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ردیف | عنوان | نوع |
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21 |
Quantum Computing Based Optimization for Intelligent Reflecting Surface (IRS)-Aided Cell-Free Network
بهینهسازی مبتنی بر محاسبات کوانتومی برای شبکههای بدون سلول با کمک سطح بازتابی هوشمند (IRS)-2022 Intelligent reflecting surface (IRS) enables the control of propagation characteristics and is attracting considerable attention
as a technology to improve energy utilization efficiency in 6th generation mobile communication systems. As cell-free networks with
multiple distributed base stations (BSs) can communicate in a coordinated manner, they are being actively researched as a new
network architecture to resolve the problem of inter-cell interference in conventional cellular networks. The introduction of the IRS into
the cell-free network can avoid shadowing at a lower cost with less power consumption. Thus, in this study, we considered the case of
communication with user equipment (UE) in a shadowing environment using IRS in a cell-free network that contained distributed BSs
with a single antenna. Moreover, the selection of multiple access methods was derived according to the numbers of BSs, IRSs, and
UEs. In addition, we proposed a quadratic unconstrained binary optimization formulation to optimize the IRS reflection coefficient using
quantum computing. The simulation results verified that the application of the proposed method resulted in a more efficient
communication. Thus, this study clarifies that the optimum control method in every communication environment and aims to act as a
stepping stone to optimize the entire cell-free system.
Index Terms: Intelligent Reflecting Surface | Cell-Free Network | Quantum Computing | Quantum Annealing | Combinatorial Optimization. |
مقاله انگلیسی |
22 |
Quantum Computing for Applications in Data Fusion
محاسبات کوانتومی برای برنامه های کاربردی در ترکیب داده ها-2022 Quantum computing promises significant improvements of computation capabilities in various fields such as machine
learning and complex optimization problems. Rapid technological advancements suggest that adiabatic and gate base quantum
computing may see practical applications in the near future. In
this work, we adopt quantum computing paradigms to develop
solvers for two well–known combinatorial optimization problems
in information fusion and resource management: multi-target
data association (MTDA) and weapon target assignment (WTA).
These problems are NP-hard (non-)linear integer programming
optimization tasks which become computationally expensive for
large problem sizes. We derive the problem formulations adapted
for the use in quantum algorithms and present solvers based on
adiabatic quantum computing (AQC) and the Quantum Approximative Optimization Algorithm (QAOA). The feasibility of the models
is demonstrated by numerical simulation and first experiments on
quantum hardware.
Index Terms: adiabatic quantum computing | weapon-target assignment | data association | multi-target tracking | quantum gates | Ising model |
مقاله انگلیسی |
23 |
Quantum computing in power systems
محاسبات کوانتومی در سیستم های قدرت-2022 Electric power systems provide the backbone of modern industrial societies. Enabling scalable grid analytics is the keystone to
successfully operating large transmission and distribution systems. However, today’ s power systems are suffering from everincreasing computational burdens in sustaining the expanding communities and deep integration of renewable energy resources,
as well as managing huge volumes of data accordingly. These unprecedented challenges call for transformative analytics to support
the resilient operations of power systems. Recently, the explosive growth of quantum computing techniques has ignited new hopes
of revolutionizing power system computations. Quantum computing harnesses quantum mechanisms to solve traditionally
intractable computational problems, which may lead to ultra-scalable and efficient power grid analytics. This paper reviews the
newly emerging application of quantum computing techniques in power systems. We present a comprehensive overview of existing
quantum-engineered power analytics from different operation perspectives, including static analysis, transient analysis, stochastic
analysis, optimization, stability, and control. We thoroughly discuss the related quantum algorithms, their benefits and limitations,
hardware implementations, and recommended practices. We also review the quantum networking techniques to ensure secure
communication of power systems in the quantum era. Finally, we discuss challenges and future research directions. This paper will
hopefully stimulate increasing attention to the development of quantum-engineered smart grids.
keywords: Quantum computing | power system | variational quantum algorithms | quantum optimization | quantum machine learning | quantum security. |
مقاله انگلیسی |
24 |
Quantum Distributed Unit Commitment: An Application in Microgrids
تعهد واحد توزیع شده کوانتومی: یک کاربرد در ریزشبکه ها-2022 The dawn of quantum computing brings on a revolution in the way combinatorially complex power system problems such as Unit Commitment are solved. The Unit Commitment
problem complexity is expected to increase in the future because
of the trend toward the increase of penetration of intermittent
renewables. Even though quantum computing has proven effective
for solving a host of problems, its applications for power systems’
problems have been rather limited. In this paper, a quantum unit
commitment is innovatively formulated and the quantum version
of the decomposition and coordination alternate direction method
of multipliers (ADMM) is established. The above is achieved by
devising quantum algorithms and by exploiting the superposition
and entanglement of quantum bits (qubits) for solving subproblems, which are then coordinated through ADMM to obtain feasible
solutions. The main contributions of this paper include: 1) the
innovative development of a quantum model for Unit Commitment;
2) development of decomposition and coordination-supported
framework which paves the way for the utilization of limited
quantum resources to potentially solve the large-scale discrete
optimization problems; 3) devising the novel quantum distributed
unit commitment (QDUC) to solve the problem in a larger scale
than currently available quantum computers are capable of solving.
The QDUC results are compared with those from its classical
counterpart, which validate the efficacy of quantum computing.
Index Terms: Microgrids | quantum computing | quantum distributed optimization | unit commitment. |
مقاله انگلیسی |
25 |
Quantum Embedding Search for Quantum Machine Learning
جستجوی توکار کوانتومی برای یادگیری ماشین کوانتومی-2022 This paper introduces an automated search algorithm (QES, pronounced as ‘‘quest’’), which
derives optimal design of entangling layout for supervised quantum machine learning. First, we establish
the connection between the structures of entanglement using CNOT gates and the representations of
directed multi-graphs, enabling a well-defined search space. The proposed encoding scheme of quantum
entanglement as genotype vectors bridges the ansatz optimization and classical machine learning, allowing
efficient search on any well-defined search space. Second, we instigate the entanglement level to reduce
the cardinality of the search space to a feasible size for practical implementations. Finally, we mitigate the
cost of evaluating the true loss function by using surrogate models via sequential model-based optimization.
We demonstrate the feasibility of our proposed approach on simulated and bench-marking datasets, including
Iris, Wine and Breast Cancer datasets, which empirically shows that found quantum embedding architecture
by QES outperforms manual designs in term of the predictive performance.
INDEX TERMS: Ansatz optimization | quantum embeddings | quantum machine learning | quantum logic gates | quantum neural network | quantum computing. |
مقاله انگلیسی |
26 |
Quantum Federated Learning With Decentralized Data
یادگیری فدرال کوانتومی با داده های غیرمتمرکز-2022 Variational quantum algorithm (VQA) accesses
the centralized data to train the model, and using distributed
computing can significantly improve the training overhead;
however, the data is privacy sensitive. In this paper, we propose
communication-efficient learning of VQA from decentralized data,
which is so-called quantumfederated learning(QFL).Motivated by
the classical federated learning algorithm, we improve data privacy
by aggregating updates from local computation to share model parameters. Here, aiming to find approximate optima in the parameter landscape, we develop an extension of the conventional VQA. Finally, we deploy onthe TensorFlowQuantum processor within variational quantumtensor networks classifiers, approximate quantum
optimization for the Ising model, and variational quantum eigensolver for molecular hydrogen. Our algorithm demonstrates model
accuracy from decentralized data, which have higher performance
on near-term processors. Importantly, QFL may inspire new
investigations in the field of secure quantum machine learning.
Index Terms: Quantum algorithm | quantum computing | quantum information | quantum machine learning. |
مقاله انگلیسی |
27 |
Quantum SVR for Chlorophyll Concentration Estimation in Water With Remote Sensing
-2022 The increasing availability of quantum computers
motivates researching their potential capabilities in enhancing
the performance of data analysis algorithms. Similarly, as in
other research communities, also in remote sensing (RS), it is
not yet defined how its applications can benefit from the usage
of quantum computing (QC). This letter proposes a formulation
of the support vector regression (SVR) algorithm that can be
executed by D-Wave quantum computers. Specifically, the SVR
is mapped to a quadratic unconstrained binary optimization
(QUBO) problem that is solved with quantum annealing (QA).
The algorithm is tested on two different types of computing
environments offered by D-Wave: the advantage system, which
directly embeds the problem into the quantum processing unit
(QPU), and a hybrid solver that employs both classical and
QC resources. For the evaluation, we considered a biophysical
variable estimation problem with RS data. The experimental
results show that the proposed quantum SVR implementation
can achieve comparable or, in some cases, better results than the
classical implementation. This work is one of the first attempts to
provide insight into how QA could be exploited and integrated in
future RS workflows based on machine learning (ML) algorithms.
Index Terms: Quantum annealing (QA) | quantum computing (QC) | quantum machine learning (QML) | remote sensing (RS) | support vector regression (SVR). |
مقاله انگلیسی |
28 |
Reordering and Partitioning of Distributed Quantum Circuits
مرتب سازی مجدد و پارتیشن بندی مدارهای کوانتومی توزیع شده-2022 A new approach to reduce the teleportation cost and execution time in Distributed Quantum
Circuits (DQCs) was proposed in the present paper. DQCs, a well-known solution, have been applied to
solve the problem of maintaining a large number of qubits next to each other. In the distributed quantum
system, the qubits are transferred to another subsystem by a quantum protocol like teleportation. Hence,
a novel method was proposed to optimize the number of teleportation and to reduce the execution time for
generating DQC. To this end, first, the quantum circuit was reordered according to the qubits placement to
improve the computational execution time, and then the quantum circuit was modeled as a graph. Finally,
we combined the genetic algorithm (GA) and the modified tabu search algorithm (MTS) to partition the
graph model in order to obtain a distributed quantum circuit aimed at reducing the number of teleportation
costs. A significant reduction in teleportation cost (TC) and execution time (ET) was obtained in benchmark
circuits. In particular, we performed a more accurate optimization than the previous approaches, and the
proposed approach yielded the best results for several benchmark circuits.
INDEX TERMS: Quantum computing | distributed quantum circuit | optimization | genetic algorithm | teleportation. |
مقاله انگلیسی |
29 |
Resource Management for Edge Intelligence (EI)-Assisted IoV Using Quantum-Inspired Reinforcement Learning
مدیریت منابع برای IoV به کمک هوش لبه (EI) با استفاده از یادگیری تقویتی الهام گرفته از پردازش کوانتومی-2022 Recent developments in the Internet of Vehicles
(IoV) enable interconnected vehicles to support ubiquitous
services. Various emerging service applications are promising to
increase the Quality of Experience (QoE) of users. On-board
computation tasks generated by these applications have heavily
overloaded the resource-constrained vehicles, forcing it to offload
on-board tasks to other edge intelligence (EI)-assisted servers.
However, excessive task offloading can lead to severe competition
for communication and computation resources among vehicles,
thereby increasing the processing latency, energy consumption,
and system cost. To address these problems, we investigate
the transmission-awareness and computing-sense uplink resource
management problem and formulate it as a time-varying Markov
decision process. Considering the total delay, energy consumption, and cost, quantum-inspired reinforcement learning (QRL)
is proposed to develop an intelligence-oriented edge offloading
strategy. Specifically, the vehicle can flexibly choose the network
access mode and offloading strategy through two different radio
interfaces to offload tasks to multiaccess edge computing (MEC)
servers through WiFi and cloud servers through 5G. The objective of this joint optimization is to maintain a self-adaptive
balance between these two aspects. Simulation results show that
the proposed algorithm can significantly reduce the transmission
latency and computation delay.
Index Terms: Cloud computing | edge intelligence (EI) | Internet of Vehicles (IoV) | multiaccess edge computing (MEC) | quantum-inspired reinforcement learning (QRL) |
مقاله انگلیسی |
30 |
Retargetable Optimizing Compilers for Quantum Accelerators via a Multilevel Intermediate Representation
کامپایلرهای بهینه سازی مجدد قابل هدف گیری برای شتاب دهنده های کوانتومی از طریق یک نمایش میانی چند سطحی-2022 We present a multilevel quantum–classical intermediate representation (IR) that
enables an optimizing, retargetable compiler for available quantum languages.
Our work builds upon the multilevel intermediate representation (MLIR)
framework and leverages its unique progressive lowering capabilities to map
quantum languages to the low-level virtual machine (LLVM) machine-level IR.
We provide both quantum and classical optimizations via the MLIR pattern
rewriting subsystem and standard LLVM optimization passes, and demonstrate
the programmability, compilation, and execution of our approach via standard
benchmarks and test cases. In comparison to other standalone language and
compiler efforts available today, our work results in compile times that are
1,000 faster than standard Pythonic approaches, and 5–10 faster than
comparative standalone quantum language compilers. Our compiler provides
quantum resource optimizations via standard programming patterns that result
in a 10 reduction in entangling operations, a common source of program
noise. We see this work as a vehicle for rapid quantum compiler prototyping.
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مقاله انگلیسی |