. QUANTUM COMPUTATION AND QUANTUM INFORMATION. CAMBRIDGE UNIVERSITY PRESS. - DE WOLF, R. QUANTUM COMPUTING: LECTURE NOTES. |
| More Information | |
|---|---|
| THE COURSE IS HELD IN ITALIAN THE TEACHING MATERIAL IS AVAILABLE ON THE UNIVERSITY E-LEARNING PLATFORM (HTTP://ELEARNING.UNISA.IT) ACCESSIBLE TO STUDENTS USING THEIR OWN UNIVERSITY CREDENTIALS. |
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