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.     

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.     

Bidirectional Information Flow Quantum State Tomography

The exact reconstruction of many-body quantum systems is one of the major challenges in modern physics,because it is impractical to overcome the exponential complexity problem brought by high-dimensional quantum manybody systems.Recently,machine learning techniques are well used to promote quantum information research and quantum state tomography has also been developed by neural network generative models.We propose a quantum state tomography method,which is based on a bidirectional gated recurrent unit neural network,to learn and reconstruct both easy quantum states and hard quantum states in this study.We are able to use fewer measurement samples in our method to reconstruct these quantum states and to obtain high fidelity.     

Interaction-Induced Chiral Quantum States of the Ultracold Polar Molecules

The ultracold polar molecules with the tunable dipole-dipole interaction, not only would enable explorations of a large class of exotic many-body physics phenomena, but also could be used for quantum information processing. In the present paper we demonstrate that this dipole-dipole interaction can generate the degenerate chiral quantum states acting as a qubit robust against noise when the ultracold polar molecules are confined by a triangular lattice. Moreover, we also find two first-order quantum phase transitions by controlling an external driving field. One is the transition with the change of the different degenerate chiral quantum states. The other is the transition with the breaking of the degenerate quantum chiral states to the nondegenerate state. In experiment, these first-order quantum phase transitions can be detected by measuring the collective molecular population.     

Interaction-Induced Chiral Quantum States of the Ultracold Polar Molecules

The ultracold polar molecules with the tunable dipole-dipole interaction, not only would enable explorations of a large class of exotic many-body physics phenomena, but also could be used for quantum information processing. In the present paper we demonstrate that this dipole-dipole interaction can generate the degenerate chiral quantum states acting as a qubit robust against noise when the ultracold polar molecules are confined by a triangular lattice. Moreover, we also find two first-order quantum phase transitions by controlling an external driving field. One is the transition with the change of the different degenerate chiral quantum states. The other is the transition with the breaking of the degenerate quantum chiral states to the nondegenerate state. In experiment, these first-order quantum phase transitions can be detected by measuring the collective molecular population.     

Green's function Monte Carlo method combined with restricted Boltzmann machine approach to the frustrated J1-J2 Heisenberg model

Restricted Boltzmann machine (RBM) has been proposed as a powerful variational ansatz to represent the ground state of a given quantum many-body system. On the other hand, as a shallow neural network, it is found that the RBM is still hardly able to capture the characteristics of systems with large sizes or complicated interactions. In order to find a way out of the dilemma, here, we propose to adopt the Green's function Monte Carlo (GFMC) method for which the RBM is used as a guiding wave function. To demonstrate the implementation and effectiveness of the proposal, we have applied the proposal to study the frustrated J₁-J₂ Heisenberg model on a square lattice, which is considered as a typical model with sign problem for quantum Monte Carlo simulations. The calculation results demonstrate that the GFMC method can significantly further reduce the relative error of the ground-state energy on the basis of the RBM variational results. This encourages to combine the GFMC method with other neural networks like convolutional neural networks for dealing with more models with sign problem in the future.     

Deep Learning Quantum States for Hamiltonian Estimation

Human experts cannot efficiently access physical information of a quantum many-body states by simply "reading"its coefficients, but have to reply on the previous knowledge such as order parameters and quantum measurements.We demonstrate that convolutional neural network(CNN) can learn from coefficients of many-body states or reduced density matrices to estimate the physical parameters of the interacting Hamiltonians, such as coupling strengths and magnetic fields, provided the states as the ground states. We propose QubismNet that consists of two main parts: the Qubism map that visualizes the ground states(or the purified reduced density matrices) as images, and a CNN that maps the images to the target physical parameters. By assuming certain constraints on the training set for the sake of balance, QubismNet exhibits impressive powers of learning and generalization on several quantum spin models. While the training samples are restricted to the states from certain ranges of the parameters, QubismNet can accurately estimate the parameters of the states beyond such training regions. For instance, our results show that QubismNet can estimate the magnetic fields near the critical point by learning from the states away from the critical vicinity. Our work provides a data-driven way to infer the Hamiltonians that give the designed ground states, and therefore would benefit the existing and future generations of quantum technologies such as Hamiltonian-based quantum simulations and state tomography.     

基于纠缠态表象的复脊波变换理论

本文基于连续变量量子态构造小波变换的研究结果, 从经典信息的连续脊波变换出发, 利用有序算符内ket-bra型积分, 构造连续复脊波变换对应的量子算符和表象表示, 采用表象的内积运算与态矢投影展开, 研究量子光学态的复脊波变换理论. 关键词： 有序算符内积分技术 复脊波变换 纠缠态表象 相干态     

基于张量网络算法的自旋梯子系统的弦序参量的研究

通过基于矩阵乘积态(MPS)的强关联电子量子自旋梯子格点系统的张量网络(TN)算法,摸索研究自旋梯子量子多体系统的弦序参量,探测系统的量子相变点,刻画系统的量子临界现象,获取系统的量子相图,这为我们提供了一个研究自旋梯子系统的量子多体物理性质强有力的工具和方法:在不知道系统是否缺乏Landau对称性破缺序或者系统是否存在相关的拓扑弦序的情况下,可以先得到系统的基态波函数,如果基态缺乏Landau对称性破缺序,或可以通过其它方式找出系统存在若干非局域的弦序参量,来完整地描述一些拓扑量子相变点,获得系统的量子相图,从而丰富和发展了传统的Landau对称性破缺的相变理论.     

Quantum Prisoner’s Dilemma game on hypergraph networks

We study the possible advantages of adopting quantum strategies in multi-player evolutionary games. We base our study on the three-player Prisoner’s Dilemma (PD) game. In order to model the simultaneous interaction between three agents we use hypergraphs and hypergraph networks. In particular, we study two types of networks: a random network and a SF-like network. The obtained results show that in the case of a three-player game on a hypergraph network, quantum strategies are not necessarily stochastically stable strategies. In some cases, the defection strategy can be as good as a quantum one.     

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.     

Emergence of Objectivity for Quantum Many-Body Systems

We examine the emergence of objectivity for quantum many-body systems in a setting without an environment to decohere the system’s state, but where observers can only access small fragments of the whole system. We extend the result of Reidel (2017) to the case where the system is in a mixed state, measurements are performed through POVMs, and imprints of the outcomes are imperfect. We introduce a new condition on states and measurements to recover full classicality for any number of observers. We further show that evolutions of quantum many-body systems can be expected to yield states that satisfy this condition whenever the corresponding measurement outcomes are redundant.     

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.      

Solving Schrodinger equations using a physically constrained neural network

Deep neural networks (DNNs) and auto differentiation have been widely used in computational physics to solve variational problems. When a DNN is used to represent the wave function and solve quantum many-body problems using variational optimization, various physical constraints have to be injected into the neural network by construction to increase the data and learning efficiency. We build the unitary constraint to the variational wave function using a monotonic neural network to represent the cumulative distribution function (CDF) \begin{document}$F(x) = \int_{-\infty}^{x} \psi^*\psi {\rm d}x'$\end{document}. Using this constrained neural network to represent the variational wave function, we solve Schrodinger equations using auto-differentiation and stochastic gradient descent (SGD) by minimizing the violation of the trial wave function \begin{document}$ \psi(x) $\end{document} to the Schrodinger equation. For several classical problems in quantum mechanics, we obtain their ground state wave function and energy with very low errors. The method developed in the present paper may pave a new way for solving nuclear many-body problems in the future.     

Quantum state sharing with a genuinely entangled five-qubit state and Bell-state measurements

We construct several distinct schemes for tripartite Quantum state sharing (QSTS) of arbitrary single- and two-qubit states. Our schemes use genuinely entangled five-qubit state that has recently been introduced by Brown et al. [J. Phys. A 38 1119 (2005)] as the quantum channel. The Bell-state measurements and the single-qubit measurement are needed in our schemes. In comparison with the QSTS scheme using the same quantum channel [Phys. Rev. A 77 (2008) 032321], not joint measurement, which makes this scheme simpler than the latter.     

Continuous-variable Measurement-device-independent Quantum Relay Network with Phase-sensitive Amplifiers

Continuous-variable (CV) measurement-device-independent (MDI) quantum cryptography is now heading towards solving the practical problem of implementing scalable quantum networks. In this paper, we show that a solution can come from deploying an optical amplifier in the CV-MDI system, aiming to establish a high-rate quantum network. We suggest an improved CV-MDI protocol using the EPR states coupled with optical amplifiers. It can implement a practical quantum network scheme, where the legal participants create the secret correlations by using EPR states connecting to an untrusted relay via insecure links and applying the multi-entangled Greenberger-Horne-Zeilinger (GHZ) state analysis at relay station. Despite the possibility that the relay could be completely tampered with and imperfect links are subject to the powerful attacks, the legal participants are still able to extract a secret key from network communication. The numerical simulation indicates that the quantum network communication can be achieved in an asymmetric scenario, fulfilling the demands of a practical quantum network. Furthermore, we show that the use of optical amplifiers can compensate the inherent imperfections and improve the secret key rate of the CV-MDI system.     

On Isospectral Deformations of an Inhomogeneous String

For an anyon model in two spatial dimensions described by a modular tensor category, the topological S-matrix encodes the mutual braiding statistics, the quantum dimensions, and the fusion rules of anyons. It is nontrivial whether one can compute the S-matrix from a single ground state wave function. Here, we define a class of Hamiltonians consisting of local commuting projectors and an associated matrix that is invariant under local unitary transformations. We argue that the invariant is equivalent to the topological S-matrix. The definition does not require degeneracy of the ground state. We prove that the invariant depends on the state only, in the sense that it can be computed by any Hamiltonian in the class of which the state is a ground state. As a corollary, we prove that any local quantum circuit that connects two ground states of quantum double models (discrete gauge theories) with non-isomorphic abelian groups must have depth that is at least linear in the system’s diameter. As a tool for the proof, a manifestly Hamiltonian-independent notion of locally invisible operators is introduced. This gives a sufficient condition for a many-body state not to be generated from a product state by any small depth quantum circuit; this is a many-body entanglement witness.     

Scheme for teleporting an unknown atomic state to any node in a quantum communication network

We propose a scheme for teleporting an unknown atomic state. In order to realize the teleportation to any node in a quantum communication network, an n-atom Greenberger-Horne-Zeilinger (GHZ) state is needed, which is utilized as the quantum channel. From this n-atom GHZ state, two-node entanglement of processing and receiving teleported states can be obtained through the quantum logic gate manipulation. Finally, for the unequally weighted GHZ state, probabilistic teleportation is shown.