We present the model of one-dimensional chain of two-level artificial atoms driven with dc field and quantum light simultaneously in strong coupling regime. The interaction of atoms with light leads to entanglement of electron and photon states (dressing of the atoms). The driving via dc field leads to the Bloch oscillations (BO) in the chain of dressed atoms. We considered the mutual influence of dressing and BO and show that scenario of oscillations dramatically differs from predicted by the Jaynes-Cummings and Bloch-Zener models. We study the evolution of the population inversion, tunneling current, photon probability distribution, mean number of photons, photon number variance and show the influence of BO on the quantum-statistical characteristics of light. For example, collapse-revivals picture and vacuum Rabi-oscillations are strongly modulated with Bloch frequency. As a result, quantum properties of light and degree of electron-photon entanglement become controllable via adiabatic dc field turning. On the other hand, the low-frequency tunneling current depends on the quantum light statistics (in particular, for coherent initial state it is modulated accordingly the collapse-revivals picture). The developed model is universal with respect to the physical origin of artificial atom and frequency range of atom-light interaction. The model is adapted to the 2D-heterostructures (THz frequencies), semiconductor quantum dots (optical range), and Josephson junctions (microwaves). The data for numerical simulations are taken from recently published experiments. The obtained results open a new ways in quantum state engineering and nano-photonic spectroscopy.
Supervised Learning Using a Dressed Quantum Network with "Super Compressed Encoding": Algorithm and Quantum-Hardware-Based Implementation
Indian Institute of Technology Delhi（印度理工学院德里分校）
已知在有噪声的中间尺度量子（NISQ）设备上实现变分量子机器学习（QML）算法具有与所需的大量量子比特和与多量子比特门相关联的噪声有关的问题。在本文中，我们提出了一个变分QML算法，使用一个穿着量子网络来解决这些问题。使用我们在这里遵循的“超压缩编码”方案，在将输入馈送到变分量子线路之前，我们的修饰网络中的经典编码层大幅降低了输入维度。因此，我们的量子线路所需的量子比特数量急剧下降。此外，与大多数其他现有的QML算法不同，我们的量子线路仅由单量子比特门组成，使其对噪声具有鲁棒性。这些因素使我们的算法适合NISQ硬件上实现。为了支持我们的论点，我们在真实的NISQ硬件上实现了我们的算法，从而使用流行的机器学习数据集（如Fisher's Iris，Wisconsin's Breast Cancer（WBC）和Abalone）显示了准确的分类。然后，为了给我们的算法的工作提供一个直观的解释，我们展示了量子态的聚类，它对应于不同的输出类的输入样本，在Bloch球（使用WBC和MNIST数据集）。这种聚类是我们算法中训练过程的结果。通过这种基于布洛赫球的表示，我们还显示了经典编码层的可调参数和变分量子线路的可调参数（在训练中）所起的不同作用。这些参数在训练期间通过损失最小化迭代地调整。
Synthesizing In-Bulk Topological Corner States via Giant Atoms
Shaanxi Province Key Laboratory of Quantum Information and Quantum Optoelectronic Devices, School of Physics, Xi’an Jiaotong University（陕西量子信息与光电子器件重点实验室，西安交通大学物理学院）
Optomechanical analog of two-color electromagnetically-induced transparency: Photon transmission through an optomechanical device with a two-level system
Quantum dynamics of microwave photons in synthetic frequency dimension
Shenzhen Institute for Quantum Science and Engineering, Southern University of Science and Technology（南方科技大学深圳量子科学与工程学院）；International Quantum Academy（国际量子学院）；Guangdong Provincial Key Laboratory of Quantum Science and Engineering, Southern University of Science and Technology（南方科技大学广东省量子科学与工程重点实验室）；State Key Laboratory of Photonics and Communications, School of Physics and Astronomy, Shanghai Jiao Tong University（上海交通大学物理与天文学院光子学与通信国家重点实验室）
