01HGHUU

A.A. 2023/24

Course Language

Inglese

Degree programme(s)

Course structure

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Lecturers

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Co-lectuers

Context

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2023/24

QUANTUM HARDWARE DESIGN AND OPTIMIZATION:
The aim is to offer an overview of the building blocks of a quantum computer and of the methods adopted to implement it. The focus starts from the knowledge of a physical QBit and examines the design parameters to be tackled to implement a real QComputer dealing with decoherence and noise when the number o QBits increases. Afterwords the goal is to step to elementary quantum gates based on two or more Qbits and on their combination used to actually implement simple algorithms. Furthermore, starting from the QBit and Qbit gates model examined, some algorithms will be implemented to test the model functionality. The problem will then be further scaled for the resolution of algorithms by integrating classical and quantum computation through FPGAs-based emulators with increasing complexity.
QUANTUM COMPUTING:
The Quantum Computing (QC) course is a mandatory course belonging to the Master Degree in Quantum Engineering; it is taught in the first semester of the second year, and it is taught in English.
The main goal of the course is to provide the students with a knowledge of the software applications of quantum computing, by means of integrated business hw/sw platforms.
The purpose is to show what it is actually possible to do with a quantum computer.
During the course, will be provided contributions from companies with whom Politecnico di Torino is involved for research and development projects, such as IBM, Intesa Sanpaolo, TIM, Pasqal, Fondazione Links.

QUANTUM HARDWARE DESIGN AND OPTIMIZATION:
The aim is to offer an overview of the building blocks of a quantum computer and of the methods adopted to implement it. The focus starts from the knowledge of a physical QBit and examines the design parameters to be tackled to implement a real QComputer dealing with decoherence and noise when the number o QBits increases. Afterwords the goal is to step to elementary quantum gates based on two or more Qbits and on their combination used to actually implement simple algorithms. Furthermore, starting from the QBit and Qbit gates model examined, some algorithms will be implemented to test the model functionality. The problem will then be further scaled for the resolution of algorithms by integrating classical and quantum computation through FPGAs-based emulators with increasing complexity.
QUANTUM COMPUTING:
The Quantum Computing (QC) course is a mandatory course belonging to the Master Degree in Quantum Engineering; it is taught in the first semester of the second year, and it is taught in English.
The main goal of the course is to provide the students with a knowledge of the software applications of quantum computing, by means of integrated business hw/sw platforms.
The purpose is to show what it is actually possible to do with a quantum computer.
During the course, will be provided contributions from companies with whom Politecnico di Torino is involved for research and development projects, such as IBM, Intesa Sanpaolo, TIM, Pasqal, Fondazione Links.

QUANTUM HARDWARE DESIGN AND OPTIMIZATION:
The student wil know how to implementation on a hardware basis the quantum gates and circuits; this ability will be deployed on realistic qbits and multi-qubit array both in ideal and non-ideal conditions.
The student will have the ability to simulate functionally simple quantum circuits using simulators available at teaching level.
The student will have the ability to compile quantum algorithms of simple and medium complecity, holding first of all a solid methdology independently on the tye of algorithm.
The student will have the ability to implment a general algorithm on Field Progrmmable Gate Aarrays (FPGAs) and to deploy quantum algorthms emulator usign FPGAs
The sutdent wil hold the basic skills to integrate classical and quantum electronic architectures in an embedded or coherently interfaced hardware support.
QUANTUM COMPUTING:
- Basic knowledge of software platforms for quantum computers
- Detailed knowledge of IBM platform
- Detailed knowledge of D-Wave platform
- Knowledge of Pasqal platform
- Knowledge of main applications of quantum computing in finance, optimization, and telecommunications

QUANTUM HARDWARE DESIGN AND OPTIMIZATION:
The student wil know how to implementation on a hardware basis the quantum gates and circuits; this ability will be deployed on realistic qbits and multi-qubit array both in ideal and non-ideal conditions.
The student will have the ability to simulate functionally simple quantum circuits using simulators available at teaching level.
The student will have the ability to compile quantum algorithms of simple and medium complecity, holding first of all a solid methdology independently on the tye of algorithm.
The student will have the ability to implment a general algorithm on Field Progrmmable Gate Aarrays (FPGAs) and to deploy quantum algorthms emulator usign FPGAs
The sutdent wil hold the basic skills to integrate classical and quantum electronic architectures in an embedded or coherently interfaced hardware support.
QUANTUM COMPUTING:
- Basic knowledge of software platforms for quantum computers
- Detailed knowledge of IBM platform
- Detailed knowledge of D-Wave platform
- Knowledge of Pasqal platform
- Knowledge of main applications of quantum computing in finance, optimization, and telecommunications

QUANTUM HARDWARE DESIGN AND OPTIMIZATION:
The student is expected to have knowledges on:
Quantum information processing in terms of methods and tools
Fundamentals of Quantum algebra, both at theoretical and practical level, i.e. should be able to follow quantum algebra demostrations and to solve basic problems using quantum algebra methods
Qbit technology both in terns of materials and processes, and single Qbit physical implementation based on the major existing technologies
Critieria, problems and circuits for interfacing Qbit devices and gates to standard technologies for dirving and reading
Fundamentals of Quantum algorithms
QUANTUM COMPUTING:
- basic knowledge in computer sciences
- basic knowledge in Python programming and high level programming languages
- knowledge of the quantum gates from a hardware point of view
- knowledge of the main quantum algorithms (e.g. Shor)

QUANTUM HARDWARE DESIGN AND OPTIMIZATION: The student is expected to have knowledges on:
Quantum information processing in terms of methods and tools
Fundamentals of Quantum algebra, both at theoretical and practical level, i.e. should be able to follow quantum algebra demostrations and to solve basic problems using quantum algebra methods
Qbit technology both in terns of materials and processes, and single Qbit physical implementation based on the major existing technologies
Critieria, problems and circuits for interfacing Qbit devices and gates to standard technologies for dirving and reading
Fundamentals of Quantum algorithms
QUANTUM COMPUTING:
- basic knowledge in computer sciences
- basic knowledge in Python programming and high level programming languages
- knowledge of the quantum gates from a hardware point of view
- knowledge of the main quantum algorithms (e.g. Shor)

QUANTUM HARDWARE DESIGN AND OPTIMIZATION:
Universal set of gates for QC, Di Vincenzo's Criteria and ardware implementation of quantum gates and circuits on realistic qbits and multi-qubit array: ideal, non-ideal conditions, noise impact, temperature and defects. [1CFU]
Functional comparison through models and simulations of NMR Qbits, Josephson Junction Qbits, Trapped ions Qbits and Quantum Dots Qbits [1CFU]
Design of quantum circuits [1CFU]:
• Basic quantum algorithms implementation based on quantum gates
• Compilation of quantum circuits tailored for actual hardware specifications and comparison with classical compilation processes
• Technology-dependent gate optimizations and two-qubit gate templates
• Basics on CAD design for quantum circuits
Quantum computing emulation and integration with classical digital architectures [2CFU]:
• Fundamentals of classical digital circuits for FPGA
• Design of digital circuits to be synthesized on FPGAs for quantum computing emulation
• Simulation and characterization of emulated quantum circuits
Deployment of quantum-based algorithms and methodologies on quantum HW through Quadratic Unconstrained Binary Optimization (QUBO) for real-life problems. [1CFU]
QUANTUM COMPUTING:
- Introduction to software platforms for quantum computing (0,5 credits)
- IBM platform (1,5 credits)
- D-Wave platform (1,5 credits)
- Pasqal platform (1,5 credits)
- Applications (finance, optimization, telecommunications) (1 credit)
- Laboratory exercises will be done, most of the course will be hands-on.

QUANTUM HARDWARE DESIGN AND OPTIMIZATION:
Universal set of gates for QC, Di Vincenzo's Criteria and ardware implementation of quantum gates and circuits on realistic qbits and multi-qubit array: ideal, non-ideal conditions, noise impact, temperature and defects. [1CFU]
Functional comparison through models and simulations of NMR Qbits, Josephson Junction Qbits, Trapped ions Qbits and Quantum Dots Qbits [1CFU]
Design of quantum circuits [1CFU]:
• Basic quantum algorithms implementation based on quantum gates
• Compilation of quantum circuits tailored for actual hardware specifications and comparison with classical compilation processes
• Technology-dependent gate optimizations and two-qubit gate templates
• Basics on CAD design for quantum circuits
Quantum computing emulation and integration with classical digital architectures [2CFU]:
• Fundamentals of classical digital circuits for FPGA
• Design of digital circuits to be synthesized on FPGAs for quantum computing emulation
• Simulation and characterization of emulated quantum circuits
Deployment of quantum-based algorithms and methodologies on quantum HW through Quadratic Unconstrained Binary Optimization (QUBO) for real-life problems. [1CFU]
QUANTUM COMPUTING:
- Introduction to software platforms for quantum computing (0,5 credits)
- IBM platform (1,5 credits)
- D-Wave platform (1,5 credits)
- Pasqal platform (1,5 credits)
- Applications (finance, optimization, telecommunications) (1 credit)
- Laboratory exercises will be done, most of the course will be hands-on.

QUANTUM HARDWARE DESIGN AND OPTIMIZATION:
The course consists of theoretical lectures and an application part of exercises carried out in the laboratory with the aid of electronic boards and computer systems.
The experimental exercises involve the design of basic blocks defined starting from elementary cells and the analysis of their performance using simulators. They then foresee the description of more complex architectures through VHDL language.
The number of exercises foreseen is 3 and are carried out in the laboratory by a group of 3/4 students.
Each laboratory requires drafting a report which will contribute to the achievement of the final grade.
QUANTUM COMPUTING:
- Class lectures (also hands-on): 50% of the course duration;
- Extensive Class exercise time: 30% of the course duration;
- Assisted laboratories: 20% of the course duration.
Students are highly invited to interact with Lecturers, at lecture, exercise, and laboratory slots. Students are invited to take always with themselves their notebook for hands-on activities.

QUANTUM HARDWARE DESIGN AND OPTIMIZATION:
The course consists of theoretical lectures and an application part of exercises carried out in the laboratory with the aid of electronic boards and computer systems.
The experimental exercises involve the design of basic blocks defined starting from elementary cells and the analysis of their performance using simulators. They then foresee the description of more complex architectures through VHDL language.
The number of exercises foreseen is 3 and are carried out in the laboratory by a group of 3/4 students.
Each laboratory requires drafting a report which will contribute to the achievement of the final grade.
QUANTUM COMPUTING:
- Class lectures (also hands-on): 50% of the course duration;
- Extensive Class exercise time: 30% of the course duration;
- Assisted laboratories: 20% of the course duration.
Students are highly invited to interact with Lecturers, at lecture, exercise, and laboratory slots. Students are invited to take always with themselves their notebook for hands-on activities.

QUANTUM HARDWARE DESIGN AND OPTIMIZATION:
Suggested books:
• QuantumComputation and Quantum Information, M.Nielsen and I. Chuang, Cmabrige University Press
• Quantum Computing for Computer Sicentist, Yanofky, Mannucci, Cambridge University press
• Introducation to Classical and Quantum Computing, T.Wong
Copies of the slides used in the lessons and the manuals for the laboratory exercises are available. All the course material can be downloaded through the teaching portal.
QUANTUM COMPUTING:
• Learn Quantum Computing with Python and IBM Quantum Experience: A hands-on introduction to quantum computing and writing your own quantum programs with Python - Robert Loredo - Packt Publishing (28 settembre 2020) - ISBN-10 : 1838981004 - ISBN-13 : 978-1838981006
• Quantum Machine Learning: An Applied Approach: The Theory and Application of Quantum Machine Learning in Science and Industry – Santanu Ganguly - Apress (29 luglio 2021) - ISBN-10 : 1484270975 - ISBN-13 : 978-1484270974
• Quantum Machine Learning and Optimisation in Finance: On the Road to Quantum Advantage - Antoine Jacquier (Autore), Oleksiy Kondratyev (Autore), Alexander Lipton (Avanti), Marcos Lopez de Prado (Avanti) - Packt Publishing (31 ottobre 2022) - ISBN-10 : 1801813574 - ISBN-13 : 978-180181357
The three books are freely available for Politecnico di Torino students in the university library in digital format.

QUANTUM HARDWARE DESIGN AND OPTIMIZATION:
Suggested books:
• Quantum Computation and Quantum Information, M.Nielsen and I. Chuang, Cambridge University Press
• Quantum Computing for Computer Scientist, Yanofky, Mannucci, Cambridge University press
• Introduction to Classical and Quantum Computing, T.Wong
• Advanced Digital System Design using SoC FPGAs: An Integrated Hardware/Software Approach, R.K. Snider, Springer
Copies of the slides used in the lessons and the manuals for the laboratory exercises are available. All the course material can be downloaded through the teaching portal.
QUANTUM COMPUTING:
• Learn Quantum Computing with Python and IBM Quantum Experience: A hands-on introduction to quantum computing and writing your own quantum programs with Python - Robert Loredo - Packt Publishing (28 settembre 2020) - ISBN-10 : 1838981004 - ISBN-13 : 978-1838981006
• Quantum Machine Learning: An Applied Approach: The Theory and Application of Quantum Machine Learning in Science and Industry – Santanu Ganguly - Apress (29 luglio 2021) - ISBN-10 : 1484270975 - ISBN-13 : 978-1484270974
• Quantum Machine Learning and Optimisation in Finance: On the Road to Quantum Advantage - Antoine Jacquier (Autore), Oleksiy Kondratyev (Autore), Alexander Lipton (Avanti), Marcos Lopez de Prado (Avanti) - Packt Publishing (31 ottobre 2022) - ISBN-10 : 1801813574 - ISBN-13 : 978-180181357
The three books are freely available for Politecnico di Torino students in the university library in digital format.

...
QUANTUM HARDWARE DESIGN AND OPTIMIZATION:
The exam consists of two parts: an oral exam and the execution of laboratory exercises with relative reports.
The part linked to the laboratories weighs 20% of the final evaluation, while the oral discussion weighs 80%.
The oral exam will include a couple of questions, from which the understanding of the concepts and the maturity managed to elaborate on them are assessed.
A third more applicative question can be asked to evaluate the ability to exploit the skills acquired for problem-solving in more detail.
In the laboratory reports, the completeness and accuracy of the arguments, the organization and the conciseness of the report are evaluated.
QUANTUM COMPUTING:
The examination is composed of two parts, a written part and an assignment.
Written examination:
The written exam is composed of questions and exercises on the course content. It lasts for 1h. The maximum score for this part is 30/30. A sufficient mark is >= 18.
Assignment
Students will be asked to deliver a group assignment (usually 2 persons per group) to be developed at the end of the semester.
The maximum score for this part is 30/30. A sufficient mark is >= 18.
The final grade is the arithmetic mean of the two parts, provided that both of them are sufficient (>=18)
An optional oral examination can be asked to the teacher, in order to improve a mark of 30 and reach the 30 cum laude.
The teacher will also be able to ask for an oral exam independently of marks obtained in the written part and assignment or even in substitution of them in particular conditions.

Gli studenti e le studentesse con disabilità o con Disturbi Specifici di Apprendimento (DSA), oltre alla segnalazione tramite procedura informatizzata, sono invitati a comunicare anche direttamente al/la docente titolare dell'insegnamento, con un preavviso non inferiore ad una settimana dall'avvio della sessione d'esame, gli strumenti compensativi concordati con l'Unità Special Needs, al fine di permettere al/la docente la declinazione più idonea in riferimento alla specifica tipologia di esame.

QUANTUM HARDWARE DESIGN AND OPTIMIZATION:
The exam consists of two parts: an oral exam and the execution of laboratory exercises with relative reports.
The part linked to the laboratories weighs 20% of the final evaluation, while the oral discussion weighs 80%.
The oral exam will include a couple of questions, from which the understanding of the concepts and the maturity managed to elaborate on them are assessed.
A third more applicative question can be asked to evaluate the ability to exploit the skills acquired for problem-solving in more detail.
In the laboratory reports, the completeness and accuracy of the arguments, the organization and the conciseness of the report are evaluated.
QUANTUM COMPUTING:
The examination is composed of two parts, a written part and an assignment.
Written examination:
The written exam is composed of questions and exercises on the course content. It lasts for 1h. The maximum score for this part is 30/30. A sufficient mark is >= 18.
Assignment
Students will be asked to deliver a group assignment (usually 2 persons per group) to be developed at the end of the semester.
The maximum score for this part is 30/30. A sufficient mark is >= 18.
The final grade is the arithmetic mean of the two parts, provided that both of them are sufficient (>=18)
An optional oral examination can be asked to the teacher, in order to improve a mark of 30 and reach the 30 cum laude.
The teacher will also be able to ask for an oral exam independently of marks obtained in the written part and assignment or even in substitution of them in particular conditions.

In addition to the message sent by the online system, students with disabilities or Specific Learning Disorders (SLD) are invited to directly inform the professor in charge of the course about the special arrangements for the exam that have been agreed with the Special Needs Unit. The professor has to be informed at least one week before the beginning of the examination session in order to provide students with the most suitable arrangements for each specific type of exam.

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