Quantum Tensor Networks for Material Design and Probabilistic Modelling

我正在探索量子张量网络在物理模拟中的应用，特别是应用于光诱导高温超导和存在耗散的量子热力学，以及概率建模，特别是应用于具有幂律相关性的顺序和通常结构化数据，如生物信息学和自然语言。前者的动机是需要找到量子系统可用的指数大状态空间的有效简化表示，以便研究和设计具有更高能量效率的材料。后者的动机是资源压缩、高效采样（生成）以及机器学习和人工智能中改进的泛化。两者之间的桥梁是分别在能源和信息部门争取更有效地利用资源。

I am exploring the use of quantum tensor networks for physics simulation, specifically with application to optically induced high temperature superconductivity and quantum thermodynamics in the presence of dissipation, and to probabilistic modelling, specifically with application to sequential and often structured data with power-law correlations such as bio-informatics and natural language. The former is motivated by the need to find efficient reduced representations of the exponentially large state spaces available to quantum systems in order to study and design materials with increased energy efficiency. The latter is motivated by resource compression, efficient sampling (generation) and improved generalisation in machine learning and AI. A bridge between both, is striving for more efficient resource use in the energy and information sectors respectively.