2nd Cycle Studies - Mestrado
Basic knowledge of computer architecture and programming, digital systems, as well a mathematical background in probabilities, is welcome.
Lecture classes with detailed presentation, using audiovisual resources, of the concepts, principles and fundamental theories and solving of basic practical exercises to illustrate the practical interest of the subject and exemplify its application to real cases.
Laboratory classes, with lab assignments of growing complexity, initially with a tutorial set of instructions, but later fostering a more autonomou work, and the last assignment is already a full mini-project. Online reources will be used, such as IBM Q, Quantum Computing, (URL: https://www.ibm.com/quantum-computing/ , 2019)
The key learning outcomes are:
- Understand the classical deterministic computing implementations and probabilistic approaches on conventional hardware.
- Understand the differences between quantum and classical computation
- Discern potential performance gains of quantum vs. classical algorithms.
- Understand the mathematical description of quantum states and basic quantum operations.
- Understand engineering challenges currently faced by developers of quantum computers.
- Become proficient with engineering requirements for quantum vs classical algorithm implementation.
- Understand the scientific limits of quantum algorithms for optimisation.
- Implement quantum computing algorithms using currently available tools.
- Be well positioned to use emerging quantum technologies.
Analogue Computing, Stochastic Computing, and Deterministic Digital Systems
Moore’s Law and the Stored-program Computer
Parallel Computing, Hardware Accelerators and the limits of classical approaches
Quantum Computer as Accelerator and potential performance gains
History of Quantum Computing and approaches to building Qubits
Entanglement, Decoherence, and Robustness
Quantum Gates and Universal Quantum Algorithms
Quantum Simulation, Emulation and Annealing
:Quantum Computing Model and Tools:
Quantum Circuits and Gates, and Effects of Superposition and Entanglement
Quantum Software, IBM Q, Qiskit framework and development tools.
:Quantum computing applications:
RSA Cryptosystem, Factoring, and Shor’s Algorithm
Random Number Generators and Quantum Repeaters
Adiabatic Quantum Computing
Quantum Annealing & Polynomial-Speedup Quantum Algorithms
Quantum Computing for Machine Learning
Jorge Nuno de Almeida e Sousa Almada Lobo
Laboratory work or Field work: 30.0%
Bibliografia principal / key bibliography
• M. A. Nielsen and I. L. Chuang. Quantum Computation and Quantum Information (10th Anniversary Edition). Cambridge University Press, 2010
• E. Rieffel and W. Polak. Quantum Computing: A Gentle Introduction. MIT Press, 2011.
• Noson S. Yanofsky and Mirco A. Mannucci, 2008, Quantum Computing for Computer Scientists, Cambridge University Press.
• Learn Quantum Computing using Qiskit, Abraham Asfaw, Luciano Bello, Yael Ben-Haim, Sergey Bravyi, Lauren Capelluto, Almudena Carrera Vazquez, Jack Ceroni, Jay Gambetta, Shelly Garion, Leron Gil, Salvador De La Puente Gonzalez, David McKay, Zlatko Minev, Paul Nation, Anna Phan, Arthur Rattew, Javad Shabani, John Smolin, Kristan Temme, Madeleine Tod, James Wootton, URL: https://qiskit.org/textbook/preface.html , 2019