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49th International Symposium on Mathematical Foundations of computer science, MFCS 2024

作者： Agarwal, Avantika Gharibian, Sevag Koppula, Venkata Rudolph, Dorian David R. Cheriton School of Computer Science Institute for Quantum Computing University of Waterloo Canada Paderborn University Germany Department of Computer Science and Engineering Indian Institute of Technology Delhi India

ISBN: (纸本)9783959773355

The Polynomial-Time Hierarchy (PH) is a staple of classical complexity theory, with applications spanning randomized computation to circuit lower bounds to "quantum advantage" analyses for near-term quantum computers. quantumly, however, despite the fact that at least four definitions of quantum PH exist, it has been challenging to prove analogues for these of even basic facts from PH. This work studies three quantum-verifier based generalizations of PH, two of which are from [Gharibian, Santha, Sikora, Sundaram, Yirka, 2022] and use classical strings (QCPH) and quantum mixed states (QPH) as proofs, and one of which is new to this work, utilizing quantum pure states (pureQPH) as proofs. We first resolve several open problems from [GSSSY22], including a collapse theorem and a Karp-Lipton theorem for QCPH. Then, for our new class pureQPH, we show one-sided error reduction pureQPH, as well as the first bounds relating these quantum variants of PH, namely QCPH ⊆ pureQPH ⊆ EXPPP © Avantika Agarwal, Sevag Gharibian, Venkata Koppula, and Dorian Rudolph.

关键词： quantum computers

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Efficient CNN-based disaster events classification using UAV-aided images for emergency response application

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Neural Computing and Applications 2024年第18期36卷 10599-10612页

作者： Bashir, Munzir Hubiba Ahmad, Musheer Rizvi, Danish Raza El-Latif, Ahmed A. Abd Department of Computer Engineering Jamia Millia Islamia New Delhi110025 India EIAS Data Science Lab College of Computer and Information Sciences Prince Sultan University Riyadh11586 Saudi Arabia Center of Excellence in Quantum and Intelligent Computing Prince Sultan University Riyadh11586 Saudi Arabia Department of Mathematics and Computer Science Faculty of Science Menoufia University Shebin El-Kom32511 Egypt

Natural disasters can be unpredictable and catastrophic. Even after the event, the repercussions are prolonged due to the incompetence of disaster management strategies. To mitigate the effects of a natural hazard, disaster management teams have to rapidly come forth with innovative plans of action. To contain the damage that a disaster causes, the response time, and preparedness of a disaster management team is crucial. Having a forewarning about the nature of the disaster can prove to be beneficial for the management team. Remote/unreachable areas such as deep parts of the forests, far-flung rural areas, oceans and other hard to reach locations are at a higher risk of receiving poorer aid and response due to the lack of communication and connectivity with the rest of the world. Our paper provides a solution for this particular problem by suggesting UAV/Drones for surveillance and monitoring in these inaccessible, disaster struck areas. The drones not only monitor the situation but constantly click pictures and send them to a base station, where the images are used to collect insights about the type of disaster that has to be dealt with. The paper proposes a deep learning model based on feature concatenation for classification of disasters which can be deployed at the base station, and can receive data in the form of images from a UAV/drone hovering over the affected place. The proposed model is efficient and able to achieve a higher accuracy as compared to the leading CNN models and closely related recent works as well. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Human resource management

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Block chain Technology & Its Scope in Various Fields: A Comprehensive Review

Block chain Technology & Its Scope in Various Fields: A Comp...

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2024 International Conference on Sustainable Computing and Integrated Communication in Changing Landscape of AI, ICSCAI 2024

作者： Joshi, Mahak Yadav, Vijay K. Goyal, Udita Negi, Ganga Mehra, Ritika Department of Computer Science Engineering SoEC Dev Bhoomi Uttarakhand University Dehradun India Lloyd Institute of Engineering & Technology Greater Noida India Quantum University India

ISBN: (纸本)9798350355338

In recent years, block chain technology has become a powerful force for change, upending established markets and altering how data is shared and kept. This review discusses the underlying characteristics and features of block chain technology and looks at some of the potential fields in which it might be used. We highlight the versatility of block chain by analysing its decentralized structure, immutability, transparency, and security features, as well as its applications in finance, supply chains, healthcare, voting systems, and other fields. The assessment comes to a close with some reflections on the block chain technology's bright future and the issues that still need to be solved before it can be widely used. © 2024 IEEE.

关键词： Decentralized finance

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Comparative Analysis on Automated Detection of Credit Card Fraud 3

Comparative Analysis on Automated Detection of Credit Card F...

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3rd International Conference on Technological Advancements in Computational sciences, ICTACS 2023

作者： Bhatt, Chandradeep Singh, Abhyuday Pratap Chauhan, Rahul Singh, Teekam Baloni, Dev Graphic Era Hill University Cse Department Dehradun India Computer Science and Engineering Graphic Era Hill University Dehradun India Computer Science and Engineering Graphic Era Deemed to Be University Dehradun India Computer Science and Engineering Quantum University Dehradun India

ISBN: (纸本)9798350342338

Detection of the fraud of Credit Card is one of the maximum popular troubles in cutting-edge worldwide. that is due to the upward push of buying and selling and e-exchange structures. credit score card fraud normally takes location at the same time as a card is stolen for fraudulent capabilities or on the equal time as a fraudster makes use of credit score card information for their very own purposes. In worldwide, we are going through many credit score card issues. A credit score card fraud detection device is installed area to come upon fraudulent sports activities. This venture is broadly talking supposed to interest on machine reading algos. The algos used are RF set of on-line and Ada-boost set of online. The consequences of the 2 algos are primarily based on the accuracy, the precision, the maintain in mind, and the F1 rating. The ROC is drawn primarily based completely on the CM. We evaluate the Random wooded place set of online tips and the Ada-boost set of regulations and recall the set of on lines with awesome accuracy, precision, consider, and the F1 rating because the extraordinary set of online tips used for fraud detection. © 2023 IEEE.

关键词： Ada-boost Credit Card Fraud (CCF) Random Forest (RF) ROC curve

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Towards an Analytical quantum Full-Wave Solution of a Transmon Qubit in a 3D Microwave Cavity

Towards an Analytical Quantum Full-Wave Solution of a Transm...

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2024 United States National Committee of URSI National Radio science Meeting, USNC-URSI NRSM 2024

作者： Moon, Soomin Roth, Thomas E. Elmore Family School of Electrical and Computer Engineering Purdue University IN47906 United States Purdue Quantum Science and Engineering Institute Purdue University IN47907 United States

ISBN: (纸本)9781946815194

Circuit quantum electrodynamics (cQED) is one of the most promising platforms for building quantum information processors. As quantum technology evolves the hardware is becoming increasingly complex, so there is a need for a general-purpose, high-fidelity numerical modeling method for an accurate analysis. However, there is a lack of analytical solutions that can serve as a validation method to support the development of efficient and accurate numerical models in this new field. In this study, we develop an analytical solution using microwave cavity perturbation and antenna theories within a field-based description of cQED devices (T. E. Roth and W. C. Chew, "Macroscopic circuit quantum electrodynamics: A new look toward developing full-wave numerical models," IEEE Journal on Multiscale and Multiphysics Computational Techniques, Vol. 6, 109-124, 2021). The system analyzed corresponds to a transmon inside a rectangular waveguide cavity, where the transmon is formed by connecting a Josephson junction across the terminals of a small dipole antenna. We validate our analytical solution against our same formalism evaluated with numerical eigenmodes and the energy participation ratio quantization approach (Z. K. Minev, Z. Leghtas, S. O. Mundhada, L. Christakis, I. M. Pop, and M. H. Devoret, "Energy-participation quantization of Josephson circuits," npj quantum Information, Vol. 7, No. 1, 1-11, 2021). © 2024 IEEE. All rights reserved.

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On-chip meta-lens based Adaptive Integrated Photonic Imager

On-chip meta-lens based Adaptive Integrated Photonic Imager

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2024 Conference on Lasers and Electro-Optics, CLEO 2024

作者： Kim, Jong Ryul Jeong, Heijun Gu, Tingyi Quantum Science and Engineering Program University of Delaware NewarkDE19711 United States Electrical and Computer Engineering University of Delaware NewarkDE19711 United States

ISBN: (纸本)9781957171395

关键词： Adaptive optics

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Dzyaloshinskii-Moriya-type spin-spin interaction from mixed-parity superconductivity

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Physical Review B 2025年第6期111卷 L060504-L060504页

作者： Jabir Ali Ouassou Takehito Yokoyama Jacob Linder Department of Computer Science Electrical Engineering and Mathematical Sciences Western Norway University of Applied Sciences NO-5528 Haugesund Norway Department of Physics Tokyo Institute of Technology Meguro Tokyo 152-8551 Japan Center for Quantum Spintronics Department of Physics Norwegian University of Science and Technology NO-7491 Trondheim Norway

Interacting impurity spins adsorbed on surfaces have been suggested as basic components for applications in quantum computation and spintronics. Such spins usually prefer a parallel or antiparallel configuration, but weakly noncollinear alignments are possible due to the Dzyaloshinskii-Moriya interaction (DMI) that arises in the presence of relativistic spin-orbit coupling. Here, we show that an effective Dzyaloshinskii-Moriya-type interaction (DMTI) can emerge purely from superconducting correlations without any spin-orbit interaction. We give an analytical proof and provide a numerical study which shows that DMTI arises in mixed-parity superconductors solely from the superconducting pairing. Moreover, we show that the same effect can be realized in Josephson junctions between s-wave and p-wave superconductors, where a phase bias toggles the DMTI entirely on and off. These results enable a way to engineer spin textures using superconducting order.

关键词： Shear waves

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Scalable quantum Photonic Neural Network Processor via Cavity-Assisted Interactions

Scalable Quantum Photonic Neural Network Processor via Cavit...

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CLEO: Fundamental science, CLEO:FS 2024 - Part of Conference on Lasers and Electro-Optics, CLEO 2024

作者： Basani, Jasvith Raj Niu, Murphy Yuezhen Waks, Edo Department of Electrical and Computer Engineering Institute for Research in Electronics and Applied Physics Joint Quantum Institute University of Maryland College ParkMD20742 United States Department of Computer Science Joint Center for Quantum Information and Computer Science University of Maryland College ParkMD20742 United States

We present an integrated photonic architecture that uses a single atom trapped in a cavity for deterministic high-fidelity quantum operations. Our design is unique in providing a photon-number-selective nonlinearity, ... 详细信息

ISBN: (纸本)9781957171395

关键词： Photonic integrated circuits

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Deep Q-learning with hybrid quantum neural network on solving maze problems

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quantum Machine Intelligence 2024年第1期6卷 1-11页

作者： Chen, Hao-Yuan Chang, Yen-Jui Liao, Shih-Wei Chang, Ching-Ray Department of Computer Science University of London LondonWC1E 7HU United Kingdom Department of Physics National Taiwan University Taipei106216 Taiwan Department of Computer Science and Information Engineering National Taiwan University Taipei106216 Taiwan Quantum Information Center Chung Yuan Christian University Taoyuan320314 Taiwan

quantum computing holds great potential for advancing the limitations of machine learning algorithms to handle higher dimensions of data and reduce overall training parameters in deep learning (DL) models. This study uses a trainable variational quantum circuit (VQC) on a gate-based quantum computing model to investigate the potential for quantum benefit in a model-free reinforcement learning problem. Through a comprehensive investigation and evaluation of the current model and capabilities of quantum computers, we designed and trained a novel hybrid quantum neural network based on the latest Qiskit and PyTorch framework. We compared its performance with a full-classical CNN with and without an incorporated VQC. Our research provides insights into the potential of deep quantum learning to solve a maze problem and, potentially, other reinforcement learning problems. We conclude that reinforcement learning problems can be practical with reasonable training epochs. Moreover, a comparative study of full-classical and hybrid quantum neural networks is discussed to understand these two approaches’ performance, advantages, and disadvantages to deep Q-learning problems, especially on larger-scale maze problems larger than 4 × 4. © 2024, The Author(s).

关键词： Reinforcement learning

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Principal eigenstate classical shadows 37

Principal eigenstate classical shadows

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37th Annual Conference on Learning Theory, COLT 2024

作者： Grier, Daniel Pashayan, Hakop Schaeffer, Luke Department of Mathematics Department of Computer Science and Engineering University of California San Diego United States Dahlem Center for Complex Quantum Systems Freie Universität Berlin Germany Joint Center for Quantum Information and Computer Science University of Maryland College Park United States

Given many copies of an unknown quantum state ρ, we consider the task of learning a classical description of its principal eigenstate. Namely, assuming that ρ has an eigenstate |〉 with (unknown) eigenvalue λ > 1/2, the goal is to learn a (classical shadows style) classical description of |〉 which can later be used to estimate expectation values 〈|O|〉 for any O in some class of observables. We consider the sample-complexity setting in which generating a copy of ρ is expensive, but joint measurements on many copies of the state are possible. We present a protocol for this task scaling with the principal eigenvalue λ and show that it is optimal within a space of natural approaches, e.g., applying quantum state purification followed by a single-copy classical shadows scheme. Furthermore, when λ is sufficiently close to 1, the performance of our algorithm is optimal-matching the sample complexity for pure state classical shadows. © 2024 D. Grier, H. Pashayan & L. Schaeffer.

关键词： quantum theory

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