scholarly journals A Bayesian Network-based Quantum procedure for Failure Risk Analysis

2021 ◽  
Author(s):  
Ginés Carrascal de las Heras ◽  
Guillermo Botella ◽  
Alberto A Del Barrio ◽  
David Kremer
Keyword(s):  
Monte Carlo ◽  
Quantum Computer ◽  
Distribution Networks ◽  
Quantum Circuit ◽  
Interconnected Systems ◽  
Single Quantum ◽  
Failure Risk ◽  
Electrical Distribution Networks ◽  
Restricted Representation ◽  
Quantum Procedure

<div> <div> <div> <p>Studying the propagation of failure probabilities in interconnected systems like that of electrical distribution networks is traditionally performed by means of Monte Carlo simulations. In this paper, we propose a procedure to create a model of the system in a quantum computer using a restricted representation of Bayesian networks. Some examples of this implementation on sample models are presented using Qiskit and tested using both quantum simulators and IBM Quantum hardware. Results show a correlation in the precision of the results when considering the number of Monte Carlo iterations alongside the sum of shots in a single quantum circuit execution. </p> </div> </div> </div>

scholarly journals A Bayesian Network-based Quantum procedure for Failure Risk Analysis

2021 ◽  
Author(s):  
Ginés Carrascal de las Heras ◽  
Guillermo Botella ◽  
Alberto A Del Barrio ◽  
David Kremer
Keyword(s):  
Monte Carlo ◽  
Quantum Computer ◽  
Distribution Networks ◽  
Quantum Circuit ◽  
Interconnected Systems ◽  
Single Quantum ◽  
Failure Risk ◽  
Electrical Distribution Networks ◽  
Restricted Representation ◽  
Quantum Procedure

<div> <div> <div> <p>Studying the propagation of failure probabilities in interconnected systems like that of electrical distribution networks is traditionally performed by means of Monte Carlo simulations. In this paper, we propose a procedure to create a model of the system in a quantum computer using a restricted representation of Bayesian networks. Some examples of this implementation on sample models are presented using Qiskit and tested using both quantum simulators and IBM Quantum hardware. Results show a correlation in the precision of the results when considering the number of Monte Carlo iterations alongside the sum of shots in a single quantum circuit execution. </p> </div> </div> </div>

A study on appropriate investment of pipeline rehabilitation for water distribution network

2005 ◽  
Vol 5 (2) ◽  
pp. 31-38
Author(s):  
A. Asakura ◽  
A. Koizumi ◽  
O. Odanagi ◽  
H. Watanabe ◽  
T. Inakazu
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Water Supply ◽  
Water Distribution ◽  
Ad Hoc ◽  
Distribution Network ◽  
Water Distribution Network ◽  
Distribution Networks ◽  
Optimal Basis ◽  
Model Case

In Japan most of the water distribution networks were constructed during the 1960s to 1970s. Since these pipelines were used for a long period, pipeline rehabilitation is necessary to maintain water supply. Although investment for pipeline rehabilitation has to be planned in terms of cost-effectiveness, no standard method has been established because pipelines were replaced on emergency and ad hoc basis in the past. In this paper, a method to determine the maintenance of the water supply on an optimal basis with a fixed budget for a water distribution network is proposed. Firstly, a method to quantify the benefits of pipeline rehabilitation is examined. Secondly, two models using Integer Programming and Monte Carlo simulation to maximize the benefits of pipeline rehabilitation with limited budget were considered, and they are applied to a model case and a case study. Based on these studies, it is concluded that the Monte Carlo simulation model to calculate the appropriate investment for the pipeline rehabilitation planning is both convenient and practical.

scholarly journals Connectivity matrix model of quantum circuits and its application to distributed quantum circuit optimization

2021 ◽  
Vol 20 (7) ◽  
Author(s):  
Ismail Ghodsollahee ◽  
Zohreh Davarzani ◽  
Mariam Zomorodi ◽  
Paweł Pławiak ◽  
Monireh Houshmand ◽  
...  
Keyword(s):  
Quantum Computation ◽  
Quantum Computer ◽  
Quantum Circuit ◽  
Quantum Circuits ◽  
Connectivity Matrix ◽  
Circuit Optimization ◽  
Novel Approach ◽  
The Matrix ◽  
Minimum Number ◽  
Two Phases

AbstractAs quantum computation grows, the number of qubits involved in a given quantum computer increases. But due to the physical limitations in the number of qubits of a single quantum device, the computation should be performed in a distributed system. In this paper, a new model of quantum computation based on the matrix representation of quantum circuits is proposed. Then, using this model, we propose a novel approach for reducing the number of teleportations in a distributed quantum circuit. The proposed method consists of two phases: the pre-processing phase and the optimization phase. In the pre-processing phase, it considers the bi-partitioning of quantum circuits by Non-Dominated Sorting Genetic Algorithm (NSGA-III) to minimize the number of global gates and to distribute the quantum circuit into two balanced parts with equal number of qubits and minimum number of global gates. In the optimization phase, two heuristics named Heuristic I and Heuristic II are proposed to optimize the number of teleportations according to the partitioning obtained from the pre-processing phase. Finally, the proposed approach is evaluated on many benchmark quantum circuits. The results of these evaluations show an average of 22.16% improvement in the teleportation cost of the proposed approach compared to the existing works in the literature.

scholarly journals Practical Quantum Computing

2021 ◽  
Vol 2 (1) ◽  
pp. 1-35
Author(s):  
Adrien Suau ◽  
Gabriel Staffelbach ◽  
Henri Calandra
Keyword(s):  
Wave Equation ◽  
Quantum Computer ◽  
Quantum Algorithms ◽  
Quantum Algorithm ◽  
Quantum Circuit ◽  
Equation Solving ◽  
Small Quantum ◽  
Quantum Wave ◽  
Variational Algorithms ◽  
The One

In the last few years, several quantum algorithms that try to address the problem of partial differential equation solving have been devised: on the one hand, “direct” quantum algorithms that aim at encoding the solution of the PDE by executing one large quantum circuit; on the other hand, variational algorithms that approximate the solution of the PDE by executing several small quantum circuits and making profit of classical optimisers. In this work, we propose an experimental study of the costs (in terms of gate number and execution time on a idealised hardware created from realistic gate data) associated with one of the “direct” quantum algorithm: the wave equation solver devised in [32]. We show that our implementation of the quantum wave equation solver agrees with the theoretical big-O complexity of the algorithm. We also explain in great detail the implementation steps and discuss some possibilities of improvements. Finally, our implementation proves experimentally that some PDE can be solved on a quantum computer, even if the direct quantum algorithm chosen will require error-corrected quantum chips, which are not believed to be available in the short-term.

scholarly journals Error mitigation with Clifford quantum-circuit data

Quantum ◽  
2021 ◽  
Vol 5 ◽  
pp. 592
Author(s):  
Piotr Czarnik ◽  
Andrew Arrasmith ◽  
Patrick J. Coles ◽  
Lukasz Cincio
Keyword(s):  
State Energy ◽  
Quantum Computer ◽  
Quantum Circuit ◽  
Training Data ◽  
Quantum Circuits ◽  
Accurate Estimation ◽  
Error Mitigation ◽  
Order Of Magnitude ◽  
Quantum Observables ◽  
Near Term

Achieving near-term quantum advantage will require accurate estimation of quantum observables despite significant hardware noise. For this purpose, we propose a novel, scalable error-mitigation method that applies to gate-based quantum computers. The method generates training data {Xinoisy,Xiexact} via quantum circuits composed largely of Clifford gates, which can be efficiently simulated classically, where Xinoisy and Xiexact are noisy and noiseless observables respectively. Fitting a linear ansatz to this data then allows for the prediction of noise-free observables for arbitrary circuits. We analyze the performance of our method versus the number of qubits, circuit depth, and number of non-Clifford gates. We obtain an order-of-magnitude error reduction for a ground-state energy problem on 16 qubits in an IBMQ quantum computer and on a 64-qubit noisy simulator.

scholarly journals Influence of Clandestine Connections on Energy Loss Evaluation in Electrical Distribution Networks

2020 ◽  
Author(s):  
Clainer B. Donadel ◽  
Gilberto C. D. Sousa ◽  
Flávio M. Varejão
Keyword(s):  
Artificial Intelligence ◽  
Developing Countries ◽  
Energy Loss ◽  
Loss Factor ◽  
Distribution Systems ◽  
Distribution Network ◽  
Distribution Networks ◽  
Operation Costs ◽  
Electrical Distribution ◽  
Electrical Distribution Networks

In the literature, there are several methodologies to estimate technical losses in electrical distribution networks. The range of techniques is broad, ranging from basic techniques (based on loss factor, for example) to sophisticated ones (based on artificial intelligence). These methodologies are important, because the costs of technical losses represent a huge part of the total operation costs of distribution network operators (DNOs). However, the presence of clandestine connections, common in developing countries, was not considered in the methodologies encountered in the literature. Clandestine connections occur when a consumer has made his/her connection without DNO permission. In these cases, the amount of energy consumed by a clandestine "consumer" is a nontechnical loss (and, therefore, should be correctly computed as nonbilled energy). Therefore, a new methodology is proposed to consider the presence of clandestine connections in energy loss estimation in distribution systems.

scholarly journals Optimal Segmentation of Electrical Distribution Networks

2021 ◽  
Vol 19 (8) ◽  
pp. 1375-1382
Author(s):  
Carlos Bonetti ◽  
Jezabel Bianchotti ◽  
Jorge Vega ◽  
Gabriel Puccini
Keyword(s):  
Distribution Networks ◽  
Electrical Distribution ◽  
Electrical Distribution Networks ◽  
Optimal Segmentation
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