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Model-Predictive Quantum Control via Hamiltonian Learning
مدل-کنترل کوانتومی پیشبینیکننده از طریق یادگیری همیلتونی-2022 This work proposes an end-to-end framework for the learning-enabled control of closed
quantum systems. The proposed learning technique is the first of its kind to utilize a hierarchical design
which layers probing control, quantum state tomography, quantum process tomography, and Hamiltonian
learning to identify both the internal and control Hamiltonians. Within this context, a novel quantum
process tomography algorithm is presented which involves optimization on the unitary group, i.e., the
space of unitary operators, to ensure physically meaningful predictions. Our scalable Hamiltonian learning
algorithms have low memory requirements and tunable computational complexity. Once the Hamiltonians
are learned, we formalize data-driven model-predictive quantum control (MPQC). This technique utilizes
the learned model to compute quantum control parameters in a closed-loop simulation. Then, the optimized
control input is given to a physical quantum system in an open-loop fashion. Simulations show modelpredictive quantum control to be more efficient than the current state-of-the-art, quantum optimal control,
when sequential quadratic programming (SQP) is used to solve each control problem.
INDEX TERMS: Quantum Hamiltonian learning | quantum process tomography | quantum control | quantum consensus | quantum networks | quantum computing |
مقاله انگلیسی |
2 |
A pointer network based deep learning algorithm for unconstrained binary quadratic programming problem
یک شبکه اشاره گر مبتنی بر الگوریتم یادگیری عمیق برای مسئله برنامه نویسی درجه دوم باینری نامحدود-2020 Combinatorial optimization problems have been widely used in various fields. And many types of com- binatorial optimization problems can be generalized into the model of unconstrained binary quadratic programming (UBQP). Therefore, designing an effective and efficient algorithm for UBQP problems will also contribute to solving other combinatorial optimization problems. Pointer network is an end-to-end sequential decision structure and combines with deep learning technology. With the utilization of the structural characteristics of combinatorial optimization problems and the ability to extract the rule be- hind the data by deep learning, pointer network has been successfully applied to solve several classical combinatorial optimization problems. In this paper, a pointer network based algorithm is designed to solve UBQP problems. The network model is trained by supervised learning (SL) and deep reinforcement learning (DRL) respectively. Trained pointer network models are evaluated by self-generated benchmark dataset and ORLIB dataset respectively. Experimental results show that pointer network model trained by SL has strong learning ability to specific distributed dataset. Pointer network model trained by DRL can learn more general distribution data characteristics. In other words, it can quickly solve problems with great generalization ability. As a result, the framework proposed in this paper for UBQP has great potential to solve large scale combinatorial optimization problems. Keywords: UBQP | Pointer network | Supervised learning | Deep reinforcement learning |
مقاله انگلیسی |
3 |
Heuristic algorithms based on deep reinforcement learning for quadratic unconstrained binary optimization
الگوریتم های ابتکاری مبتنی بر یادگیری تقویتی عمیق برای بهینه سازی باینری بدون محدودیت درجه دوم-2020 The unconstrained binary quadratic programming (UBQP) problem is a difficult combinatorial optimization
problem that has been intensively studied in the past decades. Due to its NP-hardness, many
heuristic algorithms have been developed for the solution of the UBQP. These algorithms are usually
problem-tailored, which lack generality and scalability. To address these issues, a heuristic algorithm
based on deep reinforcement learning (DRLH) is proposed in this paper. It features in inputting
specific features and using a neural network model called NN to guild the selection of variable at
each solution construction step. Also, to improve the algorithm speed and efficiency, two algorithm
variants named simplified DRLH (DRLS) and DRLS with hill climbing (DRLS-HC) are developed as
well. These three algorithms are examined through extensive experiments in comparison with famous
heuristic algorithms from the literature. Experimental results show that the DRLH, DRLS, and DRLS-HC
outperform their competitors in terms of both solution quality and computational efficiency. Precisely,
the DRLH achieves the best-quality results, while DRLS offers a high-quality solution in a very short
time. By adding a hill-climbing procedure to DRLS, the resulting DRLS-HC algorithm is able to obtain
almost the same quality result as DRLH with however 5 times less computing time on average. We
conducted additional experiments on large-scale instances and various data distributions to verify the
generality and scalability of the proposed algorithms, and the results on benchmark instances indicate
the ability of the algorithms to be applied to practical problems. Keywords: Unconstrained binary quadratic | programming | Heuristic algorithm | Deep reinforcement learning | Neural network |
مقاله انگلیسی |
4 |
Intensive quadratic programming approach for home energy management systems with power utility requirements
رویکرد برنامه نویسی درجه دوم فشرده برای سیستم های مدیریت انرژی خانه با نیازهای ابزار برق-2020 This paper proposes a model of a home energy management system (HEMS) to meet utility requirements while
maximizing home profit. It contributes to intensify the flattening effects on the exchanging power pattern with a
constraint of a fair profit reduction among households. The proposed method first uses a normal mixed-integer
linear programming approach to find out the highest profit a household can get under a condition of a generous
power limitation. It is highly possible that the resulted power aggregated from numerous homes may negatively
affect power system operation such as violating voltage limits and overloading transformers. Based on that
highest profit, the utility proposes the same percentage number of profit reduction for all households. Then, each
HEMS performs an intensive mixed-integer quadratic programming optimization to flatten the selling and
buying profiles whilst constraining the home profit reduction to the percentage set by the utility. A simulation
shows that the peak power demand at the substation transformer would reduce about 44% if each household
suffered a reduction of just 10% of the highest possible home profit. Since the flattening effects are improved if
increasing the home profit reduction, our method is a basis for the utility to determine a compensation or
alternative incentives to shave the peak-load and flatten the demand curve. Keywords: Home Energy Management System | Peak-load shaving | Smart household | Smart home | Rooftop solar |
مقاله انگلیسی |
5 |
DQPFS: Distributed quadratic programming based feature selection for big data
DQPFS: انتخاب ویژگی های مبتنی بر برنامه نویسی درجه دوم برای داده های بزرگ-2020 With the advent of the Big data, the scalability of the machine learning algorithms has become more
crucial than ever before. Furthermore, Feature selection as an essential preprocessing technique can
improve the performance of the learning algorithms in confront with large-scale dataset by removing
the irrelevant and redundant features. Owing to the lack of scalability, most of the classical feature
selection algorithms are not so proper to deal with the voluminous data in the Big Data era. QPFS is
a traditional feature weighting algorithm that has been used in lots of feature selection applications.
By inspiring the classical QPFS, in this paper, a scalable algorithm called DQPFS is proposed based on
the novel Apache Spark cluster computing model. The experimental study is performed on three big
datasets that have a large number of instances and features at the same time. Then some assessment
criteria such as accuracy, execution time, speed-up and scale-out are figured. Moreover, to study more
deeply, the results of the proposed algorithm are compared with the classical version QPFS and the
DiRelief, a distributed feature selection algorithm proposed recently. The empirical results illustrate
that proposed method has (a) better scale-out than DiRelief, (b) significantly lower execution time
than DiRelief, (c) lower execution time than QPFS, (d) better accuracy of the Naïve Bayes classifier in
two of three datasets than DiRelief. Keywords: Big data | Apache Spark | Feature selection | Feature ranking | Quadratic programming |
مقاله انگلیسی |
6 |
An optimized energy management strategy for fuel cell hybrid power system based on maximum efficiency range identification
یک استراتژی مدیریت انرژی بهینه برای سیستم قدرتمند هیبریدی سلول سوختی بر اساس شناسایی حداکثر برد بهره وری-2020 This study proposes an optimized energy management strategy (EMS) based on maximum efficiency range (MER)
identification for a fuel cell/battery hybrid sightseeing car. And the aim of this study is to optimize hydrogen
consumption of hybrid system and make sure that the power distribution between the fuel cell (FC) system and
battery is optimal. FC system has the MER and is also a strongly coupled system. The MER of FC system will move
with the change of operating conditions, and consequently, a parameter identification technique is needed to
estimate the boundary powers of MER. This goal is achieved in this paper by using a forgetting factor recursive
least square (FFRLS) online identification approach. Then the sequential quadratic programming (SQP) algorithm
is used to solve the majorization problem of equivalent consumption minimum strategy (ECMS) so that the
FC system operates as much as possible in the MER, while ensuring that the battery state of charge (SOC)
fluctuates within the limited range. This helps to improve the efficiency, performance, and durability of the FC
system and reduce the equivalent hydrogen consumption of the battery. A reduce-scale test platform is designed
to verify the feasibility of the proposed optimized ECMS (OECMS). In addition, the conventional ECMS and rulebased
state machine control (SMC) strategy are utilized in this paper to highlight the advantages of the proposed
strategy. The experiment results show that the proposed OECMS helps to improve FC performance and optimize
system hydrogen consumption. Keywords: Fuel cell (FC) | Fuel cell/battery hybrid sightseeing car | Forgetting factor recursive least square (FFRLS) | Online identification | Equivalent consumption minimum strategy | (ECMS) | Reduce-scale test platform |
مقاله انگلیسی |
7 |
Multi-task least squares twin support vector machine for classification
حداقل مربعات جزئی چند وظیفه ای ماشین بردار پشتیبانی برای طبقه بندی-2019 With the bloom of machine learning, pattern recognition plays an important role in many aspects. How- ever, traditional pattern recognition mainly focuses on single task learning (STL), and the multi-task learning (MTL) has largely been ignored. Compared to STL, MTL can improve the performance of learn- ing methods through the shared information among all tasks. Inspired by the recently proposed di- rected multi-task twin support vector machine (DMTSVM) and the least squares twin support vector ma- chine (LSTWSVM), we put forward a novel multi-task least squares twin support vector machine (MTLS- TWSVM). Instead of two dual quadratic programming problems (QPPs) solved in DMTSVM, our algorithm only needs to deal with two smaller linear equations. This leads to simple solutions, and the calculation can be effectively accelerated. Thus, our proposed model can be applied to the large scale datasets. In addition, it can deal with linear inseparable samples by using kernel trick. Experiments on three popular multi-task datasets show the effectiveness of our proposed methods. Finally, we apply it to two popular image datasets, and the experimental results also demonstrate the validity of our proposed algorithm. Keywords: Pattern recognition | Multi-task learning | Relation learning | Least square twin support vector machine |
مقاله انگلیسی |
8 |
Pattern recognition of SEMG based on wavelet packet transform and improved SVM
تشخیص الگوی SEMG بر اساس تبدیل بسته های موجک و بهبود SVM-2019 The purpose of this paper is to solve the problem of low recognition accuracy of three-degree-offreedom
myoelectric prosthesis and long training time.According to the nonstationarity of the
EMG signal, the wavelet packet is used to decompose the EMG signal and the energy and variance
of the wavelet packet coefficients of the four-channel EMG signal are extracted as feature
vectors.Then Particle Swarm Optimization(PSO) was combined with improved support vector
machine(ISVM) to construct a new model(PSO-ISVM). Under the premise of ensuring the sparseness
of the SVM, the slack variables and the decision function was improved to reduce the
constraint conditions for solving the optimal face in the quadratic programming. SVM is optimized
by the PSO in order to improve the accuracy of the model.The experimental results show
that the improved algorithm can effectively identify six kinds of commonly used upper limb
movements compared with the traditional SVM. The average recognition rate reaches 90.66%
and training time can be shortened 0.042 s. Keywords: Three degrees of freedom electromyographic | prosthesis | EMG | Wavelet packet | SVM | Particle swarm optimization |
مقاله انگلیسی |
9 |
On the multi-product newsvendor with bounded demand distributions
روزنامه فروش چند محصولی با توزیع های محدود تقاضا-2018 We consider a multi-item newsvendor problem with side constraints and common continuous demand distributions that are bounded implying that the items’ profit functions are nondifferentiable. In particular we focus on the cases of uniform and triangular distributions. These distributions naturally describe demand that is guaranteed to exceed a certain threshold – for example, consumption of basic food products or electric power consumption over any given day. Moreover, in practice, it is often difficult to estimate the demand distribution. Accordingly, the uniform and triangular distributions become default modeling choices when only information regarding the bounds and possibly the mode of the distribution is known. We generalize a previous quadratic programming model for uniformly distributed demand on to allow a to be nonzero and to allow the order quantity to be smaller than a. We study the corrected model and propose an efficient algorithm for determining an optimal solution. The algorithm is motivated by a structural result of an upper bound on the number of guaranteed shortage products, which typically appear in multiproduct settings with a positive demand distribution lower bound. The performance of our specialized algorithm is compared to that achieved when solving our formulation with a piecewise quadratic objective using a state-of-the-art standard solver. We also extend the modeling technique to propose a nonlinear programming formulation for triangular demand distributions. A similar approach can be adopted to approximate other demand distributions with a possibly non-finite support, such as truncated normal with strictly positive lower bounds.
keywords: Logistics |Inventory management |Multi-item newsvendor |Quadratic programming |Convex programming |
مقاله انگلیسی |
10 |
الگوریتم بهینه سازی ترکیبی MIGA و NLPQL برای بهینه سازی پارامترهای bus الکتریکی هیبریدی پلاگین
سال انتشار: 2017 - تعداد صفحات فایل pdf انگلیسی: 6 - تعداد صفحات فایل doc فارسی: 11 در این مقاله، اقتصاد سوخت به عنوان هدف بهینه سازی bus الکتریکی هیبریدی پلاگین (PHEB) انتخاب شده است. مدل ریاضی بهینه سازی پارامترهای نیروی برق PHEB است که بر اساس استراتژی مدیریت انرژی مطلوب است و استراتژی مدیریت انرژی این الگوریتم با استفاده از الگوریتم برنامه نویسی پویا (DP) انجام میشود. در مرحله اول، اقتصاد سوخت PHEB به عنوان هدف تابع بهینه سازی پارامتر انتخاب شد. سپس، الگوریتم بهینه سازی ترکیبی توسط الگوریتم ژنتیک چند جزیره (MIGA) و برنامه نویسی مستطیلی NLPQL طراحی شد. در ابتدا MIGA برای بهینه سازی جهانی مورد استفاده قرار گرفت و NLPQL برای بهینه سازی محلی استفاده شد. در نهایت، نتایج آزمایشات نشان داد که مصرف سوخت PHEB در هر 100 کیلومتر از 18.51 لیتر دیزل به 17.41 لیتر دیزلی رسید و مصرف برق در هر 100 کیلومتر، در سطح یکسانی حفظ شد.
کلمات کلیدی: بهینه سازی پارامترها | bus الکتریکی هیبریدی | الگوریتم ژنتیک چند جزیره | برنامه نویسی درجه دوم مرتبه NLPQL |
مقاله ترجمه شده |