Graph kernels are often used in bioinformatics and network applications to measure the similarity between graphs; therefore, they may be used to construct efficient graph classifiers. Many graph kernels have been developed thus far, but to the best of our knowledge there is no existing graph kernel that considers all subgraphs to measure similarity. We propose a novel graph kernel that applies a quantum computer to measure the graph similarity taking all subgraphs into account by fully exploiting the power of quantum superposition to encode every subgraph into a feature. For the construction of the quantum kernel, we develop an efficient protocol that removes the index information of subgraphs encoded in the quantum state. We also prove that the quantum computer requires less query complexity to construct the feature vector than the classical sampler used to approximate the same vector. A detailed numerical simulation of a bioinformatics problem is presented to demonstrate that, in many cases, the proposed quantum kernel achieves better classification accuracy than existing graph kernels.
ICFO-Institut de Ciencies Fotoniques, The Barcelona Institute of Science and Technology（ICFO-光子科学研究所，巴塞罗那科学与技术研究院）；Universidad de Buenos Aires, Facultad de Ciencias Exactas y Naturales（布宜诺斯艾利斯大学，理学院）；CONICET - Universidad de Buenos Aires, Instituto de Física de Buenos Aires (IFIBA)（阿根廷国家科学研究委员会-布宜诺斯艾利斯大学，布宜诺斯艾利斯物理研究所 (IFIBA)）
Matrix encoding method in variational algorithm of calculating eigenvalues and generalized eigenvalues
Federal Research Center of Problems of Chemical Physics and Medicinal Chemistry RAS（俄罗斯科学院化学物理与医药化学问题联邦研究中心）；School of Mathematical Sciences, Zhejiang University（浙江大学数学科学学院）
Quantum Error Correction with Superpositions of Squeezed Fock States
RIKEN Center for Quantum Computing, RIKEN（理化学研究所量子计算中心）；Key Laboratory of Low-Dimensional Quantum Structures and Quantum Control of Ministry of Education, Department of Physics and Synergetic Innovation Center for Quantum Effects and Applications, Hunan Normal University（湖南师范大学低维量子结构与量子控制教育部重点实验室、物理系及量子效应与应用协同创新中心）；Quantum Research Institute and Department of Physics, University of Michigan（密歇根大学量子研究所与物理系）
Quantum search algorithm for similar subgraph identification under fixed edge removal
Institute of Climate and Energy Systems (ICE-1), Forschungszentrum Jülich（于利希研究中心气候与能源系统研究所）；Institute for Quantum Information, RWTH Aachen University（亚琛工业大学量子信息研究所）；Institute for Quantum Computing Analytics (PGI-12), Forschungszentrum Jülich（于利希研究中心量子计算分析研究所）；Computational Methods for Energy Systems Engineering, RWTH Aachen University（亚琛工业大学能源系统工程计算方法）
All-optical generation of states for "Encoding a qubit in an oscillator"
离散和连续系统都可以用来编码量子信息。大多数量子计算方案提出在二能级系统中编码量子比特，例如二能级原子或电子自旋。另一些则利用无限维系统，如谐振子。在“Encoding a qubit in an oscillator”[Phys. Rev. A 64 012310（2001）]中，Gottesman、Kitaev和Preskill（GKP）结合了这些方法，提出了一种容错量子计算方案，其中qubit被编码在振荡器的连续位置和动量自由度中。该方案的一个优点是它可以通过使用相对简单的线性光学器件、压缩和零差检测来执行。然而，我们缺乏一个实用的方法来准备初始GKP状态。在这里，我们提出了一个近似的GKP状态的产生，使用叠加的光学相干态（有时称为“薛定谔猫态”），压缩，线性光学器件，和零差检测。
Unified framework for bosonic quantum information encoding, resources and universality from superselection rules
Université Paris Cité（巴黎西岱大学）；CNRS（法国国家科学研究中心）；Laboratoire Matériaux et Phénomènes Quantiques（物质与现象量子实验室）；Département de Mathématiques et Applications de l’Ecole Normale Supérieure - PSL（巴黎高等师范学院-PSL数学与应用系）；Université Paris-Saclay（巴黎萨克雷大学）
Resource-Efficient Quantum Optimization via Higher-Order Encoding
University of Hamburg（汉堡大学）；Institute for Quantum Physics, University of Hamburg（汉堡大学量子物理研究所）；Max Planck Institute for the Structure and Dynamics of Matter（马克斯·普朗克物质结构与动力学研究所）；Zentrum für Optische Quantentechnologien, University of Hamburg（汉堡大学光学量子技术中心）；Department of Physics & Astronomy, University of Tennessee（田纳西大学物理与天文学系）；UTC Quantum Center, University of Tennessee（田纳西大学UTC量子中心）；Lufthansa Industry Solutions（汉莎工业解决方案）；The Hamburg Centre for Ultrafast Imaging, University of Hamburg（汉堡大学超快成像中心）；Clarendon Laboratory, University of Oxford（牛津大学克拉伦登实验室）
Quantum Data Representation via Circuit Partitioning and Reintegration
Texas Tech University（德克萨斯理工大学）；Lawrence Berkeley National Laboratory（劳伦斯伯克利国家实验室）；University of Saskatchewan（萨斯喀彻温大学）；Rochester Institute of Technology（罗切斯特理工学院）
