Collaborative Research: Adiabatic Quantum Computing and Statistics

合作研究：绝热量子计算与统计

• 批准号: 1528735
• 负责人: Yazhen Wang
• 金额: $ 24.67万
• 依托单位: University of Wisconsin-Madison
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-09-01 至 2020-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1528735&HistoricalAwards=false
• 关键词: Collaborative; Research; Adiabatic; Quantum; Computing

For decades computer power has doubled for constant cost roughly once every two years according to the so-called Moore's law. This dream run is realized through technological advances in the fabrication of computer hardware, making electronic devices smaller and smaller. However, as the sizes of the computer electronic devices get close to the atomic scale, quantum effects are starting to interfere in their functioning, and thus conventional computer technology approaches run up against fundamental difficulties of size limit. This research project concerns quantum computing, the development of computer technology dependent on the principles of quantum physics, as opposed to the electronic devices following laws of classical physics used by classical computers. This revolutionary field will enable a range of exotic new devices, and in particular it will likely lead to the creation of powerful quantum computers. The research project is among the frontier research endeavors where quantum technologies are being developed and quantum devices are being built with capabilities exceeding those of classical computational devices. Quantum computation and quantum information science more generally concern the preparation and control of the quantum states of physical systems to manipulate and transmit information. A quantum system usually has complexity exponentially increasing with its size. As a result, it takes an exponential number of bits of memory on a classical computer to store the state of a quantum system, and simulations of quantum systems via classical computers face great computational challenge. On the other hand, since quantum systems are able to store and track an exponential number of complex numbers and perform data manipulations and calculations as the systems evolve, quantum systems hold great promise as computational tools. Quantum information science grapples with understanding how to take advantage of the enormous amount of information hidden in the quantum systems and to harness the immense potential computational power of atoms and photons for the purpose of information processing and computation. This cross-disciplinary research project addresses questions in quantum information science on the interface between quantum computing and machine learning and between quantum tomography and compressed sensing. The collaborative research aims to explore the power of adiabatic quantum computation and its impact on computer science and statistics in general and machine learning and Monte Carlo sampling in particular. The work investigates the use of leading techniques from machine learning in computer science and statistics, compressed sensing in applied mathematics, statistics, and engineering, and quantum tomography in quantum physics for adiabatic quantum computing. The research activities promote collaborations among investigators with different disciplinary backgrounds and stimulate novel ideas for possible breakthrough, transformative research.

根据所谓的摩尔定律，数十年来，计算机电源的恒定成本大约每两年大约每两年翻了一番。通过在计算机硬件的制造方面的技术进步来实现这种梦想，使电子设备越来越小。但是，随着计算机电子设备的尺寸接近原子量表，量子效应开始干扰其功能，因此，传统的计算机技术方法与尺寸限制的根本困难相比会产生。该研究项目涉及量子计算，计算机技术的开发取决于量子物理原理，而不是按照经典计算机使用的经典物理学定律的电子设备。这个革命性的领域将启用一系列异国情调的新设备，尤其可能会导致创建强大的量子计算机。该研究项目是开发量子技术的前沿研究努力之一，并以超过经典计算设备的功能来构建量子设备。量子计算和量子信息科学通常更涉及处理和传输信息的物理系统量子状态的准备和控制。量子系统通常具有与其大小相关的复杂性。结果，它需要在经典计算机上进行指数级的存储器数量来存储量子系统的状态，并且通过古典计算机对量子系统进行模拟面临巨大的计算挑战。另一方面，由于量子系统能够存储和跟踪指数数的复数数量并随着系统的发展而执行数据操作和计算，因此量子系统作为计算工具具有巨大的希望。 量子信息科学与了解如何利用量子系统中隐藏的大量信息并利用原子和光子的巨大潜在计算能力来解决信息处理和计算的巨大潜在计算能力。该跨学科研究项目解决了量子信息科学中有关量子计算与机器学习之间以及量子断层扫描与压缩感测之间的界面的问题。该协作研究旨在探讨绝热量子计算的力量及其对一般和机器学习的计算机科学和统计数据的影响，尤其是蒙特卡洛采样。这项工作研究了计算机科学和统计中的机器学习中的领先技术，在应用数学，统计和工程学中的压缩感测，以及用于绝热量子计算的量子物理学中的量子层析成像。研究活动促进了具有不同纪律背景的研究人员之间的合作，并刺激了新颖的思想，以实现突破性，变革性研究。