Learning resources

Optimization learning resources

The following publications might be good starting points to understand the mathematical principles behind the solvers:

• Glover F., Kochenberger G., Du Y. (2019). A Tutorial on Formulating and Using QUBO Models.
• Lucas, Andrew. (2014) Ising formulations of many NP problems. Frontiers in Physics

Quantum computing learning resources

While the tools discussed in the quantum computing concepts section are foundational for any developer of quantum software, they by no means span the depth or breadth of what is known about quantum computer programming and algorithm design. Since quantum computing remains a rapidly developing field, there is no one resource that has all of the information needed to learn how to best use these tools in order to solve problems. For this reason we have compiled a reference list that may interest the reader who wishes to learn more about the art and beauty of quantum computer programming. This section contains selected references to deep coverage of quantum computing topics.

Basic quantum computing

• Nielsen, M. A. & Chuang, I. L. (2010). Quantum Computation and Quantum Information. Quantum Computation and Quantum Information, UK: Cambridge University Press, 2010.
• Kaye, P., Laflamme, R., & Mosca, M. (2007). An introduction to quantum computing. Oxford University Press.
• Rieffel, E. G., & Polak, W. H. (2011). Quantum computing: A gentle introduction. MIT Press.
• Sarah C. Kaiser and Christopher E. Granade (Manning Early Access Program began April 2019 Publication in Fall 2020). Learn Quantum Computing with Python and Q# - A hands-on approach.

Community made content

• Awesome qsharp: An open source list of Q# code and resources.
• Quantum computing StackExchange: an online community for quantum developers to learn, and share their knowledge.
• Subreddit for the Q# programming language: an online community in Reddit to discuss the latest news and developments of Q#.
• Subreddit for the quantum computing: an online community in Reddit to discuss the latest news and developments of quantum computing.
• Q# Community: A community group that collects and maintains projects related to the Q# programming language by a community of folks who are excited about quantum programming.

Online Courses

• MS Learn Quantum Computing modules. A ste-by-step tutorial of quantum computing concepts and practices.
• Quantum Computing - Brilliant Course. Learn to build quantum algorithms from the ground up with a quantum computer simulated in your browser.
• Introduction to Quantum Computing - LinkedIn Learning. 1h25m video introduction.
• Quantum Computing through Comics. Sunday weekly hands-on community class.

Advanced topics

Elementary techniques for building controlled gates

• Barenco, A., Bennett, C. H., Cleve, R., DiVincenzo, D. P., Margolus, N., Shor, P., ... & Weinfurter, H. (1995). Elementary gates for quantum computation. Physical Review A, 52(5), 3457.
• Jones, C. (2013). Low-overhead constructions for the fault-tolerant Toffoli gate. Physical Review A, 87(2), 022328

Techniques for preparing quantum states

• Shende, V. V., Markov, I. L., & Bullock, S. S. (2004). Minimal universal two-qubit controlled-NOT-based circuits. Physical Review A, 69(6), 062321.
• Ozols, M., Roetteler, M., & Roland, J. (2013). Quantum rejection sampling. ACM Transactions on Computation Theory (TOCT), 5(3), 11.
• Grover, L., & Rudolph, T. (2002). Creating superpositions that correspond to efficiently integrable probability distributions. arXiv preprint quant-ph/0208112.
• Farhi, E., Goldstone, J., Gutmann, S., & Sipser, M. (2000). Quantum computation by adiabatic evolution. arXiv preprint quant-ph/0001106.

Approaches for synthesizing circuits out of H, T and CNOT gates

• Kliuchnikov, V., Maslov, D., & Mosca, M. (2013). Asymptotically optimal approximation of single qubit unitaries by Clifford and T circuits using a constant number of ancillary qubits. Physical Review Letters, 110(19), 190502.
• Ross, N. J., & Selinger, P. (2014). Optimal ancilla-free Clifford+ T approximation of z-rotations. arXiv preprint arXiv:1403.2975.
• Kliuchnikov, V. (2013). Synthesis of unitaries with Clifford+ T circuits. arXiv preprint arXiv:1306.3200.
• Jones, N. C., Whitfield, J. D., McMahon, P. L., Yung, M. H., Van Meter, R., Aspuru-Guzik, A., & Yamamoto, Y. (2012). Faster quantum chemistry simulation on fault-tolerant quantum computers. New Journal of Physics, 14(11), 115023.

Approaches for quantum arithmetic

• Takahashi, Y., & Kunihiro, N. (2005). A linear-size quantum circuit for addition with no ancillary qubits. Quantum Information & Computation, 5(6), 440-448.
• Draper, T. G. (2000). Addition on a quantum computer. arXiv preprint quant-ph/0008033.
• Soeken, M., Roetteler, M., Wiebe, N., & De Micheli, G. (2017, March). Design automation and design space exploration for quantum computers. In 2017 Design, Automation & Test in Europe Conference & Exhibition (DATE) (pp. 470-475). IEEE.

Methods for fast quantum sampling (amplitude amplification) and probability estimation

• Brassard, G., Hoyer, P., Mosca, M., & Tapp, A. (2002). Quantum amplitude amplification and estimation. Contemporary Mathematics, 305, 53-74.
• Grover, L. K. (2005). Fixed-point quantum search. Physical Review Letters, 95(15), 150501.
• Berry, D. W., Childs, A. M., & Kothari, R. (2015, October). Hamiltonian simulation with nearly optimal dependence on all parameters. In Foundations of Computer Science (FOCS), 2015 IEEE 56th Annual Symposium on (pp. 792-809). IEEE.

Algorithms for quantum simulation

• Lloyd, S. (1996). Universal Quantum Simulators. Science (New York, NY), 273(5278), 1073.
• Berry, D. W., Childs, A. M., Cleve, R., Kothari, R., & Somma, R. D. (2015). Simulating Hamiltonian dynamics with a truncated Taylor series. Physical Review Letters, 114(9), 090502.
• Low, G. H., & Chuang, I. L. (2017). Optimal Hamiltonian simulation by quantum signal processing. Physical Review Letters, 118(1), 010501.
• Low, G. H., & Chuang, I. L. (2016). Hamiltonian simulation by qubitization. arXiv preprint:1610.06546.
• Reiher, M., Wiebe, N., Svore, K. M., Wecker, D., & Troyer, M. (2017). Elucidating reaction mechanisms on quantum computers. Proceedings of the National Academy of Sciences, 201619152.
• Wiebe, N., Berry, D. W., Høyer, P., & Sanders, B. C. (2011). Simulating quantum dynamics on a quantum computer. Journal of Physics A: Mathematical and Theoretical, 44(44), 445308.
• Peruzzo, A., McClean, J., Shadbolt, P., Yung, M. H., Zhou, X. Q., Love, P. J., ... & Obrien, J. L. (2014). A variational eigenvalue solver on a photonic quantum processor. Nature communications, 5.

Procedures for quantum optimization

• Durr, C., & Høyer, P. (1996). A quantum algorithm for finding the minimum. arXiv preprint quantph/9607014.
• Farhi, E., Goldstone, J., & Gutmann, S. (2014). A quantum approximate optimization algorithm. arXiv preprint arXiv:1411.4028.
• Brandao, F. G., Svore, K. M. (2017). Quantum Speed-ups for Semidefinite Programming. In Annual Symposium on Foundations of Computer Science (FOCS 2017).