首页>
外国专利>
Discrete variational auto-encoder systems and methods for machine learning using adiabatic quantum computers
Discrete variational auto-encoder systems and methods for machine learning using adiabatic quantum computers
展开▼
机译:使用绝热量子计算机的离散变分自动编码器系统和机器学习方法
展开▼
页面导航
摘要
著录项
相似文献
摘要
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.
展开▼