Digraph states and their neural network representations

With the rapid development of machine learning, artificial neural networks provide a powerful tool to represent or approximate many-body quantum states. It was proved that every graph state can be generated by a neural network. Here, we introduce digraph states and explore their neural network representations (NNRs). Based on some discussions about digraph states and neural network quantum states (NNQSs), we construct explicitly an NNR for any digraph state, implying every digraph state is an NNQS. The obtained results will provide a theoretical foundation for solving the quantum many-body problem with machine learning method whenever the wave-function is known as an unknown digraph state or it can be approximated by digraph states.     

Complete and Partial Separability Conditions of Mixed State for a Quantum Network of Any Nodes

By using the block division method in matrix calculus, this article successfully calculate the expectationvalues of the generating operators and the correlation tensors for quantum network of any nodes. Thence, by meansof the criterion of entanglement in terms of the covariance correlation tensor in quantum network theory, this articlediscusses the complete and partial separability conditions of the mixed quantum state for a quantum network of anynodes and judge the separability of a quantum state in the generalcase of any nodes for two examples.     

Complete and Partial Separability Conditions of Mixed State for a Quantum Network of Any Nodes

By using the block division method in matrix calculus, this article successfully calculate the expectation values of the generating operators and the correlation tensors for quantum network of any nodes. Thence, by means of the criterion of entanglement in terms of the covariance correlation tensor in quantum network theory, this article discusses the complete and partial separability conditions of the mixed quantum state for a quantum network of any nodes and judge the separability of a quantum state in the general case of any nodes for two examples.     

Representations of hypergraph states with neural networks

The quantum many-body problem(QMBP) has become a hot topic in high-energy physics and condensed-matter physics. With an exponential increase in the dimensions of Hilbert space, it becomes very challenging to solve the QMBP, even with the most powerful computers. With the rapid development of machine learning, artificial neural networks provide a powerful tool that can represent or approximate quantum many-body states. In this paper, we aim to explicitly construct the neural network representations of hypergraph states. We construct the neural network representations for any k-uniform hypergraph state and any hypergraph state,respectively, without stochastic optimization of the network parameters. Our method constructively shows that all hypergraph states can be represented precisely by the appropriate neural networks introduced in [Science 355(2017) 602] and formulated in [Sci. China-Phys.Mech. Astron. 63(2020) 210312].     

Quantum M-P Neural Network

Quantum Neural Network (QNN) is a young and outlying science built upon the combination of classical neural network and quantum computing. Making use of quantum linear superposition, this paper presents a quantum M-P neural network based on the analysis of the conventional M-P neural network. Moreover, the working principle of this proposed network and its corresponding weight updating algorithm are expatiated in the two cases of input state being in the orthogonal and non-orthogonal basic set, respectively. In addition, this paper not only validates that this quantum M-P network can realize some network functions, such as “XOR”, but also verifies the feasibility and validity of its weight learning algorithm by some simple examples.     

General Solution for the Complete Separability of a Pure Quantum State with Real Coefficients for a Quantum Network of Any Nodes

By means of the criterion of entanglement in terms of the covariance correlation tensor in quantum network theory, this article discusses the general solution for the complete separability of the pure quantum state with real coefficients for a quantum network of any nodes.     

Quantum Stochastic Walk models for quantum state discrimination

Quantum Stochastic Walks (QSW) allow for a generalization of both quantum and classical random walks by describing the dynamic evolution of an open quantum system on a network, with nodes corresponding to quantum states of a fixed basis. We consider the problem of quantum state discrimination on such a system, and we solve it by optimizing the network topology weights. Finally, we test it on different quantum network topologies and compare it with optimal theoretical bounds.     

General Solution for the Complete Separability of a Pure Quantum State with Real Coefficients for a Quantum Network of Any Nodes

By means of the criterion of entanglement in terms of the covariance correlation tensor in quantum network theory, this article discusses the generalsolution for the complete separability of the pure quantum state with real coefficients for a quantum network of any nodes.     

连续变量量子态的光学操控

量子态的光学操控是指在光场的传输、存储和频率变换等过程中用光学方法对光场的量子态进行操作与控制.量子态操控是量子通讯及量子态制备和应用的基础.在简要介绍连续变量多组份纠缠态光场制备的基础上,概述基于多组份纠缠态光场的连续变量量子通讯网络及在执行压缩态和纠缠态光场操控方面的实验研究进展.     

Demonstration of eight-partite two-diamond shape cluster state for continuous variables

Multipartite entangled state is the basic resource for implementing quantum information networks and quantum computation. In this paper, we present the experimental demonstration of the eight-partite two-diamond shape cluster states for continuous variables, which consist of eight spatially separated and entangled optical modes. Eight resource squeezed states of light with classical coherence are produced by four nondegenerate optical parametric amplifiers and then they are transformed to the eight-partite two-diamond shape cluster states by a specially designed linear optical network. Since the spatially separated multipartite entangled state can be prepared off-line, it can be conveniently applied in the future quantum technology.     

Separability of Mixed States of the Quantum Network of Three Nodes

This article discusses the complete separability and partial separability of the mixed states of quantum network of three nodes by means of the crJterlon of entanglement in terms of the covarJance correlation tensor in quantum network theory.     

Separability of Pure States of the Quantum Network of Three Nodes

This article discusses the complete separability and partial separability of the pure states of the quantum network of three nodes by means of the criterion of entanglement in terms of the covariance correlation tensor in quantum network theory.     

High-fidelity quantum state transfer through a dissipative quantum data bus

We propose and analyze a robust quantum state transfer protocol through a scalable quantum data bus that consists of a network of controlled dissipative modules. In particular, we first demonstrate the ability to achieve perfect state transfer between two distinct quantum sites which are adiabatically coupled to the data bus in non-dissipative situation. We then consider the role of dissipation in adiabatic quantum state transfer via using Born–Markov master equation in the standard Lindblad form. Numerical simulation shows that the dissipation effect on the quality of transmission can be suppressed by engineering the network couplings of data bus properly.     

基于投影纠缠对态算法优化的研究

二维强关联电子量子格点系统的投影纠缠对态(PEPS)算法是数值计算领域中研究二维强关联电子量子格点系统最为重要的张量网络算法.基于PEPS算法研究二维量子XYX模型与二维量子Ising模型,本文对PEPS算法进行了一些优化和改进研究,这些优化和改进主要体现在如何进行PEPS张量的更新与如何进行物理观测量的计算这两个方面,从而可以大大提高计算资源的利用.因而优化和改进后的PEPS算法可为研究热力学极限下的二维强关联电子量子格点系统的量子相变和量子临界现象提供一种更有效的强大的工具.     

Separability of Pure States and Mixed States of the Quantum Network of Two Nodes

This article discusses the separability of the pure states and mixed states of the quantum network of twonodes by means of the criterion of no entanglement in terms of the covariance correlation tensor in quantum networktheory, i.e. for a composite system consisting of two nodes. The covariance correlation tensor Mjk(1, 2) is equal to zerofor all possible j and k.     

Quantum dynamics of tight-binding networks coherently controlled by external fields

With some reviews on the investigations on the schemes for quantum state transfer based on spin systems, we discuss the quantum dynamics of magnetically-controlled networks for Bloch electrons. The networks are constructed by connecting several tight-binding chains with uniform nearest-neighbor hopping integrals. The external magnetic field and the connecting hopping integrals can be used to control the intrinsic properties of the networks. For several typical networks, rigorous results are shown for some specific values of external magnetic field and the connecting hopping integrals: a complicated network can be reduced into a virtual network, which is a direct sum of some independent chains with uniform nearest-neighbor hopping integrals. These reductions are due to the fermionic statistics and the Aharonov-Bohm effects. In application, we study the quantum dynamics of wave packet motion of Bloch electrons in such networks. For various geometrical configurations, these networks can function as some optical devices, such as beam splitters, switches and interferometers. When the Bloch electrons as Gaussian wave packets input these devices, various quantum coherence phenomena can be observed, e.g., the perfect quantum state transfer without reflection in a Y-shaped beam, the multi-mode entanglers of electron wave by star-shaped network, magnetically controlled switches, and Bloch electron interferometer with the lattice Aharonov-Bohm effects. With these quantum coherent features, the networks are expected to be used as quantum information processors for the fermion system based on the possible engineered solid state systems, such as the array of quantum dots that can be implemented experimentally.      

Quantum Logic Network for Probabilistic Cloning Quantum States

We construct efficient quantum logic network for probabilistic cloning the quantum states used in implemented tasks for which cloning provides some enhancement in performance.     

Neural network representations of quantum many-body states

Machine learning is currently the most active interdisciplinary field having numerous applications;additionally,machine-learning techniques are used to research quantum many-body problems.In this study,we first propose neural network quantum states(NNQSs)with general input observables and explore a few related properties,such as the tensor product and local unitary operation.Second,we determine the necessary and sufficient conditions for the representability of a general graph state using normalized NNQS.Finally,to quantify the approximation degree of a given pure state,we define the best approximation degree using normalized NNQSs.Furthermore,we observe that some 7V-qubit states can be represented by a normalized NNQS,such as separable pure states,Bell states and GHZ states.     

Quantum logic networks for cloning a quantum state near a given state

Two quantum logic networks are proposed to simulate a cloning machine that copies the states near a given one.Probabilistic cloning based on the first network is realized and the cloning probability of success based on the second network is 100%.Therefore,the second network is more motivative than the first one.     

基于GHZ态的量子网络身份认证方案

利用量子力学中纠缠态的非定域关联性,提出了一种基于GHZ态的星型量子通信网络方案,该方案能有效地对用户身份进行认证,提高信息传输的安全性,实现任意站点间的量子通信.