How a Quantum Computer Could Solve a Microkinetic Model
Source abstract
Abstract
A CO oxidation microkinetic model is set up for a quantum circuit. The CO oxidation microkinetic model, and microkinetic models in general, exhibit an advantage of not requiring an encoding step because of being a subclass of systems of equations. The microkinetic model is cast as a nonlinear set of equations at first. Then, a linearizing approximation is made, and the resulting linear set of equations may be iterated to converge to the solution to the nonlinear set of equations. In this CO oxidation, the linearized set of equations is realized to chemical accuracy with one iteration. Current limitations in executing the quantum circuit to obtain the solution are discussed.
Source: openalex_inverted_index · Quality: baseline
Research context
OpenAlex topics: Quantum Computing Algorithms and Architecture · Quantum and electron transport phenomena · Quantum Information and Cryptography
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Institute for Computational and Data Sciences, University at Buffalo, The State University of New York, Buffalo, New York 14260, United States
Department of Chemical and Biological Engineering, University at Buffalo, The State University of New York, Buffalo, New York 14260, United States
References (34)
Show source-provided references
1. 10.1073/pnas.080824510510.1073/pnas.0808245105 ↗
2. 10.1103/PhysRevLett.114.09050210.1103/PhysRevLett.114.090502 ↗
3. 10.1021/acscatal.9b0123410.1021/acscatal.9b01234 ↗
4. 10.1021/acs.jpcc.0c0647010.1021/acs.jpcc.0c06470 ↗
5. 10.1021/acscatal.7b0353110.1021/acscatal.7b03531 ↗
6. 10.1002/cctc.20200095310.1002/cctc.202000953 ↗
7. 10.1021/acscatal.6b0254810.1021/acscatal.6b02548 ↗
8. 10.1038/ncomms1462110.1038/ncomms14621 ↗
9. 10.1002/cctc.20200097610.1002/cctc.202000976 ↗
10. 10.1021/acscatal.8b0071310.1021/acscatal.8b00713 ↗
11. 10.1103/PhysRevLett.103.15050210.1103/PhysRevLett.103.150502 ↗
12. 10.1007/BF0032426910.1007/BF00324269 ↗
13. Engel T. · 1979 · 28 · 1
14. 10.1126/science.105947810.1126/science.1059478 ↗
15. 10.1021/acs.jpcc.7b1177110.1021/acs.jpcc.7b11771 ↗
16. Chorkendorff I. · 200310.1002/3527602658 ↗
17. 10.1021/acs.jpcc.6b0134810.1021/acs.jpcc.6b01348 ↗
18. 10.1016/j.ces.2014.09.01110.1016/j.ces.2014.09.011 ↗
19. 10.1016/0927-0256(96)00008-010.1016/0927-0256(96)00008-0 ↗
20. 10.1103/PhysRevB.47.55810.1103/PhysRevB.47.558 ↗
21. 10.1103/PhysRevB.85.23514910.1103/PhysRevB.85.235149 ↗
22. 10.1002/jcc.2472010.1002/jcc.24720 ↗
23. 10.1021/ct400319w10.1021/ct400319w ↗
24. 10.1063/1.480416210.1063/1.4804162 ↗
25. 10.1021/ct300659d10.1021/ct300659d ↗
26. 10.1002/jcc.2327110.1002/jcc.23271 ↗
27. 10.1039/C8CP04703K10.1039/C8CP04703K ↗
28. 10.1002/jcc.2383310.1002/jcc.23833 ↗
29. 10.1016/j.fuel.2020.11938810.1016/j.fuel.2020.119388 ↗
30. 10.1021/acs.jpcc.0c0084910.1021/acs.jpcc.0c00849 ↗
31. Cox J. D. · 1984
32. 10.1002/aic.1462710.1002/aic.14627 ↗
33. Coles P. J. · arXiv · 2018
34. 10.1080/00268976.2012.66828910.1080/00268976.2012.668289 ↗