Discrete variational auto-encoder systems and methods for machine learning using adiabatic quantum computers
US11042811B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Oct 5, 2017 |
| Grant date | Jun 22, 2021 |
| Priority date | — |
| Expiry date | Apr 23, 2040 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06N7/01
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
A computational system can include digital circuitry and analog circuitry, for instance a digital processor and a quantum processor. The quantum processor can operate as a sample generator providing samples. Samples can be employed by the digital processing in implementing various machine learning techniques. For example, the computational system can perform unsupervised learning over an input space, for example via a discrete variational auto-encoder, and attempting to maximize the log-likelihood of an observed dataset. Maximizing the log-likelihood of the observed dataset can include generating a hierarchical approximating posterior. Unsupervised learning can include generating samples of a prior distribution using the quantum processor. Generating samples using the quantum processor can include forming chains of qubits and representing discrete variables by chains.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.