Near-term Quantum Computing

• Lecturer: Dr. Tobias Stollenwerk
• Teaching Assistant: Nil Rodellas Gracia
• Host institution: RWTH Aachen, Physics Department (Prof. Dr. Hendrik Bluhm)

Overview

This lecture is an introduction to modern algorithmic challenges as they occur in the usage of near-term quantum computing hardware. After reviewing the basics of quantum computation and algorithms we will cover the limitation of current quantum computing hardware. Next, we will discuss algorithms for near-term quantum computers, before we turn to quantum compilation, i.e. algorithms for the optimal mapping of quantum algorithms to real quantum computers. Finally, we will cover the basics of quantum error correction and mitigation.

• Target audience: Master and PhD students in physics, computer science, mathematics
• Scope: 3h/week lecture, 1h/week programming exercise
• ETCP: 6
• Requirements:
• Linear algebra
• Quantum information/computing fundamentals

• Time and Place
• Thu, 16:30 - 18:00, Lecture, Room 1080, Schinkelstr. 1, Templergraben 51
• Fri, 10:30 - 12:00, Lecture and Practice, Room 1080, Schinkelstr. 1, Templergraben 51
• Lecture notes (see Moodle)

Literature

Fundamentals

• Phillip Kaye, Raymond Laflamme, and Michele Mosca. An Introduction to Quantum Computing. USA: Oxford University Press, Inc., 2007. isbn: 0198570007.
• Michael A. Nielsen and Isaac L. Chuang. Quantum Computation and Quantum Information: 10th Anniversary Edition. 10th. USA: Cambridge University Press, 2011. isbn: 1107002176.
• Eleanor Rieffel and Wolfgang Polak. Quantum Computing: A Gentle Introduction. 1st. The MIT Press, 2011. isbn: 9780262015066.

Near-term Algorithms

• Edward Farhi, Jeffrey Goldstone, and Sam Gutmann. “A quantum approximate optimization algorithm”. In: arXiv preprint arXiv:1411.4028 (2014).
• Alberto Peruzzo et al. “A variational eigenvalue solver on a photonic quantum processor”. In: Nature communications 5.1 (2014), pp. 1–7. doi: 10.1038/ncomms5213.
• John Preskill. “Quantum computing in the NISQ era and beyond”. In: Quantum 2 (2018), p. 79.
• Stuart Hadfield et al. “From the Quantum Approximate Optimization Algorithm to a Quantum Alternating Operator Ansatz”. In: Algorithms 12.2 (2019). issn: 1999-4893. doi: 10 .3390/a12020034.
• Marco Cerezo et al. “Variational quantum algorithms”. In: Nature Reviews Physics 3.9 (2021), pp. 625–644.
• M. Schuld and F. Petruccione. Supervised Learning with Quantum Computers. Quantum Science and Technology. Springer International Publishing, 2018. isbn: 9783319964249.