quantum computers promise a revolution in informationprocessing and communi- cation. They can be used to better solve optimization problems in fields as diverse as defense, financial trading, and medicine. Convention...
quantum computers promise a revolution in informationprocessing and communi- cation. They can be used to better solve optimization problems in fields as diverse as defense, financial trading, and medicine. Conventional classical computers store information in bits, which can be in one of two states '0' or '1'. Similarly, a quantum computer will operate on quantum bits, known as qubits. A qubit can consist of any superposition of its two states ('0' and '1'), and entanglement between several qubits can be used to implement quantumalgorithms. quantum states are eas- ily destroyed by interaction with their environment, and so they must be isolated from their surroundings, but the preparation and measurement of quantum states require such interactions. Today, there are many mature qubit systems, such as semiconductor qubits, superconducting qubits, or trapped ions. In a semiconductor spin qubit the '0' and '1' are encoded by preparing two different spin states. These qubits have the advantage that their operation and fabrication of gate electrodes are similar to conventional transistors. However, the life time of the spin states, which determines the viability of the qubit, is limited by interactions with nuclear spin in the embedding material as well as magnetic moments due to nearby impurities. Spin qubits in graphene suffer comparatively little from these problems. 99 % of the carbon atoms have zero net nuclear spin, yielding weak spin-orbit and hyperfine interactions. Due to its crystal structure, graphene exhibits, in addition to spin degeneracy, a valley degeneracy, which offers a new platform of rich physics and the possibility of valley qubits. The past five years have seen the first electrostatically defined quantum point contacts and quantum dots in graphene-based nanostructures, resulting in the investigation of the excited state spectrum. We have also seen the first demonstrations of Pauli spin and valley blockade, the in situ tuning from an electron-like
The Internet of Things (IoT) benefits from social networking platforms in establishing and enhancing social-oriented services, information, and autonomous social relationships. Social IoT (SIoT) systems can boost the ...
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The Internet of Things (IoT) benefits from social networking platforms in establishing and enhancing social-oriented services, information, and autonomous social relationships. Social IoT (SIoT) systems can boost the user experience in the real world in several applications, including healthcare, transportation, and entertainment. However, the collected data from various interconnected SIoT systems is massive, demanding robust and efficient processingalgorithms, feature extraction, selection, and inference. This work presents an enhanced Artificial Hummingbird algorithm (AHA) for feature selection (FS). The enhanced version of AHA is performed using the advantages of quantum-based optimization. The main aim of using quantum is to improve the population's exploration ability while discovering feasible regions. Extensive experiments utilizing eighteen UCI datasets were conducted to validate the developed FS method, QAHA. The QAHA is compared with other FS methods, and the experimental established its efficiency. Moreover, a set of four datasets from SIoT are used to evaluate the applicability of QAHA to the real-world setting. The results using these datasets indicate the high performance of QAHA to increase the accuracy by decreasing the number of features. In the case of UCI datasets, the average accuracy of the developed QAHA is 93% among the eighteen datasets. Whereas, In the case of the SIoT datasets, the developed QAHA has an accuracy of nearly 90.7%, 98.7%, 92.2%, and 84.6% for the Trajectory, GAS sensors, Hepatitis, and MovementAAL datasets, respectively.
作者:
Yuki TakeuchiNTT Communication Science Laboratories
<a href="https://***/00berct97">NTT Corporation</a> 3-1 Morinosato Wakamiya Atsugi Kanagawa 243-0198 Japan and NTT Research Center for Theoretical Quantum Information <a href="https://***/00berct97">NTT Corporation</a> 3-1 Morinosato Wakamiya Atsugi Kanagawa 243-0198 Japan.
There are two types of universality in measurement-based quantum computation (MBQC): strict and computational. It is well known that the former is stronger than the latter. We present a method of transforming from a c...
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There are two types of universality in measurement-based quantum computation (MBQC): strict and computational. It is well known that the former is stronger than the latter. We present a method of transforming from a certain type of computationally universal MBQC to a strictly universal one. Our method simply replaces a single qubit in a resource state with a Pauli-Y eigenstate. We applied our method to show that hypergraph states can be made strictly universal with only Pauli measurements, while only computationally universal hypergraph states were known.
Perfect state transfer has attracted a great deal of attention recently due to its crucial role in quantum communication and scalable quantum computation. In this paper, we propose the perfect state transfer algorithm...
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Perfect state transfer has attracted a great deal of attention recently due to its crucial role in quantum communication and scalable quantum computation. In this paper, we propose the perfect state transfer algorithms with a pair of sender-receiver and two pairs of sender-receiver on the complete bipartite graph respectively. The algorithm with a pair of sender-receiver is implemented through discrete-time quantum walk, flexibly setting the coin operators based on the positions of the sender and receiver. The algorithm with two pairs of sender-receiver ensures that the two quantum states are distributed on both sides of the complete bipartite graph during the process, thereby achieving perfect state transfer. In addition, the quantum circuits corresponding to the algorithms are provided. The algorithms can transfer an arbitrary quantum state and can simultaneously transfer two arbitrary quantum states from the senders to the receivers in any case. Moreover, the algorithms are not only applicable to complete bipartite graphs but also to more graph structures with complete bipartite subgraphs, which will provide potential applications for quantuminformationprocessing.
quantum data encoding is a crucial step in harnessing the power of noisy intermediate-scale quantum computers. To efficiently encode classical data into a quantum state, it is essential to find shallow quantum circuit...
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quantum data encoding is a crucial step in harnessing the power of noisy intermediate-scale quantum computers. To efficiently encode classical data into a quantum state, it is essential to find shallow quantum circuits that are resilient to noise and errors. Here we propose a transformer-decoder model that generates optimal quantum circuits for data encoding. We found that our model requires only a small amount of training data to successfully encode classical information into a quantum state, even for previously unseen inputs. This indicates its understanding of the global structure of quantum circuits in the training data. Notably, our trained model can find shallower quantum circuits than those provided during training, suggesting its potential to uncover efficient circuit structures that have not been explored before. This work paves the way for the development of robust and scalable quantumalgorithms.
quantum random walks represent a powerful tool for the implementation of various quantumalgorithms. We consider a convolution problem for the graphs which provide quantum and classical random walks. We suggest a new ...
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quantum random walks represent a powerful tool for the implementation of various quantumalgorithms. We consider a convolution problem for the graphs which provide quantum and classical random walks. We suggest a new method for lattices and hypercycle convolution that preserves quantum walk dynamics. Our method is based on the fact that some graphs represent a result of Kronecker's product of line graphs. We support our methods by means of various numerical experiments that check quantum and classical random walks on hypercycles and their convolutions. Our findings may be useful for saving a significant number of qubits required for algorithms that use quantum walk simulation on quantum devices.
In the fast-paced field of quantum computing, identifying the architectural characteristics that will enable quantum processors to achieve high performance across a diverse range of quantumalgorithms continues to pos...
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In the fast-paced field of quantum computing, identifying the architectural characteristics that will enable quantum processors to achieve high performance across a diverse range of quantumalgorithms continues to pose a significant challenge. Given the extensive and costly nature of experimentally testing different designs, this paper introduces the first Design Space Exploration (DSE) for quantum-dot spin-qubit architectures. Utilizing the upgraded SpinQ compilation framework, this study explores a substantial design space comprising 29,312 spin-qubit-based architectures and applies an innovative optimization tool, ArtA (Artificial Architect), to speed up the design space traversal. ArtA can leverage 17 optimization configurations, significantly reducing exploration times by up to 99.1% compared to a traditional brute force approach while maintaining the same result quality. After a comprehensive evaluation of best-matching optimization configurations per quantum circuit, ArtA suggests specific as well as universal architectural features that provide optimal performance across the examined circuits. Our work demonstrates that combining DSE methodologies with optimization algorithms can be effectively used to generate meaningful design insights for quantum processor development.
quantum walks have emerged as a transformative paradigm in quantuminformationprocessing and can be applied to various graph problems. This study explores discrete-time quantum walks on simplicial complexes, a higher...
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quantum walks have emerged as a transformative paradigm in quantuminformationprocessing and can be applied to various graph problems. This study explores discrete-time quantum walks on simplicial complexes, a higher-order generalization of graph structures. Simplicial complexes, encoding higher-order interactions through simplices, offer a richer topological representation of complex systems. Since the conventional classical random walk cannot directly detect community structures, we present a quantum walk algorithm to detect higher-order community structures called simplicial communities. We utilize the Fourier coin to produce entangled translation states among adjacent simplices in a simplicial complex. The potential of our quantum algorithm is tested on Zachary's karate club network. This study may contribute to understanding complex systems at the intersection of algebraic topology and quantum walk algorithms.
Rough Set Theory (RST) is a commonly used and effective data processing tool. To extend the range of data that RST and its related theories can handle, researchers have proposed many innovative approaches based on the...
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ISBN:
(数字)9798350377613
ISBN:
(纸本)9798350377620;9798350377613
Rough Set Theory (RST) is a commonly used and effective data processing tool. To extend the range of data that RST and its related theories can handle, researchers have proposed many innovative approaches based on the Pawlak model. This paper presents an efficient phased method that incorporates a quantum genetic algorithm to address the problem of attribute reduction in high-dimensional data analysis and accelerate the computation process. The research method is mainly divided into three stages: first, feature importance assessment is used to classify the feature set into three categories: deterministic, chaotic, and redundant, with redundant features being eliminated;second, the quantum genetic algorithm is applied to optimize the chaotic feature set, ensuring effective partitioning while retaining dataset information;third, the core set undergoes fine-tuning to adjust the distribution of specific attributes, making it closely align with the decision distribution, thereby achieving rapid attribute reduction. To further discuss the ability of this reduction algorithm to eliminate redundant attributes, comparative experiments were conducted using four different algorithms, and speed validation was performed using five cancer gene datasets with over 10,000 dimensions. The experimental results show that this method not only effectively reduces computational complexity but also overcomes the limitations of rough set theory, significantly enhancing the ability to handle large datasets, and demonstrates its potential and application prospects in high-dimensional data analysis.
Genetic algorithms are heuristic optimization techniques inspired by Darwinian evolution. quantum computation is a new computational paradigm which exploits quantum resources to speed up informationprocessing tasks. ...
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ISBN:
(纸本)9781665487689
Genetic algorithms are heuristic optimization techniques inspired by Darwinian evolution. quantum computation is a new computational paradigm which exploits quantum resources to speed up informationprocessing tasks. Therefore, it is sensible to explore the potential enhancement in the performance of genetic algorithms by introducing quantum degrees of freedom. Along this line, a modular quantum genetic algorithm has recently been proposed, with individuals encoded in independent registers comprising exchangeable quantum subroutines [1], which leads to different variants. Here, we address the numerical benchmarking of these algorithms against classical genetic algorithms, a comparison missing from previous literature. To overcome the severe limitations of simulating quantumalgorithms, our approach focuses on measuring the effect of quantum resources on the performance. In order to isolate the effect of the quantum resources on the performance, we selected the classical variants to resemble the fundamental characteristics of the quantum genetic algorithms. Under these conditions, we encode an optimization problem in a two-qubit Hamiltonian and face the problem of finding its ground state. A numerical analysis based on a sample of 200 random cases shows that some quantum variants outperform all classical ones in convergence speed towards a near-optimal result. Additionally, we have considered a diagonal Hamiltonian and the Hamiltonian of the hydrogen molecule to complete the analysis with two relevant use-cases. If this advantage holds for larger systems, quantum genetic algorithms would provide a new tool to address optimization problems with quantum computers.
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