DISCRETE VARIATIONAL AUTO-ENCODER SYSTEMS AND METHODS FOR MACHINE LEARNING USING ADIABATIC QUANTUM COMPUTERS

• Main Author: ROLFE JASON
• Format: Patent
• Language: Chinese; English
• Published: 08.06.2018
• Subjects: CALCULATING; COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS; COMPUTING; COUNTING; PHYSICS

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. 种计算系统可以包括数字电路系统和模拟电路系统，例如，数字处理器和量子处理器。所述量子处理器可以作为提供样本的样本发生器操作。在实施各种机器学习技术时可以通过数字处理来采用样本。例如，所述计算系统可以例如经由离散变分自动编码器执行输入空间上的无监督学习，并且尝试使观测数据集的对数似然值最大化。使所述观测数据集的所述对数似然值最大化可以包括生成分层近似后验。