Training a quantum optimizer
US10176433B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Mar 13, 2017 |
| Grant date | Jan 8, 2019 |
| Priority date | — |
| Expiry date | Mar 13, 2037 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06N99/007
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
Among the embodiments disclosed herein are variants of the quantum approximate optimization algorithm with different parametrization. In particular embodiments, a different objective is used: rather than looking for a state which approximately solves an optimization problem, embodiments of the disclosed technology find a quantum algorithm that will produce a state with high overlap with the optimal state (given an instance, for example, of MAX-2-SAT). In certain embodiments, a machine learning approach is used in which a “training set” of problems is selected and the parameters optimized to produce large overlap for this training set. The problem was then tested on a larger problem set. When tested on the full set, the parameters that were found produced significantly larger overlap than optimized annealing times. Testing on other random instances (e.g., from 20 to 28 bits) continued to show improvement over annealing, with the improvement being most notable on the hardest problems. Embodiments of the disclosed technology can be used, for example, for near-term quantum computers with limited coherence times.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.