Transport, metastability, and neuromorphic applications in quantum networks

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，并倾向于破坏量子力学相干性。当量子位扩展到更大的数量时，大多数减轻它们对量子运算有害影响的策略很快变得低效。QNet的目标是提供操纵量子信息的概念和方法，其中容错性能是由噪声和耗散辅助甚至提高的。为此，QNet将评估量子网络的非平衡动力学，在存在和作为噪声函数的远程相互作用下，特别关注量子联想记忆和量子库计算的应用。QNet联合了世界领先的理论和实验科学家，他们的专业知识包括量子光学、凝聚态物质、量子信息和量子热力学。原理验证概念将在最先进的实验平台上进行测试，该实验平台包括(i)在具有可调温度、噪声和耗散的高精细腔中的超冷原子，以及（ii）耦合到片上介观热浴的超导量子电路。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将提供一个概念和范例的工具箱，为下一代量子技术铺平道路。

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.