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quantum measurement detection algorithms

quantum information processing 2022年第8期21卷 1-38页

作者： Lugilde Fernandez, Guillermo Combarro, Elias F. Rua, Ignacio F. Univ Oviedo Math Dept Oviedo Spain Univ Oviedo Comp Sci Dept Oviedo Spain

In this paper, we introduce and study the quantum measurement detection algorithms (QMDA), whose objective is to detect whether unwanted measurements are being taken in a quantum circuit or not by applying the Zeno effect. A QMDA is a quantum circuit that includes three unitary matrices, one of them being applied numerous times consecutively, and whose initial state is fixed when no foreign measurements occur. One example is the Elitzur-Vaidman bomb tester, which is generalized by the QMDA definition, allowing the detection of measurements that are taken in an unknown basis and in circuits with an arbitrary number of qubits. We prove some key properties and limitations of these algorithms, as well as studying the performance of the Elitzur-Vaidman bomb tester and its possible improvements. Some extensions of the definition would lead to algorithms such as the counterfactual communication one.

关键词： quantum measurement detection algorithms quantum detection quantum measurements Elitzur-Vaidman bomb tester Zeno effect Counterfactual communication

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Configurable sublinear circuits for quantum state preparation

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quantum information processing 2023年第2期22卷 1-27页

作者： Araujo, Israel F. Park, Daniel K. Ludermir, Teresa B. Oliveira, Wilson R. Petruccione, Francesco da Silva, Adenilton J. Univ Fed Pernambuco Ctr Informat BR-50740560 Recife PE Brazil Yonsei Univ Dept Appl Stat Seoul 03722 South Korea Yonsei Univ Dept Stat & Data Sci Seoul 03722 South Korea Univ Fed Rural Pernambuco Dept Estat & Informat Recife PE Brazil Stellenbosch Univ Sch Data Sci & Computat Thinking ZA-7600 Stellenbosch South Africa Natl Inst Theoret & Computat Sci NITheCS ZA-7600 Stellenbosch South Africa Univ KwaZulu Natal Quantum Res Grp ZA-4001 Durban South Africa

The theory of quantum algorithms promises unprecedented benefits of harnessing the laws of quantum mechanics for solving certain computational problems. A prerequisite for applying quantum algorithms to a wide range of real-world problems is loading classical data to a quantum state. Several circuit-based methods have been proposed for encoding classical data as probability amplitudes of a quantum state. However, in these methods, either quantum circuit depth or width must grow linearly with the data size, nullifying the advantage of representing exponentially many classical data in a quantum state. In this paper, we present a configurable bidirectional procedure that addresses this problem by tailoring the resource trade-off between quantum circuit width and depth. In particular, we show a configuration that encodes an N-dimensional classical data using a quantum circuit whose width and depth both grow sublinearly with N. We demonstrate proof-of-principle implementations on five quantum computers accessed through the IBM and IonQ quantum cloud services.

关键词： quantum computing State preparation Bidirectional Circuit optimization

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Fast quantum search driven by environmental engineering

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Communications in Theoretical Physics 2022年第4期74卷 31-41页

作者： Rui Zhang Tian Chen National Engineering Laboratory for Risk Perception and Prevention(NEL-RPP) CAEITBeijing 100041China Beijing Key Laboratory of Nanophotonics and Ultrafine Optoelectronic Systems School of PhysicsBeijing Institute of TechnologyBeijing 100081China

Studies have demonstrated that a joined complete graph is a typical mathematical model that can support a fast quantum search. In this paper, we study the implementation of joined complete graphs in atomic systems and realize a quantum search of runtime ■ based on this implementation with a success probability of 50%. Even though the practical systems inevitably interact with the surrounding environment, we reveal that a successful quantum search can be realized through delicately engineering the environment itself. We consider that our study will bring about a feasible way to realize quantum information processing including quantum algorithms in reality.

关键词： quantum walk quantum search algorithm environmental engineering non-Markovian

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A quantum SEGMENTATION ALGORITHM BASED ON BACKGROUND-DIFFERENCE METHOD FOR NEQR IMAGE

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quantum information & COMPUTATION 2023年第15-16期23卷 1291-1309页

作者： Wang, Lu Liu, Wenjie Deng, Zhiliang Southeast Univ Sch Informat Sci & Engn Nanjing 211189 Peoples R China Nanjing Univ Informat Sci & Technol Sch Software Nanjing 210044 Peoples R China Nanjing Univ Informat Sci & Technol Sch Automat Nanjing 210044 Peoples R China

quantum image segmentation algorithm can use its quantum mechanism to rapidly segment the objects in a quantum image. However, the existing quantum image segmentation algorithms can only segment static objects in the image and use more quantum resource(qubit). In this paper, a novel quantum segmentation algorithm based on background-difference method for NEQR image is proposed, which can segment dynamic objects in a static scene image by using fewer qubits. In addition, an efficient and feasible quantum absolute value subtractor is designed, which is an exponential improvement over the existing quantum absolute value subtractor. Then, a complete quantum circuit is designed to segment the NEQR image. For a 2(n) x 2(n) image with gray-scale range of [0,2(q)-1], the complexity of our algorithm is O(q), which has an exponential improvement over the classical segmentation algorithm, and the complexity will not increase as the image's size increases. The experiment is conducted on IBM Q to show the feasibility of our algorithm in the noisy intermediate-scale quantum (NISQ) era.

关键词： quantum image processing quantum image segmentation Background difference method quantum absolute value subtractor

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quantum key recovery attacks on tweakable Even-Mansour ciphers

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quantum information processing 2023年第9期22卷 1-21页

作者： Zhang, Ping Luo, Yiyuan Nanjing Univ Posts & Telecommun Sch Comp Sci Nanjing 210023 Peoples R China Huizhou Univ Sch Comp Sci & Engn Huizhou 516007 Peoples R China

As tweakable block ciphers from public permutations, tweakable Even-Mansour ciphers are widely used in disk sector encryption and data storage encryption. With the rapid improvement of computing power, especially the development of quantum computing technology and quantum computers, the quantum security of tweakable Even-Mansour ciphers should be concerned and studied. This paper focuses on the security of tweakable Even-Mansour ciphers in the quantum setting. For one -round tweakable Even-Mansour cipher, we give its quantum circuit, present a quantum key recovery attack in polynomial time by Simon's algorithm and show the concrete resource estimation. For two -round tweakable Even-Mansour cipher, we present a better quantum key recovery attack by BHT -meets -Simon algorithm than that by Grover -meets -Simon algorithm from a new perspective of variable tweaks and show the concrete resource estimation. Finally, we generalize to r -round tweakable Even-Mansour cipher and present a quantum key recovery attack by combining Grover's algorithm and Simon's algorithm. Our work is of great importance. We use BHT -meets -Simon algorithm to achieve better quantum key recovery attacks than Grover -meets -Simon algorithm for the first time.

关键词： quantum computing Tweakable Even-Mansour ciphers quantum attacks quantum algorithms Resource estimation

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quantum RANGING ENHANCED TDOA LOCALIZATION 49

QUANTUM RANGING ENHANCED TDOA LOCALIZATION

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49th IEEE International Conference on Acoustics, Speech, and Signal processing (ICASSP)

作者： He, Entong Yang, Yuxiang Wu, Chenshu Univ Hong Kong Dept Comp Sci Hong Kong Peoples R China

ISBN: (纸本)9798350344868;9798350344851

Localization is critical to numerous applications. The performance of classical localization protocols is limited by the specific form of distance information and suffer from considerable ranging errors. This paper foresees a new opportunity by utilizing the exceptional property of entangled quantum states to measure a linear combination of target-anchor distances. Specifically, we consider localization with quantum-based TDoA measurements. Classical TDoA ranging takes the difference of two separate measurements. Instead, quantum ranging allows TDoA estimation within a single measurement, thereby reducing the ranging errors. Numerical simulations demonstrate that the new quantum-based localization significantly outperforms conventional algorithms based on classical ranging, with over 50% gains on average.

关键词： quantum ranging localization TDoA

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quantum algorithm for large-scale market equilibrium computation 38

Quantum algorithm for large-scale market equilibrium computa...

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38th Conference on Neural information processing Systems, NeurIPS 2024

作者： Huang, Po-Wei Rebentrost, Patrick Centre for Quantum Technologies National University of Singapore 117543 Singapore Department of Computer Science National University of Singapore 117417 Singapore

Classical algorithms for market equilibrium computation such as proportional response dynamics face scalability issues with Internet-based applications such as auctions, recommender systems, and fair division, despite having an almost linear runtime in terms of the product of buyers and goods. In this work, we provide the first quantum algorithm for market equilibrium computation with sub-linear performance. Our algorithm provides a polynomial runtime speedup in terms of the product of the number of buyers and goods while reaching the same optimization objective value as the classical algorithm. Numerical simulations of a system with 16384 buyers and goods support our theoretical results that our quantum algorithm provides a significant speedup. © 2024 Neural information processing systems foundation. All rights reserved.

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Weight adjustment of the quantum layer in a hybrid model for skin cancer image classification 18

Weight adjustment of the quantum layer in a hybrid model for...

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Conference on Optics and Photonics for information processing XVIII

作者： Alejandro Lopez, Daniel Montiel, Oscar Lopez-Montiela, Miguel Castillo, Oscar Inst Politecn Nacl CITEDI Ave Inst Politecn Nacl 1310 Tijuana 22435 BC Mexico Inst Tecnol Tijuana Tijuana 22414 BC Mexico

ISBN: (纸本)9781510679320;9781510679337

Integrating quantum algorithms with machine and deep learning models has emerged as a promising method for addressing medical image classification challenges. This integration can enhance speed and efficiency when performing complex computations. However, hybrid quantum models, particularly on quantum Convolutional Neural Networks (QCNNs) face two significant drawbacks: the placement of the quantum convolutional layer before the model architecture and the lack of integration of the quantum layer within the training process. These disadvantages reduce the robustness and reproducibility of the models. This study proposes that integrates the quantum layer into the quantum layer to address these shortcomings. We present a comparative analysis between a hybrid quantum deep learning model, which includes a trainable quantum layer, and its classical counterpart for the classification of skin cancer dermatoscopic images. The hybrid model attains 0.7865 of accuracy, a recall of 0.7321, a precision of 0.7268, and an F1 Score of 0.7288, while the classical model reaches an accuracy, recall, precision, and F1 Score of 0.8510, 0.8472, 0.8495, and 0.8447. The hybrid model achieves comparable results to its classical counterpart and demonstrates the advantages of weight adjustment in quantum layers and their potential in improving medical imaging analysis.

关键词： Skin Cancer Deep Learning quantum Computing Hybrid quantum Model Medical Image Classification

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Enhancing quantum Machine Learning algorithms for Optimized Financial Portfolio Management 3

Enhancing Quantum Machine Learning Algorithms for Optimized ...

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3rd IEEE International Conference on Intelligent Techniques in Control, Optimization and Signal processing, INCOS 2024

作者： Bhasin, Narinder Kumar Kadyan, Sunil Santosh, Kathari Hp, Ramya Changala, Ravindra Bala, B Kiran Amity University Zonal Head Indian Institute of Banking and Finance Delhi India School of Management and Commerce Manav Rachna University Haryana Faridabad India Cmr Institute of Technology Department of Mba Bengaluru India Bangalore India Guru Nanak Institutions Technical Campus Department of Information Technology Hyderabad India K.Ramakrishnan College of Engineering Department of Ai & Ds Tamil Nadu Trichy India

ISBN: (纸本)9798350361186

Advancements in quantum machine learning offer unprecedented potential to revolutionize financial portfolio optimization, maximizing returns while managing risks efficiently. This study focuses on advancing quantum machine learning algorithms for optimal financial portfolio management, presenting a novel approach implemented in Python that outperforms existing methods. The algorithm's capability to generate such substantial returns over time positions it as a groundbreaking tool for portfolio optimization in the dynamic landscape of financial markets. In the pursuit of enhancing quantum machine learning algorithms, this research focuses on the development and optimization of the QSVM algorithm. Leveraging Python for implementation, the study considers critical factors such as quantum circuit optimization, noise mitigation, and the integration of classical and quantum components to achieve superior results. The achieved portfolio performance over time not only underscores the algorithm's efficacy but also signifies a quantum advantage in financial decision-making. The implementation in Python ensures accessibility and applicability, facilitating the integration of this advanced quantum algorithm into existing financial frameworks. This research contributes to the evolving field of quantum finance, showcasing the potential of quantum machine learning in optimizing financial portfolios. The findings not only validate the superior performance of the proposed QSVM but also highlight the broader implications for the future of financial decision support systems, where quantum algorithms could play a transformative role in enhancing portfolio management strategies. The proposed quantum Support Vector Machine (QSVM) demonstrates unparalleled success, with a remarkable Portfolio performance over time of 89.65%. This result significantly surpasses existing quantum algorithms, including quantum Principal Component Analysis (QPCA), quantum Boltzmann Machines (QBM), and quantum K

关键词： Python

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Efficient VQE Approach for Accurate Simulations on the Kagome Lattice 1

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3rd International Conference on Artificial Intelligence and Knowledge processing (AIKP)

作者： Jyothikamalesh, S. Kaarnika, A. Mohankumar, M. Vishwakarma, Sanjay Ganguly, Srinjoy Yuvaraj, P. Sri Eshwar Coll Engn Coimbatore Tamil Nadu India IBM Quantum Mountain View CA USA Fractal Analyt Gurugram India

ISBN: (数字)9783031686177

ISBN: (纸本)9783031686160;9783031686177

The Kagome lattice, a captivating lattice structure composed of interconnected triangles with frustrated magnetic properties, has garnered considerable interest in condensed matter physics, quantum magnetism, and quantum computing. The Ansatz optimization provided in this study along with extensive research on optimization technique results us with high accuracy. This study focuses on using multiple ansatz models to create an effective Variational quantum Eigen-solver (VQE) on the Kagome lattice of Herbertsmithite. By comparing various optimization methods and optimizing the VQE ansatz models, the main goal is to estimate ground state attributes with high accuracy. This study advances quantum computing and advances our knowledge of quantum materials with complex lattice structures by taking advantage of the distinctive geometric configuration and features of the Kagome lattice. Aiming to improve the effectiveness and accuracy of VQE implementations, the study examines how Ansatz Modelling, quantum effects, and optimization techniques interact in VQE algorithm. The findings and understandings from this study provide useful direction for upcoming improvements in quantum algorithms, quantum machine learning and the investigation of quantum materials on the Kagome Lattice.

关键词： quantum machine learning Machine learning quantum computing Kagome lattice quantum magnetism VQE Condensed matter physics

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