Quantum Approximate Optimization for Hard Problems in Linear Algebra
| dc.contributor.author | Borle, Ajinkya | |
| dc.contributor.author | Elfving, Vincent E. | |
| dc.contributor.author | Lomonaco, Samuel J. | |
| dc.date.accessioned | 2021-01-25T18:22:28Z | |
| dc.date.available | 2021-01-25T18:22:28Z | |
| dc.description.abstract | The Quantum Approximate Optimization Algorithm (QAOA) by Farhi et al. is a quantum computational framework for solving quantum or classical optimization tasks. Here, we explore using QAOA for Binary Linear Least Squares (BLLS); a problem that can serve as a building block of several other hard problems in linear algebra. Most of the previous efforts in quantum computing for solving these problems were done using the quantum annealing paradigm. For the scope of this work, our experiments were done on the QISKIT simulator and an IBM Q 5 qubit machine. We highlight the possibilities of using QAOA and QAOA-like variational algorithms for solving such problems, where trial solutions can be obtained directly as samples, rather than being amplitude-encoded in the quantum wavefunction. Our simulations show that Simulated Annealing can outperform QAOA for BLLS at a circuit depth of p≤3 for the probability of sampling the ground state. Finally, we point out some of the challenges involved in current-day experimental implementations of this technique on a cloud-based quantum computer | en_US |
| dc.description.sponsorship | The authors would like to thank the people who made this collaboration possible. From UMBC: Dean Drake, Wendy Martin and Cameron McAdams from the Office of the Vice President for Research (OVPR), the Office of Technology Development (OTD) and the Office of Sponsored Programs (OSP) respectively. From Qu & Co: we’d like to thank Benno Broer, CEO and co-founder of Qu & Co. This work was performed in part using IBM Quantum systems as part of the IBM Q Network. | en_US |
| dc.description.uri | https://arxiv.org/abs/2006.15438 | en_US |
| dc.format.extent | 16 pages | en_US |
| dc.genre | journal articles preprints | en_US |
| dc.identifier | doi:10.13016/m2aygc-kmbv | |
| dc.identifier.citation | Ajinkya Borle, Vincent E. Elfving and Samuel J. Lomonaco, Quantum Approximate Optimization for Hard Problems in Linear Algebra, https://arxiv.org/abs/2006.15438 | en_US |
| dc.identifier.uri | http://hdl.handle.net/11603/20603 | |
| dc.language.iso | en_US | en_US |
| dc.relation.isAvailableAt | The University of Maryland, Baltimore County (UMBC) | |
| dc.relation.ispartof | UMBC Computer Science and Electrical Engineering Department Collection | |
| dc.relation.ispartof | UMBC Faculty Collection | |
| dc.relation.ispartof | UMBC Student Collection | |
| dc.rights | This item is likely protected under Title 17 of the U.S. Copyright Law. Unless on a Creative Commons license, for uses protected by Copyright Law, contact the copyright holder or the author. | |
| dc.rights | Attribution 4.0 International | |
| dc.rights.uri | https://creativecommons.org/licenses/by/4.0/ | |
| dc.subject | quantum computing | en_US |
| dc.subject | quantum annealing | en_US |
| dc.subject | linear algebra | en_US |
| dc.subject | algorithm | en_US |
| dc.title | Quantum Approximate Optimization for Hard Problems in Linear Algebra | en_US |
| dc.type | Text | en_US |