Massively Scalable Quantum Entanglement and Quantum Processing in the Optical Frequency Comb

光频梳中的大规模可扩展量子纠缠和量子处理

基本信息

  • 批准号:
    1206029
  • 负责人:
  • 金额:
    $ 54万
  • 依托单位:
  • 依托单位国家:
    美国
  • 项目类别:
    Continuing Grant
  • 财政年份:
    2012
  • 资助国家:
    美国
  • 起止时间:
    2012-09-01 至 2016-08-31
  • 项目状态:
    已结题

项目摘要

Quantum computing promises exponential speedups over classical computing for specific but important tasks, such as data encryption and the simulation of quantum physics. However, the practical implementation of quantum computing faces daunting challenges: the need for scalability of "Qbit" (here, "Qmode") registers and the need to circumvent decoherence. This project from Prof. Pfister at the University of Virginia (UVa) aims at implementing large-scale entanglement in the periodic emission spectrum of an optical parametric oscillator (OPO), a.k.a. the "quantum optical frequency comb" (QOFC). It is based on the recent realization by Pfister's group of high-quality entanglement in a world-record 60 eigenmodes ("Qmodes") of the QOFC of a single OPO, into 15 sets of 4 Qmodes, each set being in a square cluster state. This successful experiment was the core of the project supported by an NSF award entitled "One-way quantum computing in the optical frequency comb." The objective of the current project is to build on this success and forge ahead toward highly scalable quantum information, along two lines of effort. On the one hand, we seek to generate record-size linear, square-grid, and cubic-lattice cluster states, which enable universal quantum computation. On the other hand, the group is striving to implement the quantum technologies needed for quantum processing in the QOFC. This includes: (i) developing low-loss, highly dispersive optical elements to separate Qmodes, (ii) implementing a network of balanced homodyne detection with high-efficiency PIN photodiodes using integrated optics, and (iii) performing high-efficiency nonGaussian measurements by way of photon-number-resolved detection, which has recently been implemented in Pfister's group at UVa thanks to a collaboration with Sae Woo Nam at NIST and Aaron Miller at Albion College, funded by an NSF MRI award entitled "Development of a photon-number-resolving detector system for universal quantum computing." This ambitious program is tantamount to creating a bona fide quantum computer over continuous variables, and studying quantum information in this context.The broader impacts of this work comprise an active research contribution to the UVa physics graduate and undergraduate programs. One recent undergraduate student was a former Goldwater Scholar who just joined the physics graduate program at Harvard, was a finalist of the 2011 LeRoy Apker Award of the American Physical Society, based on a paper he published with Prof. Pfister. Also stemming from this research, an advanced graduate course "Quantum Optics and Quantum Information" is now taught by the PI on a regular basis. On the interdisciplinary front, this research has spawned worldwide collaborative efforts. Finally, it is important to point out that quantum computing research has stakes in fundamental physics, as well as Defense and National Security: Shor's algorithm for factoring integers exponentially faster would defeat the widely-used RSA encryption protocol. Another direct application of a universal quantum processor of elementary size would be the modeling of presently intractable quantum problems in chemistry, materials science, and condensed-matter physics. Finally, the realization of a scalable quantum register offers possibilities for fundamental tests of quantum mechanics in the regime of mesoscopic entanglement and Schrödinger cats, where theoretical predictions become intractable. As quantum information comes of age, one can thus expect deeply significant scientific discoveries in all fields of the natural sciences.
在特定但重要的任务上,例如数据加密和量子物理模拟,量子计算有望以指数级的速度超过经典计算。然而,量子计算的实际实现面临着令人生畏的挑战:需要可伸缩的“Qbit”(这里是“Qmode”)寄存器,以及需要绕过退相干。弗吉尼亚大学(UVA)Pfister教授的这个项目旨在实现光学参量振荡器(OPO)周期发射光谱中的大规模纠缠。量子光学频率梳(QOFC)。它是基于Pfister小组最近实现的高质量纠缠,该纠缠在一个OPO的QOFC的60个本征模(“Q模”)中创造了世界纪录,变成了15组4个Q模,每组都处于正方形团簇状态。这项成功的实验是由美国国家科学基金会颁发的名为“光学频率梳中的单向量子计算”的奖项支持的项目的核心。当前项目的目标是在这一成功的基础上,沿着两条工作路线,朝着高度可扩展的量子信息迈进。一方面,我们寻求产生创纪录大小的线性、正方形网格和立方晶格团簇状态,这使得普遍的量子计算成为可能。另一方面,该组织正在努力实现QOFC中量子处理所需的量子技术。这包括:(I)开发低损耗、高度色散的光学元件来分离Q模;(Ii)利用集成光学技术利用高效的PIN光电二极管实现平衡的零差探测网络;以及(Iii)通过光子数分辨探测的方式执行高效的非高斯测量,这一方法最近已在UVA的Pfister小组中实施,这要归功于与NIST的Sae Woo nam和Albion College的Aaron Miller的合作,该奖项得到了NSF MRI奖的资助,该奖项名为“开发用于通用量子计算的光子数分辨探测器系统”。这一雄心勃勃的计划相当于在连续变量上创建一台真正的量子计算机,并在此背景下研究量子信息。这项工作的更广泛影响包括对UVA物理学研究生和本科生项目的积极研究贡献。最近的一名本科生是前戈德华特学者,刚刚加入哈佛大学的物理研究生项目,根据他与菲斯特教授发表的一篇论文,他入围了2011年美国物理学会勒罗伊·阿普克奖(LeRoy Apker Award)。也是由于这项研究,国际和平研究所现在定期教授一门高级研究生课程《量子光学和量子信息》。在跨学科方面,这项研究在全球范围内引发了合作努力。最后,必须指出的是,量子计算研究与基础物理以及国防和国家安全息息相关:Shor的整数因式分解算法将以指数级的速度击败广泛使用的RSA加密协议。基本大小的通用量子处理器的另一个直接应用将是对化学、材料科学和凝聚态物理中目前难以解决的量子问题进行建模。最后,可扩展量子寄存器的实现为在介观纠缠和薛定谔猫的情况下进行量子力学的基本测试提供了可能性,在这种情况下,理论预测变得难以处理。随着量子信息时代的到来,人们可以期待在自然科学的所有领域都会有深刻的重大科学发现。

项目成果

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Olivier Pfister其他文献

Spatiotemporal graph states from a single optical parametric oscillator
来自单个光参量振荡器的时空图状态
  • DOI:
    10.1103/physreva.101.043832
  • 发表时间:
    2020
  • 期刊:
  • 影响因子:
    2.9
  • 作者:
    Rongguo Yang;Jing Zhang;Israel Klich;Carlos González-Arciniegas;Olivier Pfister
  • 通讯作者:
    Olivier Pfister
Universal quantum frequency comb measurements by spectral mode-matching
通过光谱模式匹配进行通用量子频率梳测量
  • DOI:
  • 发表时间:
    2024
  • 期刊:
  • 影响因子:
    0
  • 作者:
    B. Dioum;Virginia d'Auria;A. Zavatta;Olivier Pfister;G. Patera
  • 通讯作者:
    G. Patera
Qubits without qubits
没有量子位的量子位
  • DOI:
  • 发表时间:
    2024
  • 期刊:
  • 影响因子:
    56.9
  • 作者:
    Olivier Pfister
  • 通讯作者:
    Olivier Pfister
Jumping to hyperentanglement
跳跃到超纠缠态
  • DOI:
    10.1038/nphoton.2015.131
  • 发表时间:
    2015-07-30
  • 期刊:
  • 影响因子:
    32.900
  • 作者:
    Olivier Pfister
  • 通讯作者:
    Olivier Pfister
Experimental Generation of Cluster-state Entanglement by Phase Modulation of the Quantum Optical Frequency Comb
量子光频梳相位调制簇态纠缠的实验生成

Olivier Pfister的其他文献

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{{ truncateString('Olivier Pfister', 18)}}的其他基金

Collaborative Research: Toward universal quantum computing with heterogeneously integrated quantum optical frequency combs
合作研究:利用异构集成量子光学频率梳实现通用量子计算
  • 批准号:
    2219672
  • 财政年份:
    2022
  • 资助金额:
    $ 54万
  • 项目类别:
    Standard Grant
NSF-BSF: The Phase-Modulated Quantum Optical Frequency Comb: A Simple Platform for One-Way Quantum Computing
NSF-BSF:相位调制量子光频梳:单向量子计算的简单平台
  • 批准号:
    2112867
  • 财政年份:
    2021
  • 资助金额:
    $ 54万
  • 项目类别:
    Standard Grant
RAISE-EQuIP: Quantum mux/demux: the quantum optical frequency comb as a scalable quantum encoding resource
RAISE-EQuIP:量子复用/解复用:量子光学频率梳作为可扩展的量子编码资源
  • 批准号:
    1842641
  • 财政年份:
    2018
  • 资助金额:
    $ 54万
  • 项目类别:
    Standard Grant
NSF-BSF: Squeezing the Optical Frequency Comb: Applications to Quantum Computing and Quantum Measurement
NSF-BSF:挤压光频梳:在量子计算和量子测量中的应用
  • 批准号:
    1820882
  • 财政年份:
    2018
  • 资助金额:
    $ 54万
  • 项目类别:
    Continuing Grant
Quantum Interferometry with Photon-Subtracted Twin Beams
光子相减双光束量子干涉测量
  • 批准号:
    1708023
  • 财政年份:
    2017
  • 资助金额:
    $ 54万
  • 项目类别:
    Standard Grant
Quantum Computing and Quantum Simulation in the Optical Frequency Comb
光频梳中的量子计算与量子模拟
  • 批准号:
    1521083
  • 财政年份:
    2015
  • 资助金额:
    $ 54万
  • 项目类别:
    Continuing Grant
MRI-R2 Consortium: Development of a Photon-Number-Resolving Detector System for Universal Quantum Computing
MRI-R2 联盟:开发用于通用量子计算的光子数分辨探测器系统
  • 批准号:
    0960047
  • 财政年份:
    2010
  • 资助金额:
    $ 54万
  • 项目类别:
    Standard Grant
One-Way Quantum Computing in the Optical Frequency Comb
光频梳中的单向量子计算
  • 批准号:
    0855632
  • 财政年份:
    2009
  • 资助金额:
    $ 54万
  • 项目类别:
    Continuing Grant
Multipartite Entanglement, Multimode Squeezing, and Non-Gaussian Light from Quantum Cascades and Concurrences
量子级联和并发中的多部分纠缠、多模压缩和非高斯光
  • 批准号:
    0555522
  • 财政年份:
    2006
  • 资助金额:
    $ 54万
  • 项目类别:
    Continuing Grant
Quantum: Ultrastable heterodyne quantum information
量子:超稳定外差量子信息
  • 批准号:
    0622100
  • 财政年份:
    2006
  • 资助金额:
    $ 54万
  • 项目类别:
    Standard Grant

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Scalable Learning and Optimization: High-dimensional Models and Online Decision-Making Strategies for Big Data Analysis
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