Architectures and Distribution Arithmetic for Coupling Classical Computers to Noisy Intermediate-Scale Quantum Computers

用于将经典计算机耦合到嘈杂的中级量子计算机的架构和分布算法

基本信息

批准号：
EP/V047507/1
负责人：
Phillip Stanley-Marbell
金额：
$ 25.76万
依托单位：
University of Cambridge
依托单位国家：
英国
项目类别：
Research Grant
财政年份：
2021
资助国家：
英国
起止时间：
2021 至无数据
项目状态：
已结题

来源：
https://gtr.ukri.org/projects?ref=EP%2FV047507%2F1
关键词：
Architectures Distribution Arithmetic Coupling Classical

项目摘要

All physical measurements have measurement uncertainty and are best represented with probability distributions. Measurements from sensors feeding machine learning algorithms and measurements of the outputs of quantum computing hardware to obtain their final results are examples of increasingly-important applications of this concept in both research and industry. The distributional nature of measurements and the importance of the applications of measurements makes it increasingly valuable for computing systems to be able to perform arithmetic directly on representations of probability distributions, analogous to their ability to perform computations on approximate representations of real numbers (floating-point arithmetic).There however remains an unsolved research challenge to create number representations, and associated mathematical methods for arithmetic and logic, that could eventually be implemented in digital microprocessor architectures to enable computers of the future to perform arithmetic and logic operations on probability distributions. By analogy, microprocessors, which form the foundation of most of the modern world's technologies, perform arithmetic on integers and floating-point representations which serve as approximations of real numbers. Compact bit-level representations for joint probability distributions and efficient methods to perform arithmetic on them could have far-reaching impact on future computing systems in much the same way digital arithmetic and floating-point number representations have formed the foundation for today's microprocessors. Computation on distributions could also enable fundamentally new applications such as neural networks that track epistemic uncertainty in their network weights and aleatoric uncertainty in their inputs and predictions.Our research objective is to explore new frontiers in efficient in-processor representations of probability distributions that could enable new classes of computing systems that natively perform arithmetic and logic on probability distributions. We will investigate: (1) new bit-level number representations that can efficiently capture the properties of probability distributions that contain low-probability events which contribute significantly to the moments of a distribution; (2) new insights into the relationship between existing commonly-used distribution distance metrics and new methods for characterizing the differences between distributions; (3) new mathematical methods for performing arithmetic and logic on distributions, which are orders of magnitude faster than the de facto standard of performing Monte Carlo simulations on joint probability distributions.In the long term, the results of our investigation could be transformative for future Bayesian machine learning methods and could enable fundamentally new microprocessor architectures for processing the distributional outputs of Noisy Intermediate-Scale Quantum (NISQ) computers. In the medium term, the methods we investigate could be applied across a broad range of fundamental scientific challenges, from new compute hardware architectures for accelerating in situ computational modeling and model-predictive control of the distribution of particle sizes in precipitation processes occurring in additive manufacturing, to new compute hardware architectures for accelerating the computational modeling of particle size distributions in crystallization processes for pharmaceuticals research.

所有物理测量都有测量不确定性，并且最好用概率分布表示。从传感器喂养机学习算法和量子计算硬件输出的测量以获得最终结果的测量是该概念在研究和行业中越来越重要的应用程序的示例。测量的分布性质和测量应用的重要性使得计算系统能够直接执行算术在概率分布的表示上直接执行算术，这类似于其对实际数量的近似计算能力进行计算的能力（浮点数算术算术）。微处理器体系结构使未来的计算机能够对概率分布执行算术和逻辑操作。类比，微处理器构成了大多数现代世界技术的基础，在整数和浮点表示上执行算术，这些表示是实数的近似值。与数字算术和浮点数数字表示形式相同的方式，对关节概率分布的紧凑位级表示可能对未来的计算系统产生深远的影响，这可能对未来的计算系统产生深远的影响。 Computation on distributions could also enable fundamentally new applications such as neural networks that track epistemic uncertainty in their network weights and aleatoric uncertainty in their inputs and predictions.Our research objective is to explore new frontiers in efficient in-processor representations of probability distributions that could enable new classes of computing systems that natively perform arithmetic and logic on probability distributions.我们将调查：（1）可以有效捕获包含低概率事件的概率分布的属性的新的比特数字表示，这些概率分布对分布的力矩产生了显着贡献；（2）对现有常用分布距离指标与新方法之间的关系的新见解，以表征分布之间的差异；（3）在分布上执行算术和逻辑的新数学方法，比事实上的蒙特卡洛模拟对联合概率分布的蒙特卡洛模拟的实际标准快的速度更快。在长期以来，我们的调查结果可以对未来的贝叶斯学习方法进行变革，并且可以启用基本上新的微构造的量子构造的量子，以使得为基本上的新分配构建分配构建分配，以使得处理分布的分布，以使得处理分布的分布，（NISQ）计算机。在中期，我们研究的方法可以在广泛的基本科学挑战中应用，从新的计算硬件体系结构来加速原位计算建模和模型预测性控制粒径分布在添加剂制造中发生的降水过程中分布的分布，到新的计算硬件架构的计算过程中的计算型模型。