Transport, metastability, and neuromorphic applications in quantum networks

量子网络中的传输、亚稳态和神经形态应用

• 批准号: 532771420
• 负责人: Professorin Dr. Giovanna Morigi
• 金额: --
• 依托单位: Institut für Quantenelektronik
• 依托单位国家: 德国
• 项目类别: Research Grants
• 资助国家: 德国
• 项目状态: 未结题
• 来源: https://gepris.dfg.de/gepris/projekt/532771420?language=en
• 关键词: Transport; metastability; neuromorphic; applications; quantum

One challenge at the core of quantum technologies is the realization of coherent quantum dynamics at the mesoscopic scale. As the size of a device is scaled up, disorder and dissipation become increasingly relevant and tend to destroy quantum mechanical coherence. Most strategies for mitigating their detrimental effect on quantum operations become quickly inefficient when scaled up to larger numbers of qubits. The goal of QNet is to provide concepts and methods for manipulating quantum information, where fault-tolerant performance is assisted or even boosted by noise and dissipation. For this purpose, QNet will assess the out-of-equilibrium dynamics of a quantum network with long-range interactions in the presence and as a function of noise, with a specific focus on applications to quantum associative memories and quantum reservoir computing. QNet joins world-leading theory and experimental scientists with expertise including quantum optics, condensed matter, quantum information, and quantum thermodynamics. The proof-of-principle concepts will be tested on state-of-the-art experimental platforms consisting of (i) ultracold atoms in a high-finesse cavity with tunable temperature, noise and dissipation, and (ii) superconducting quantum circuits that are coupled to on-chip mesoscopic heat baths. On these platforms we will test the role of noise and dissipation on quantum neuromorphic computation. QNet will provide a toolbox of concepts and paradigms, paving the way to the next generation of quantum technologies.

量子技术的核心挑战之一是在介观尺度上实现相干量子动力学。随着器件尺寸的增大，无序和耗散变得越来越重要，并且往往会破坏量子力学相干性。当扩展到更多数量的量子位时，大多数减轻其对量子操作有害影响的策略很快就会变得低效。 QNet 的目标是提供操纵量子信息的概念和方法，其中容错性能通过噪声和耗散得到辅助甚至增强。为此，QNet 将评估存在长程相互作用且作为噪声函数的量子网络的不平衡动力学，特别关注量子关联存储器和量子存储计算的应用。 QNet 汇集了世界领先的理论和实验科学家，他们拥有量子光学、凝聚态物质、量子信息和量子热力学等专业知识。原理验证概念将在最先进的实验平台上进行测试，该平台包括（i）具有可调温度、噪声和耗散的高精细腔体中的超冷原子，以及（ii）与片上介观热浴耦合的超导量子电路。在这些平台上，我们将测试噪声和耗散在量子神经形态计算中的作用。 QNet 将提供概念和范式的工具箱，为下一代量子技术铺平道路。