基于量子粒子群算法的自适应随机共振方法研究

为提升随机共振理论在微弱信号检测领域中的实用性,以随机共振系统参数为研究对象,提出了基于量子粒子群算法的自适应随机共振方法.首先将自适应随机共振问题转化为多参数并行寻优问题,然后分别在Langevin系统和Duffing振子系统下进行仿真实验.在Langevin系统中,将量子粒子群算法和描点法进行了寻优结果对比;在Duffing振子系统中,Duffing振子系统的寻优结果则直接与Langevin系统的寻优结果进行了对比.实验结果表明:在寻优结果和寻优效率上,基于量子粒子群算法的自适应随机共振方法要明显高于描点法;在相同条件下,Duffing振子系统的寻优结果要优于Langevin系统的寻优结果;在两种系统下,输入信号信噪比越低就越能体现出量子粒子群算法的优越性.最后还对随机共振系统参数的寻优结果进行了规律性总结.     

基于量子粒子群算法的混沌系统参数辨识

针对混沌系统参数辨识问题, 在基本群智能算法粒子群优化算法的基础上, 提出量子粒子群算法, 测试函数证明了算法具有良好的全局优化能力. 进而将其应用于混沌系统参数辨识问题, 将参数辨识问题转化为多维函数空间上的优化问题. 通过对平衡板热对流典型混沌系统Lorenz系统进行研究, 并与基本算法和遗传算法比较. 仿真实验证明, 算法的有效性, 对混沌理论的发展有着非常重要的意义. 关键词： 量子粒子群算法 混沌系统 系统辨识     

基于量子并行粒子群优化算法的分数阶混沌系统参数估计

分数阶混沌系统参数估计的本质是多维参数优化问题, 其对于实现分数阶混沌控制与同步至关重要. 提出一种基于量子并行特性的粒子群优化新算法, 用于解决分数阶混沌的系统参数估计问题. 利用量子计算的并行特性, 设计出了一种新的量子编码, 使每代运算的可计算次数呈指数增加. 在此基础上, 构建了由量子当前旋转角、个体最优旋转角和全局最优旋转角共同组成的粒子演化方程, 以约束粒子在量子空间中的运动行为, 使算法的搜索能力得到了较大提高. 以分数阶Lorenz混沌系统和分数阶Chen混沌系统的参数估计为例, 进行了未知参数估计的数值仿真, 结果显示本算法具有良好的有效性、鲁棒性和通用性.     

混沌量子克隆优化求解认知无线网络决策引擎

通过分析认知无线网络引擎决策, 给出了其数学模型, 并将其转化为一个多目标优化问题, 进而提出一种基于混沌量子克隆的优化求解算法, 并证明了该算法以概率1收敛. 算法采用量子编码, 利用Logistic映射初始化抗体种群, 设计了一种基于混沌扰动的量子变异方案. 最后, 在多载波环境下对算法进行了仿真实验. 结果表明, 与QGA-CE(基于量子遗传算法的认知引擎)算法相比, 本文算法收敛速度较快, 具有较高的目标函数值, 可以对无线参数优化调整, 满足认知引擎的实时性要求.     

Markov entropy decomposition: a variational dual for quantum belief propagation

We present a lower bound for the free energy of a quantum many-body system at finite temperature. This lower bound is expressed as a convex optimization problem with linear constraints, and is derived using strong subadditivity of von Neumann entropy and a relaxation of the consistency condition of local density operators. The dual to this minimization problem leads to a set of quantum belief propagation equations, thus providing a firm theoretical foundation to that approach. The minimization problem is numerically tractable, and we find good agreement with quantum Monte Carlo calculations for spin-1/2 Heisenberg antiferromagnet in two dimensions. This lower bound complements other variational upper bounds. We discuss applications to Hamiltonian complexity theory and give a generalization of the structure theorem of [P. Hayden et al., Commun. Math. Phys. 246, 359 (2004).] to trees in an appendix.     

Optimization through quantum annealing: theory and some applications

Quantum annealing is a promising tool for solving optimization problems, similar in some ways to the traditional (classical) simulated annealing of Kirkpatrick et al. Simulated annealing takes advantage of thermal fluctuations in order to explore the optimization landscape of the problem at hand, whereas quantum annealing employs quantum fluctuations. Intriguingly, quantum annealing has been proved to be more effective than its classical counterpart in many applications. We illustrate the theory and the practical implementation of both classical and quantum annealing – highlighting the crucial differences between these two methods – by means of results recently obtained in experiments, in simple toy-models, and more challenging combinatorial optimization problems (namely, Random Ising model and Travelling Salesman Problem). The techniques used to implement quantum and classical annealing are either deterministic evolutions, for the simplest models, or Monte Carlo approaches, for harder optimization tasks. We discuss the pro and cons of these approaches and their possible connections to the landscape of the problem addressed.     

基于最少中继节点约束的量子VoIP路由优化策略

量子信息的传输过程中,由于拥塞、链路故障等原因,导致数据分组在路由器排队,产生时延、丢包.为了保证量子Vo IP系统的性能,本文提出了基于最少中继节点约束的路由优化策略.采用基于纠缠交换的中继技术,通过优先选择最少中继节点的量子信道,实现多用户量子Vo IP通信.理论分析和仿真结果表明,当链路出现故障和拥塞时,基于M/M/m型排队系统,采用本策略,当设定量子比特的误码率为0.2,共用信道数目从4增加到8时,量子网络的呼损率由0.25下降到0.024,量子网络的最大吞吐量由64 kbps增加到132 kbps.当设定共用信道数目为4,控制量子比特的误码率从0.3到0.1时,可使量子网络最大吞吐量从41 kbps增加到140 kbps.由此可见,本策略能够极大地提高量子Vo IP网络的性能.     

Quantum computational complexity of the N-representability problem: QMA complete

We study the computational complexity of the N-representability problem in quantum chemistry. We show that this problem is quantum Merlin-Arthur complete, which is the quantum generalization of nondeterministic polynomial time complete. Our proof uses a simple mapping from spin systems to fermionic systems, as well as a convex optimization technique that reduces the problem of finding ground states to N representability.     

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.     

Does adiabatic quantum optimization fail for NP-complete problems?

It has been recently argued that adiabatic quantum optimization would fail in solving NP-complete problems because of the occurrence of exponentially small gaps due to crossing of local minima of the final Hamiltonian with its global minimum near the end of the adiabatic evolution. Using perturbation expansion, we analytically show that for the NP-hard problem known as maximum independent set, there always exist adiabatic paths along which no such crossings occur. Therefore, in order to prove that adiabatic quantum optimization fails for any NP-complete problem, one must prove that it is impossible to find any such path in polynomial time.     

基于云模型的自适应量子免疫克隆算法

利用云模型能够兼顾随机性和模糊性的品质,提出一种基于云模型的自适应量子免疫克隆算法.使用云算子代替通用的量子旋转门这一量子进化算法核心算子用于寻优变异操作;通过控制云算子间的协作,实现算法在进化过程中对搜索范围的动态调整,使算法具有较强的全局搜索能力;同时,补充针对性的优化方案,有效避免了算法陷入局部最优.对标准数值优化问题的仿真对比实验表明,该算法具有寻优能力强、搜索精度高、稳定度好等优点;对非线性系统的参数估计仿真实验,该算法也取得了对参数的高精度有效估计.     

Experimental implementation of an adiabatic quantum optimization algorithm

We report the realization of a nuclear magnetic resonance computer with three quantum bits that simulates an adiabatic quantum optimization algorithm. Adiabatic quantum algorithms offer new insight into how quantum resources can be used to solve hard problems. This experiment uses a particularly well-suited three quantum bit molecule and was made possible by introducing a technique that encodes general instances of the given optimization problem into an easily applicable Hamiltonian. Our results indicate an optimal run time of the adiabatic algorithm that agrees well with the prediction of a simple decoherence model.     

Feature Selection for Recommender Systems with Quantum Computing

The promise of quantum computing to open new unexplored possibilities in several scientific fields has been long discussed, but until recently the lack of a functional quantum computer has confined this discussion mostly to theoretical algorithmic papers. It was only in the last few years that small but functional quantum computers have become available to the broader research community. One paradigm in particular, quantum annealing, can be used to sample optimal solutions for a number of NP-hard optimization problems represented with classical operations research tools, providing an easy access to the potential of this emerging technology. One of the tasks that most naturally fits in this mathematical formulation is feature selection. In this paper, we investigate how to design a hybrid feature selection algorithm for recommender systems that leverages the domain knowledge and behavior hidden in the user interactions data. We represent the feature selection as an optimization problem and solve it on a real quantum computer, provided by D-Wave. The results indicate that the proposed approach is effective in selecting a limited set of important features and that quantum computers are becoming powerful enough to enter the wider realm of applied science.     

Variational quantum eigensolvers by variance minimization

The original variational quantum eigensolver (VQE) typically minimizes energy with hybrid quantum-classical optimization that aims to find the ground state. Here, we propose a VQE based on minimizing energy variance and call it the variance-VQE, which treats the ground state and excited states on the same footing, since an arbitrary eigenstate for a Hamiltonian should have zero energy variance. We demonstrate the properties of the variance-VQE for solving a set of excited states in quantum chemistry problems. Remarkably, we show that optimization of a combination of energy and variance may be more efficient to find low-energy excited states than those of minimizing energy or variance alone. We further reveal that the optimization can be boosted with stochastic gradient descent by Hamiltonian sampling, which uses only a few terms of the Hamiltonian and thus significantly reduces the quantum resource for evaluating variance and its gradients.     

Solving a class of continuous global optimization problems using quantum algorithms

We investigate the entwined roles that additional information and quantum algorithms play in reducing the complexity of a class of global optimization problems (GOP). We show that: (i) a modest amount of additional information is sufficient to map the continuous GOP into the (discrete) Grover problem; (ii) while this additional information is actually available in some GOPs, it cannot be taken advantage of within classical optimization algorithms; and (iii) quantum algorithms offer a natural framework for the efficient use of this information resulting in a speed-up of the solution of the GOP.     

Optimal unambiguous discrimination of pure quantum states using SDP method

In the present paper, an exact analytic solution for the optimal unambiguous state discrimination(OPUSD) problem involving an arbitrary number of pure linearly independent quantum states with real and complex inner product is presented. Using semidefinite programming and Karush-Kuhn-Tucker convex optimization method, we derive an analytical formula which shows the relation between optimal solution of unambiguous state discrimination problem and an arbitrary number of pure linearly independent quantum states.     

Effect of local minima on adiabatic quantum optimization

We present a perturbative method to estimate the spectral gap for adiabatic quantum optimization, based on the structure of the energy levels in the problem Hamiltonian. We show that, for problems that have an exponentially large number of local minima close to the global minimum, the gap becomes exponentially small making the computation time exponentially long. The quantum advantage of adiabatic quantum computation may then be accessed only via the local adiabatic evolution, which requires phase coherence throughout the evolution and knowledge of the spectrum. Such problems, therefore, are not suitable for adiabatic quantum computation.     

Optimizing linear optics quantum gates

In this Letter, the problem of finding optimal success probabilities of linear optics quantum gates is linked to the theory of convex optimization. It is shown that by exploiting this link, upper bounds for the success probability of networks realizing single-mode gates can be derived, which hold in generality for postselected networks of arbitrary size, any number of auxiliary modes, and arbitrary photon numbers. As a corollary, the previously formulated conjecture is proven that the optimal success probability of a nonlinear sign shift without feedforward is 1/4, a gate playing the central role in the scheme of Knill-Laflamme-Milburn for quantum computation. The concept of Lagrange duality is shown to be applicable to provide rigorous proofs for such bounds, although the original problem is a difficult nonconvex problem in infinitely many objective variables. The versatility of this approach is demonstrated.     

Quantum coherence,evolution of the Wigner function,and transition from quantum to classical dynamics for a chaotic system

The paper deals with dynamics of a quantum chaotic system under influence of an environment. The effect of an environment is known to destroy the quantum coherence and can convert the quantum dynamics of a system to classical. We use a semiclassical technique for studying the process of decoherence. The condition for transition from quantum to classical dynamics is obtained in general form and checked numerically for a particular chaotic system, known as quantum the standard map on a torus. The relevance of the obtained results to the problem of correspondence between quantum and classical mechanics is briefly discussed. (c) 1996 American Institute of Physics.