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Current quantum processing technology is generally noisy with a limited number of qubits, stressing the importance of quantum state fidelity estimation. The complexity of this problem is mainly due to not only accounting for single gates and readout errors but also for interactions among which. Existing methods generally rely on either reconstructing the given circuit state, ideal state, and computing the distance of which; or forcing the system to be on a specific state. Both rely on conducting circuit measurements, in which computational efficiency is traded off with obtained fidelity details, requiring an exponential number of experiments for full information. This paper poses the question: Is the mapping between a given quantum circuit and its state fidelity learnable? If learnable, this would be a step towards an alternative approach that relies on machine learning, providing much more efficient computation. To answer this question, we propose three deep learning models for 1-, 3-, and 5-qubit circuits and experiment on the following real-quantum processors: ibmq_armonk (1-qubit), ibmq_lima (5-qubit) and ibmq_quito (5-qubit) backends, respectively. Our models achieved a mean correlation factor of 0.74, 0.67 and 0.66 for 1-, 3-, and 5-qubit random circuits, respectively, with the exponential state tomography method. Additionally, our 5-qubit model outperforms simple baseline state fidelity estimation method on three quantum benchmarks. Our method, trained on random circuits only, achieved a mean correlation factor of 0.968 while the baseline method achieved 0.738. Furthermore, we investigate the effect of dynamic noise on state fidelity estimation. The correlation factor substantially improved to 0.82 and 0.74 for the 3- and 5-qubit models, respectively. The results show that machine learning is promising for predicting state fidelity from circuit representation and this work may be considered a step towards efficient end-to-end learning. [ABSTRACT FROM AUTHOR]

Disruption of quorum sensing (QS) system, which is a central regulator for pathogenesis of Pseudomonas aeruginosa, is referring to as quorum quenching (QQ). This study was undertaken to evaluate and enhance the anti-quorum sensing (AQS) potential of probiotic strain Lactobacillus rhamnosus GG. The cell-free supernatant (CFS) of this probiotic strain showed anti-quorum sensing activity against Pseudomonas aeruginosa, which was determined using well-diffusion agar-plate assay. Anti-quorum sensing potential of L. rhamnosus GG was enhanced by optimization of various cultural conditions using classical and statistical optimization approaches. Six variables were optimized using one-variable-at-a-time (OVAT) method. Four significant variables, viz., temperature, pH, incubation time, metal ion, and its concentration, were chosen for further optimization by response surface methodology (RSM) using central composite design (CCD). Analysis of variance (ANOVA) demonstrated that the regression model is highly significant, as indicated by F test with a low probability value (p < 0.0002) and high value of coefficient of determination (0.8738) and also had significant influence on the generation of anti-quorum sensing effector molecules. Maximum production of anti-quorum sensing activity, in terms of zones of inhibition, was achieved under the following optimized conditions such as 37 °C temperature, pH 6.5, incubation time 24 h, and 2.5 mM concentration of zinc sulfate (ZnSO4). The quadratic model predicted 1.3-fold increase anti-quorum sensing activity production over un-optimized cultural conditions. The present research is the first report representing the enhancement of anti-quorum sensing potential of L. rhamnosus GG. [ABSTRACT FROM AUTHOR]

We present a comparison study of state-of-the-art classical optimization methods to a D-Wave 2000Q quantum annealer for the scheduling of agile Earth observation satellites. The problem is to acquire high-value images while obeying the attitude maneuvering constraint of the satellite. In order to investigate close to real-world problems, we created benchmark problems by simulating realistic scenarios. Our results show that a tuned quantum annealing approach can run faster when used to find the optimal solution than a classical exact solver for some of the problem instances. Moreover, we find that the solution quality of the quantum annealer is comparable to the heuristic method used operationally for small problem instances, but degrades rapidly due to the limited precision of the quantum annealer. [ABSTRACT FROM AUTHOR]

Although there are multiple methodologies to carry out collaborative practices of inventory management, none are set up for impulse purchase products. This is a disadvantage because with the opening of new markets and the proliferation of consumer culture, the economic importance of buying products on impulse always remains relevant. In this paper, a Vendor Managed Inventory model was designed based on the direct participation of a vendor and a buyer (two-level supply chain), in order to agree on the procurement operations of a portfolio of impulse purchase products. For this proposal, a mathematical model based on classical optimization was designed to minimize inventory costs. Subsequently, a case study was conducted comparing the economic impact of the model with respect to a traditional supply agreement in a non-cooperative scenario. The results reflected positive economic effects in the implementation of the model related to the economies of scale to exploit fixed costs present in the agreement. Additionally, the conditions under which the implementation of this model grants individual and global benefits to the participating companies were validated. [ABSTRACT FROM AUTHOR]

We present the mapping of a class of simplified air traffic management problems (strategic conflict resolution) to quadratic unconstrained Boolean optimization problems. The mapping is performed through an original representation of the conflict-resolution problem in terms of a conflict graph, where the nodes of the graph represent flights and the edges represent a potential conflict between flights. The representation allows a natural decomposition of a real-world instance related to wind-optimal trajectories over the Atlantic Ocean into smaller subproblems that can be discretized and are amenable to be programmed in quantum annealers. In this paper, we tested the new programming techniques, and we benchmark the hardness of the instances using both classical solvers and the D-Wave 2X and D-Wave 2000Q quantum chip. The preliminary results show that for reasonable modeling choices, the most challenging subproblems which are programmable in the current devices are solved to optimality with 99% of probability within a second of annealing time. [ABSTRACT FROM AUTHOR]

In the fields which require finding the most appropriate value, optimization became a vital approach to employ effective solutions. With the use of optimization techniques, many different fields in the modern life have found solutions to their real-world based problems. In this context, classical optimization techniques have had an important popularity. But after a while, more advanced optimization problems required the use of more effective techniques. At this point, Computer Science took an important role on providing software related techniques to improve the associated literature. Today, intelligent optimization techniques based on Artificial Intelligence are widely used for optimization problems. The objective of this paper is to provide a comparative study on the employment of classical optimization solutions and Artificial Intelligence solutions for enabling readers to have idea about the potential of intelligent optimization techniques. At this point, two recently developed intelligent optimization algorithms, Vortex Optimization Algorithm (VOA) and Cognitive Development Optimization Algorithm (CoDOA), have been used to solve some multidisciplinary optimization problems provided in the source book Thomas Calculus 11th Edition and the obtained results have compared with classical optimization solutions. [ABSTRACT FROM AUTHOR]

Many areas in power systems require solving one or more nonlinear optimization problems. While analytical methods might suffer from slow convergence and the curse of dimensionality, heuristics-based swarm intelligence can be an efficient alternative. Particle swarm optimization (PSO), part of the swarm intelligence family, is known to effectively solve large-scale nonlinear optimization problems. This paper presents a detailed overview of the basic concepts of PSO and its variants. Also, it provides a comprehensive survey on the power system applications that have benefited from the powerful nature of PSO as an optimization technique. For each application, technical details that are required for applying PSO, such as its type, particle formulation (solution representation), and the most efficient fitness functions are also discussed. [ABSTRACT FROM AUTHOR]

B-splines have today become the industry standard for CAD data representation. Freeform shape synthesis from point cloud data is an emerging technique. This predominantly involves B-spline curve / surface fitting to the point cloud data to obtain the CAD definitions. Accurate curve and surface fit-ting from point clouds needs estimation of order, i.e. number of knots and a good parameterization model, i.e. the determination of parameter values of the digitized points in order to perform least squares (LSQ) fitting. Numerous work have been done on selection of such parameters. Nevertheless, the problem of LSQ with optimal knots has not been addressed in totality. Simultaneous optimization of number of knots and parameter values leads to multiple contradictory objectives and traditional optimization is prone to fail. The present work proposes a hybrid approach based on genetic algorithms, for optimal number of knots and optimal parameter allocation, simultaneously, for curve and surface fitting. A novel population initial-ization scheme involving analytical and neural network estimation is also proposed here, ensuring that the optimization procedure is both global in nature and computationally less expensive. Further classical opti-mization of parameters alone based on error is carried if required. The present study of parameterization is for Non Uniform B-spline fitting. [ABSTRACT FROM AUTHOR]

• Metaheuristics and classical methods are compared for the reconfiguration problem. • New metrics are proposed to compare methods to solve the reconfiguration problem. • Metrics allow a fair comparison among different computational implementations. • Metaheuristics have superior computational efficiency to solve the reconfiguration. • Classical methods achieve the optimum solution for low and medium-size systems. Reconfiguration is a complex combinatorial problem in which the topology of distribution systems is modified by the opening/closing of interconnection switches aiming techno-economic benefits (e.g., minimization of losses). Numerous optimization methods have been developed to solve the reconfiguration problem, although a comparative analysis of their performances is still a challenging task due to the nature of the methods, differences in their implementation, and used computational equipment. To fulfill that gap, this paper assesses classical models along with metaheuristics already applied in the specialized literature considering the reported losses and computational effort. To eliminate differences due to implementation and equipment, two proposed metrics are assessed using a reference specialized power flow: 'equivalent time' and 'equivalent number of power flows'. The quality of the solutions was compared for standard test systems (33, 136, and 417 buses) and a ranking of the methods was produced. It was concluded that linear and conic programming models find the optimal solution for low and medium-size systems; moreover, the linear model requires lower computational effort than the conic and the nonlinear programming formulations. On the other hand, it was verified that metaheuristics need lower computational effort and provide better solutions for large-size systems compared to classical optimization. [ABSTRACT FROM AUTHOR]