The student will acquire the basic knowledge on how the construction of a nuclear physics experiment is structured according to the collection of data from the detector, the control of the equipment and the experiment, and the quality of the acquired data. The simulation of simple hardware components in laboratory sessions will be introduced.
Curriculum
teacher profile teaching materials
The second part of the course focuses on scientific programming in Python, featuring laboratory exercises aimed at data acquisition and processing, as well as the control of a robotic arm.
Machine learning principles will then be introduced, starting with Ising models—which are also used in trigger and event selection applications within physics experiments. Practical exercises will address classification problems and the training of machine learning models.
The course concludes with an introduction to quantum computing using the IBM Quantum simulator and real quantum systems, demonstrating the basics of quantum machine learning and its elementary applications in data classification.
Expected learning outcomes: By the end of the course, students will have acquired fundamental skills in data acquisition systems, digital electronics, Python programming, core machine learning techniques, and the principles of quantum computing and quantum machine learning.
Recommended reference and supplementary texts include:
a) M. A. Nielsen, I. L. Chuang, *Quantum Computation and Quantum Information*, Cambridge University Press.
b) J. Watrous, *Understanding Quantum Information and Computation*, Cambridge University Press.
c) Lecture notes from the Digital Electronics course.
d) Notes and teaching materials from the course on Data Acquisition (DAQ) and Trigger systems.
In any case, the material provided by the instructor will be sufficient for exam preparation and for carrying out laboratory activities.
Programme
The course introduces the main data acquisition systems and the fundamentals of digital electronics, covering everything from Boolean algebra and logic gates to memory units and processors. Basic concepts of quantum computing and quantum logic gates will also be presented.The second part of the course focuses on scientific programming in Python, featuring laboratory exercises aimed at data acquisition and processing, as well as the control of a robotic arm.
Machine learning principles will then be introduced, starting with Ising models—which are also used in trigger and event selection applications within physics experiments. Practical exercises will address classification problems and the training of machine learning models.
The course concludes with an introduction to quantum computing using the IBM Quantum simulator and real quantum systems, demonstrating the basics of quantum machine learning and its elementary applications in data classification.
Expected learning outcomes: By the end of the course, students will have acquired fundamental skills in data acquisition systems, digital electronics, Python programming, core machine learning techniques, and the principles of quantum computing and quantum machine learning.
Core Documentation
For topics related to digital electronics, data acquisition (DAQ) and trigger systems, and the introduction to quantum computing, the instructor will provide lecture notes and supplementary teaching materials.Recommended reference and supplementary texts include:
a) M. A. Nielsen, I. L. Chuang, *Quantum Computation and Quantum Information*, Cambridge University Press.
b) J. Watrous, *Understanding Quantum Information and Computation*, Cambridge University Press.
c) Lecture notes from the Digital Electronics course.
d) Notes and teaching materials from the course on Data Acquisition (DAQ) and Trigger systems.
In any case, the material provided by the instructor will be sufficient for exam preparation and for carrying out laboratory activities.
Attendance
Attendance isn't mandatory, but it's always highly recommendedType of evaluation
Assessment will consist of a practical test and an oral exam, both held on the same day. The practical test aims to evaluate the skills acquired in programming, data analysis, and the laboratory activities conducted during the course. The oral exam will cover the theoretical topics addressed, with particular emphasis on data acquisition systems, digital electronics, machine learning, and the fundamentals of quantum computing. The final grade will be based on the overall performance in both tests. teacher profile teaching materials
The second part of the course focuses on scientific programming in Python, featuring laboratory exercises aimed at data acquisition and processing, as well as the control of a robotic arm.
Machine learning principles will then be introduced, starting with Ising models—which are also used in trigger and event selection applications within physics experiments. Practical exercises will address classification problems and the training of machine learning models.
The course concludes with an introduction to quantum computing using the IBM Quantum simulator and real quantum systems, demonstrating the basics of quantum machine learning and its elementary applications in data classification.
Expected learning outcomes: By the end of the course, students will have acquired fundamental skills in data acquisition systems, digital electronics, Python programming, core machine learning techniques, and the principles of quantum computing and quantum machine learning.
Recommended reference and supplementary texts include:
a) M. A. Nielsen, I. L. Chuang, *Quantum Computation and Quantum Information*, Cambridge University Press.
b) J. Watrous, *Understanding Quantum Information and Computation*, Cambridge University Press.
c) Lecture notes from the Digital Electronics course.
d) Notes and teaching materials from the course on Data Acquisition (DAQ) and Trigger systems.
In any case, the material provided by the instructor will be sufficient for exam preparation and for carrying out laboratory activities.
Programme
The course introduces the main data acquisition systems and the fundamentals of digital electronics, covering everything from Boolean algebra and logic gates to memory units and processors. Basic concepts of quantum computing and quantum logic gates will also be presented.The second part of the course focuses on scientific programming in Python, featuring laboratory exercises aimed at data acquisition and processing, as well as the control of a robotic arm.
Machine learning principles will then be introduced, starting with Ising models—which are also used in trigger and event selection applications within physics experiments. Practical exercises will address classification problems and the training of machine learning models.
The course concludes with an introduction to quantum computing using the IBM Quantum simulator and real quantum systems, demonstrating the basics of quantum machine learning and its elementary applications in data classification.
Expected learning outcomes: By the end of the course, students will have acquired fundamental skills in data acquisition systems, digital electronics, Python programming, core machine learning techniques, and the principles of quantum computing and quantum machine learning.
Core Documentation
For topics related to digital electronics, data acquisition (DAQ) and trigger systems, and the introduction to quantum computing, the instructor will provide lecture notes and supplementary teaching materials.Recommended reference and supplementary texts include:
a) M. A. Nielsen, I. L. Chuang, *Quantum Computation and Quantum Information*, Cambridge University Press.
b) J. Watrous, *Understanding Quantum Information and Computation*, Cambridge University Press.
c) Lecture notes from the Digital Electronics course.
d) Notes and teaching materials from the course on Data Acquisition (DAQ) and Trigger systems.
In any case, the material provided by the instructor will be sufficient for exam preparation and for carrying out laboratory activities.
Attendance
Attendance isn't mandatory, but it's always highly recommendedType of evaluation
Assessment will consist of a practical test and an oral exam, both held on the same day. The practical test aims to evaluate the skills acquired in programming, data analysis, and the laboratory activities conducted during the course. The oral exam will cover the theoretical topics addressed, with particular emphasis on data acquisition systems, digital electronics, machine learning, and the fundamentals of quantum computing. The final grade will be based on the overall performance in both tests. teacher profile teaching materials
The second part of the course focuses on scientific programming in Python, featuring laboratory exercises aimed at data acquisition and processing, as well as the control of a robotic arm.
Machine learning principles will then be introduced, starting with Ising models—which are also used in trigger and event selection applications within physics experiments. Practical exercises will address classification problems and the training of machine learning models.
The course concludes with an introduction to quantum computing using the IBM Quantum simulator and real quantum systems, demonstrating the basics of quantum machine learning and its elementary applications in data classification.
Expected learning outcomes: By the end of the course, students will have acquired fundamental skills in data acquisition systems, digital electronics, Python programming, core machine learning techniques, and the principles of quantum computing and quantum machine learning.
Recommended reference and supplementary texts include:
a) M. A. Nielsen, I. L. Chuang, *Quantum Computation and Quantum Information*, Cambridge University Press.
b) J. Watrous, *Understanding Quantum Information and Computation*, Cambridge University Press.
c) Lecture notes from the Digital Electronics course.
d) Notes and teaching materials from the course on Data Acquisition (DAQ) and Trigger systems.
In any case, the material provided by the instructor will be sufficient for exam preparation and for carrying out laboratory activities.
Mutuazione: 20401070 ACQUISIZIONE DATI E CONTROLLO DI ESPERIMENTI in Fisica LM-17 R Branchini Paolo
Programme
The course introduces the main data acquisition systems and the fundamentals of digital electronics, covering everything from Boolean algebra and logic gates to memory units and processors. Basic concepts of quantum computing and quantum logic gates will also be presented.The second part of the course focuses on scientific programming in Python, featuring laboratory exercises aimed at data acquisition and processing, as well as the control of a robotic arm.
Machine learning principles will then be introduced, starting with Ising models—which are also used in trigger and event selection applications within physics experiments. Practical exercises will address classification problems and the training of machine learning models.
The course concludes with an introduction to quantum computing using the IBM Quantum simulator and real quantum systems, demonstrating the basics of quantum machine learning and its elementary applications in data classification.
Expected learning outcomes: By the end of the course, students will have acquired fundamental skills in data acquisition systems, digital electronics, Python programming, core machine learning techniques, and the principles of quantum computing and quantum machine learning.
Core Documentation
For topics related to digital electronics, data acquisition (DAQ) and trigger systems, and the introduction to quantum computing, the instructor will provide lecture notes and supplementary teaching materials.Recommended reference and supplementary texts include:
a) M. A. Nielsen, I. L. Chuang, *Quantum Computation and Quantum Information*, Cambridge University Press.
b) J. Watrous, *Understanding Quantum Information and Computation*, Cambridge University Press.
c) Lecture notes from the Digital Electronics course.
d) Notes and teaching materials from the course on Data Acquisition (DAQ) and Trigger systems.
In any case, the material provided by the instructor will be sufficient for exam preparation and for carrying out laboratory activities.
Attendance
Attendance isn't mandatory, but it's always highly recommendedType of evaluation
Assessment will consist of a practical test and an oral exam, both held on the same day. The practical test aims to evaluate the skills acquired in programming, data analysis, and the laboratory activities conducted during the course. The oral exam will cover the theoretical topics addressed, with particular emphasis on data acquisition systems, digital electronics, machine learning, and the fundamentals of quantum computing. The final grade will be based on the overall performance in both tests. teacher profile teaching materials
The second part of the course focuses on scientific programming in Python, featuring laboratory exercises aimed at data acquisition and processing, as well as the control of a robotic arm.
Machine learning principles will then be introduced, starting with Ising models—which are also used in trigger and event selection applications within physics experiments. Practical exercises will address classification problems and the training of machine learning models.
The course concludes with an introduction to quantum computing using the IBM Quantum simulator and real quantum systems, demonstrating the basics of quantum machine learning and its elementary applications in data classification.
Expected learning outcomes: By the end of the course, students will have acquired fundamental skills in data acquisition systems, digital electronics, Python programming, core machine learning techniques, and the principles of quantum computing and quantum machine learning.
Recommended reference and supplementary texts include:
a) M. A. Nielsen, I. L. Chuang, *Quantum Computation and Quantum Information*, Cambridge University Press.
b) J. Watrous, *Understanding Quantum Information and Computation*, Cambridge University Press.
c) Lecture notes from the Digital Electronics course.
d) Notes and teaching materials from the course on Data Acquisition (DAQ) and Trigger systems.
In any case, the material provided by the instructor will be sufficient for exam preparation and for carrying out laboratory activities.
Mutuazione: 20401070 ACQUISIZIONE DATI E CONTROLLO DI ESPERIMENTI in Fisica LM-17 R Branchini Paolo
Programme
The course introduces the main data acquisition systems and the fundamentals of digital electronics, covering everything from Boolean algebra and logic gates to memory units and processors. Basic concepts of quantum computing and quantum logic gates will also be presented.The second part of the course focuses on scientific programming in Python, featuring laboratory exercises aimed at data acquisition and processing, as well as the control of a robotic arm.
Machine learning principles will then be introduced, starting with Ising models—which are also used in trigger and event selection applications within physics experiments. Practical exercises will address classification problems and the training of machine learning models.
The course concludes with an introduction to quantum computing using the IBM Quantum simulator and real quantum systems, demonstrating the basics of quantum machine learning and its elementary applications in data classification.
Expected learning outcomes: By the end of the course, students will have acquired fundamental skills in data acquisition systems, digital electronics, Python programming, core machine learning techniques, and the principles of quantum computing and quantum machine learning.
Core Documentation
For topics related to digital electronics, data acquisition (DAQ) and trigger systems, and the introduction to quantum computing, the instructor will provide lecture notes and supplementary teaching materials.Recommended reference and supplementary texts include:
a) M. A. Nielsen, I. L. Chuang, *Quantum Computation and Quantum Information*, Cambridge University Press.
b) J. Watrous, *Understanding Quantum Information and Computation*, Cambridge University Press.
c) Lecture notes from the Digital Electronics course.
d) Notes and teaching materials from the course on Data Acquisition (DAQ) and Trigger systems.
In any case, the material provided by the instructor will be sufficient for exam preparation and for carrying out laboratory activities.
Attendance
Attendance isn't mandatory, but it's always highly recommendedType of evaluation
Assessment will consist of a practical test and an oral exam, both held on the same day. The practical test aims to evaluate the skills acquired in programming, data analysis, and the laboratory activities conducted during the course. The oral exam will cover the theoretical topics addressed, with particular emphasis on data acquisition systems, digital electronics, machine learning, and the fundamentals of quantum computing. The final grade will be based on the overall performance in both tests. teacher profile teaching materials
The second part of the course focuses on scientific programming in Python, featuring laboratory exercises aimed at data acquisition and processing, as well as the control of a robotic arm.
Machine learning principles will then be introduced, starting with Ising models—which are also used in trigger and event selection applications within physics experiments. Practical exercises will address classification problems and the training of machine learning models.
The course concludes with an introduction to quantum computing using the IBM Quantum simulator and real quantum systems, demonstrating the basics of quantum machine learning and its elementary applications in data classification.
Expected learning outcomes: By the end of the course, students will have acquired fundamental skills in data acquisition systems, digital electronics, Python programming, core machine learning techniques, and the principles of quantum computing and quantum machine learning.
Recommended reference and supplementary texts include:
a) M. A. Nielsen, I. L. Chuang, *Quantum Computation and Quantum Information*, Cambridge University Press.
b) J. Watrous, *Understanding Quantum Information and Computation*, Cambridge University Press.
c) Lecture notes from the Digital Electronics course.
d) Notes and teaching materials from the course on Data Acquisition (DAQ) and Trigger systems.
In any case, the material provided by the instructor will be sufficient for exam preparation and for carrying out laboratory activities.
Programme
The course introduces the main data acquisition systems and the fundamentals of digital electronics, covering everything from Boolean algebra and logic gates to memory units and processors. Basic concepts of quantum computing and quantum logic gates will also be presented.The second part of the course focuses on scientific programming in Python, featuring laboratory exercises aimed at data acquisition and processing, as well as the control of a robotic arm.
Machine learning principles will then be introduced, starting with Ising models—which are also used in trigger and event selection applications within physics experiments. Practical exercises will address classification problems and the training of machine learning models.
The course concludes with an introduction to quantum computing using the IBM Quantum simulator and real quantum systems, demonstrating the basics of quantum machine learning and its elementary applications in data classification.
Expected learning outcomes: By the end of the course, students will have acquired fundamental skills in data acquisition systems, digital electronics, Python programming, core machine learning techniques, and the principles of quantum computing and quantum machine learning.
Core Documentation
For topics related to digital electronics, data acquisition (DAQ) and trigger systems, and the introduction to quantum computing, the instructor will provide lecture notes and supplementary teaching materials.Recommended reference and supplementary texts include:
a) M. A. Nielsen, I. L. Chuang, *Quantum Computation and Quantum Information*, Cambridge University Press.
b) J. Watrous, *Understanding Quantum Information and Computation*, Cambridge University Press.
c) Lecture notes from the Digital Electronics course.
d) Notes and teaching materials from the course on Data Acquisition (DAQ) and Trigger systems.
In any case, the material provided by the instructor will be sufficient for exam preparation and for carrying out laboratory activities.
Attendance
Attendance isn't mandatory, but it's always highly recommendedType of evaluation
Assessment will consist of a practical test and an oral exam, both held on the same day. The practical test aims to evaluate the skills acquired in programming, data analysis, and the laboratory activities conducted during the course. The oral exam will cover the theoretical topics addressed, with particular emphasis on data acquisition systems, digital electronics, machine learning, and the fundamentals of quantum computing. The final grade will be based on the overall performance in both tests. teacher profile teaching materials
The second part of the course focuses on scientific programming in Python, featuring laboratory exercises aimed at data acquisition and processing, as well as the control of a robotic arm.
Machine learning principles will then be introduced, starting with Ising models—which are also used in trigger and event selection applications within physics experiments. Practical exercises will address classification problems and the training of machine learning models.
The course concludes with an introduction to quantum computing using the IBM Quantum simulator and real quantum systems, demonstrating the basics of quantum machine learning and its elementary applications in data classification.
Expected learning outcomes: By the end of the course, students will have acquired fundamental skills in data acquisition systems, digital electronics, Python programming, core machine learning techniques, and the principles of quantum computing and quantum machine learning.
Recommended reference and supplementary texts include:
a) M. A. Nielsen, I. L. Chuang, *Quantum Computation and Quantum Information*, Cambridge University Press.
b) J. Watrous, *Understanding Quantum Information and Computation*, Cambridge University Press.
c) Lecture notes from the Digital Electronics course.
d) Notes and teaching materials from the course on Data Acquisition (DAQ) and Trigger systems.
In any case, the material provided by the instructor will be sufficient for exam preparation and for carrying out laboratory activities.
Mutuazione: 20401070 ACQUISIZIONE DATI E CONTROLLO DI ESPERIMENTI in Fisica LM-17 R Branchini Paolo
Programme
The course introduces the main data acquisition systems and the fundamentals of digital electronics, covering everything from Boolean algebra and logic gates to memory units and processors. Basic concepts of quantum computing and quantum logic gates will also be presented.The second part of the course focuses on scientific programming in Python, featuring laboratory exercises aimed at data acquisition and processing, as well as the control of a robotic arm.
Machine learning principles will then be introduced, starting with Ising models—which are also used in trigger and event selection applications within physics experiments. Practical exercises will address classification problems and the training of machine learning models.
The course concludes with an introduction to quantum computing using the IBM Quantum simulator and real quantum systems, demonstrating the basics of quantum machine learning and its elementary applications in data classification.
Expected learning outcomes: By the end of the course, students will have acquired fundamental skills in data acquisition systems, digital electronics, Python programming, core machine learning techniques, and the principles of quantum computing and quantum machine learning.
Core Documentation
For topics related to digital electronics, data acquisition (DAQ) and trigger systems, and the introduction to quantum computing, the instructor will provide lecture notes and supplementary teaching materials.Recommended reference and supplementary texts include:
a) M. A. Nielsen, I. L. Chuang, *Quantum Computation and Quantum Information*, Cambridge University Press.
b) J. Watrous, *Understanding Quantum Information and Computation*, Cambridge University Press.
c) Lecture notes from the Digital Electronics course.
d) Notes and teaching materials from the course on Data Acquisition (DAQ) and Trigger systems.
In any case, the material provided by the instructor will be sufficient for exam preparation and for carrying out laboratory activities.
Attendance
Attendance isn't mandatory, but it's always highly recommendedType of evaluation
Assessment will consist of a practical test and an oral exam, both held on the same day. The practical test aims to evaluate the skills acquired in programming, data analysis, and the laboratory activities conducted during the course. The oral exam will cover the theoretical topics addressed, with particular emphasis on data acquisition systems, digital electronics, machine learning, and the fundamentals of quantum computing. The final grade will be based on the overall performance in both tests.