All Outputs (2)

Quantum optimization with linear Ising penalty functions for customer data science (2024)
Journal Article
Mirkarimi, P., Shukla, I., Hoyle, D. C., Williams, R., & Chancellor, N. (2024). Quantum optimization with linear Ising penalty functions for customer data science. Physical Review Research, 6(4), Article 043241. https://doi.org/10.1103/physrevresearch.6.043241

Constrained combinatorial optimization problems, which are ubiquitous in industry, can be solved by quantum algorithms such as quantum annealing (QA) and the quantum approximate optimization algorithm (QAOA). In these quantum algorithms, constraints... Read More about Quantum optimization with linear Ising penalty functions for customer data science.

Experimental demonstration of improved quantum optimization with linear Ising penalties (2024)
Journal Article
Mirkarimi, P., Hoyle, D. C., Williams, R., & Chancellor, N. (2024). Experimental demonstration of improved quantum optimization with linear Ising penalties. New Journal of Physics, 26(10), Article 103005. https://doi.org/10.1088/1367-2630/ad7e4a

The standard approach to encoding constraints in quantum optimization is the quadratic penalty method. Quadratic penalties introduce additional couplings and energy scales, which can be detrimental to the performance of a quantum optimizer. In quantu... Read More about Experimental demonstration of improved quantum optimization with linear Ising penalties.