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Relative Entropy of Coherence Quantifies Performance in Bayesian Metrology

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PRX Quantum 2024年第3期5卷 030303-030303页

作者： Ruvi Lecamwasam Syed Assad Joseph J. Hope Ping Koy Lam Jayne Thompson Mile Gu A*STAR Quantum Innovation Centre (Q.Inc) Institute of Materials Research and Engineering (IMRE) Agency for Science Technology and Research (A*STAR) 2 Fusionopolis Way 08-03 Innovis Singapore 138634 Singapore Quantum Machines Unit Okinawa Institute of Science and Technology Graduate University Onna Okinawa 904-0495 Japan Department of Quantum Science and Technology Centre for Quantum Computation and Communication Technology Australian National University Australian Capital Territory 2601 Australia Department of Quantum Science and Technology Australian National University Australian Capital Territory 2601 Australia Centre for Quantum Technologies National University of Singapore 3 Science Drive 2 Singapore 117543 Singapore Horizon Quantum Computing 05-22 Alice@Mediapolis 29 Media Circle Singapore 138565 Singapore Institute of High Performance Computing Agency for Science Technology and Research (A*STAR) Singapore 138634 Singapore Nanyang Quantum Hub School of Physical and Mathematical Sciences Nanyang Technological University 21 Nanyang Link Singapore 639673 Singapore MajuLab CNRS-UNS-NUS-NTU International Joint Research Unit UMI 3654 Singapore 117543 Singapore

The ability of quantum states to be in superposition is one of the key features that sets them apart from the classical world. This “coherence” is rigorously quantified by resource theories, which aim to understand how such properties may be exploited in quantum technologies. There has been much research on what the resource theory of coherence can reveal about quantum metrology, almost all of which has been from the viewpoint of Fisher information. We prove, however, that the relative entropy of coherence, and its recent generalization to positive operator-valued measures (POVMs), naturally quantify the performance of Bayesian metrology. In particular, we show how a coherence measure can be applied to an ensemble of states. We then prove that during parameter estimation, the ensemble relative entropy of coherence (C) is equal to the difference between the optimal Holevo information (X), and the mutual information attained by a measurement (I). We call this relation the CXI equality. The ensemble coherence lets us visualize how much information is locked away in superposition and hence is inaccessible with a given measurement scheme and quantifies the advantage that would be gained by using a joint measurement on multiple states. Our results hold regardless of how the parameter is encoded in the state, encompassing unitary, dissipative, and discrete settings. We consider both projective measurements and general POVMs. This work suggests new directions for research in coherence, provides a novel operation interpretation for the relative entropy of coherence and its POVM generalization, and introduces a new tool to study the role of quantum features in metrology.

关键词： Quantum information theory Quantum metrology Quantum parameter estimation

Projective Quantum Phase Difference Estimation Algorithm for the Direct Computation of Eigenenergy Gaps on a Quantum Computer

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arXiv 2023年

作者： Sugisaki, Kenji Graduate School of Science and Technology Keio University 7-1 Shinkawasaki Saiwai-ku Kanagawa Kawasaki212-0032 Japan Quantum Computing Center Keio University 3-14-1 Hiyoshi Kohoku-ku Kanagawa Yokohama223-8522 Japan Centre for Quantum Engineering Research and Education TCG Centres for Research and Education in Science and Technology Sector V Salt Lake Kolkata700091 India

Quantum computers are capable of calculating the energy gap of two electronic states by using the quantum phase difference estimation (QPDE) algorithm. The Bayesian inference based implementations for the QPDE have been reported so far, but this approach is not projective, and the quality of the calculated energy gap depends on the input wave functions being used. Here, we report the inverse quantum Fourier transformation based QPDE with Na of ancillary qubits, which allows us to compute the difference of eigenenergies based on the single-shot projective measurement. As a proof-of-concept demonstrations, we report numerical experiments for the singlet-triplet energy gap of hydrogen molecule and the vertical excitation energies of halogen-substituted methylenes (CHF, CHCl, CF2, CFCl and CCl2) and formaldehyde (HCHO). © 2023, CC BY.

关键词： Energy gap

engineering of anomalous Josephson effect in coherently coupled Josephson junctions

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arXiv 2023年

作者： Matsuo, Sadashige Imoto, Takaya Yokoyama, Tomohiro Sato, Yosuke Lindemann, Tyler Gronin, Sergei Gardner, Geoffrey C. Manfra, Michael J. Tarucha, Seigo Center for Emergent Matter Science RIKEN Saitama351-0198 Japan Department of Physics Tokyo University of Science Tokyo162-8601 Japan Department of Material Engineering Science Graduate School of Engineering Science Osaka University Osaka560-8531 Japan Birck Nanotechnology Center Purdue University West LafayetteIN47907 United States Department of Physics and Astronomy Purdue University West LafayetteIN47907 United States School of Materials Engineering Purdue University West LafayetteIN47907 United States Elmore Family School of Electrical and Computer Engineering Purdue University West LafayetteIN47907 United States RIKEN Center for Quantum Computing RIKEN Saitama351-0198 Japan

A Josephson junction (JJ) is a key device in the development of superconducting circuits, wherein a supercurrent in the JJ is controlled by the phase difference between the two superconducting electrodes. Recently, it has been shown that the JJ current is nonlocally controlled by the phase difference of another nearby JJ via coherent coupling. Here, we use the nonlocal control to engineer the anomalous Josephson effect. We observe that a supercurrent is produced by the nonlocal phase control even without any local phase difference, using a quantum interference device. The nonlocal phase control simultaneously generates an offset of a local phase difference giving the JJ ground state. These results provide novel concepts for engineering superconducting devices such as phase batteries and dissipationless rectifiers. © 2023, CC BY.

关键词： Ground state

Machine Learning Unveils the power law of Finite-Volume Energy Shifts

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arXiv 2025年

作者： Zhang, Wei-Jie Zhang, Zhenyu Hu, Jifeng Lu, Bing-Nan Pang, Jin-Yi Wang, Qian Guangdong-Hong Kong Joint Laboratory of Quantum Matter Guangdong Provincial Key Laboratory of Nuclear Science Southern Nuclear Science Computing Center South China Normal University Guangzhou510006 China Guangdong Basic Research Center of Excellence for Structure and Fundamental Interactions of Matter Institute of Quantum Matter South China Normal University Guangzhou510006 China Graduate School of China Academy of Engineering Physics Beijing100193 China College of Science University of Shanghai for Science and Technology Shanghai200093 China Institute of Modern Physics Chinese Academy of Sciences Guangdong Province Huizhou516000 China Osaka University Ibaraki567-0047 Japan

Finite-volume extrapolation is an important step for extracting physical observables from lattice calculations. However, it is a significant challenge for the system with long-range interactions. We employ symbolic regression to regress finite-volume extrapolation formula for both short-range and long-range interactions. The regressed formula still holds the exponential form with a factor Ln in front of it. The power decreases with the decreasing range of the force. When the range of the force becomes sufficiently small, the power converges to −1, recovering the short-range formula as expected. Our work represents a significant advancement in leveraging machine learning to probe uncharted territories within particle physics. Copyright © 2025, The Authors. All rights reserved.

关键词： Extrapolation

Backdoor Attack with Sparse and Invisible Trigger

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arXiv 2023年

作者： Gao, Yinghua Li, Yiming Gong, Xueluan Li, Zhifeng Xia, Shu-Tao Wang, Qian The Tsinghua Shenzhen International Graduate School Tsinghua University Shenzhen518055 China The State Key Laboratory of Blockchain and Data Security Zhejiang University Hangzhou311200 China College of Computing and Data Science Nanyang Technological University 639798 Singapore The School of Computer Science Wuhan University China Tencent Data Platform Shenzhen518057 China The Research Center of Artificial Intelligence Peng Cheng Laboratory Shenzhen518000 China The School of Cyber Science and Engineering Wuhan University China

Deep neural networks (DNNs) are vulnerable to backdoor attacks, where the adversary manipulates a small portion of training data such that the victim model predicts normally on the benign samples but classifies the triggered samples as the target class. The backdoor attack is an emerging yet threatening training-phase threat, leading to serious risks in DNN-based applications. In this paper, we revisit the trigger patterns of existing backdoor attacks. We reveal that they are either visible or not sparse and therefore are not stealthy enough. More importantly, it is not feasible to simply combine existing methods to design an effective sparse and invisible backdoor attack. To address this problem, we formulate the trigger generation as a bi-level optimization problem with sparsity and invisibility constraints and propose an effective method to solve it. The proposed method is dubbed sparse and invisible backdoor attack (SIBA). We conduct extensive experiments on benchmark datasets under different settings, which verify the effectiveness of our attack and its resistance to existing backdoor defenses. The codes for reproducing main experiments are available at https://***/YinghuaGao/SIBA. © 2023, CC BY.

关键词： Deep neural networks

Opportunities and Challenges from Using Animal Videos in Reinforcement Learning for Navigation

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arXiv 2022年

作者： Giammarino, Vittorio Queeney, James Carstensen, Lucas C. Hasselmo, Michael E. Paschalidis, Ioannis Ch. Division of Systems Engineering Boston University BostonMA02215 United States Center for Systems Neuroscience Department of Psychological and Brain Sciences Graduate Program for Neuroscience Boston University BostonMA02215 United States College of Engineering Hariri Institute for Computing & Computational Science & Engineering Boston University BostonMA02215 United States

We investigate the use of animal videos (observations) to improve Reinforcement Learning (RL) efficiency and performance in navigation tasks with sparse rewards. Motivated by theoretical considerations, we make use of weighted policy optimization for off-policy RL and describe the main challenges when learning from animal videos. We propose solutions and test our ideas on a series of 2D navigation tasks. We show how our methods can leverage animal videos to improve performance over RL algorithms that do not leverage such observations. © 2022, CC BY.

关键词： Reinforcement learning

LHQ-SVC: Lightweight and High Quality Singing Voice Conversion Modeling

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arXiv 2024年

作者： Huang, Yubo Lai, Xin Ye, Muyang Zhu, Anran Wang, Zixi Xu, Jingzehua Zhang, Shuai Zhou, Zhiyuan Niu, Weijie School of Civil Engineering Southwest Jiaotong University China SWJTU-Leeds Joint School Southwest Jiaotong University China School of Computing and Artificial Intelligence Southwest Jiaotong University China Shenzhen International Graduate School Tsinghua University China Department of Data Science New Jersey Institute of Technology United States School of Economics and Management Southwest Jiaotong University China Technology Co. Ltd China

Singing Voice Conversion (SVC) has emerged as a significant subfield of Voice Conversion (VC), enabling the transformation of one singer’s voice into another while preserving musical elements such as melody, rhythm, and timbre. Traditional SVC methods have limitations in terms of audio quality, data requirements, and computational complexity. In this paper, we propose LHQ-SVC, a lightweight, CPU-compatible model based on the SVC framework and diffusion model, designed to reduce model size and computational demand without sacrificing performance. We incorporate features to improve inference quality, and optimize for CPU execution by using performance tuning tools and parallel computing frameworks. Our experiments demonstrate that LHQ-SVC maintains competitive performance, with significant improvements in processing speed and efficiency across different devices. The results suggest that LHQ-SVC can meet real-time performance requirements, even in resource-constrained environments. Copyright © 2024, The Authors. All rights reserved.

关键词： Static Var compensators

Characterization of silver layers deposited by aluminum oxide, silicon dioxide, and blend for highly reflective optics

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Optical Materials 2024年 149卷

作者： Wu, Hsing-Yu Chen, Hong-Wei Huang, Shao-Rong Shih, Chih-Hsuan Yu, Guo-Yu Sun, Yung-Shin Hsu, Jin-Cherng Tawan Space Agency Taiwan Department of Optics and Photonics Center for Astronomical Physics and Engineering National Central University Taoyuan City320317 Taiwan Department of Electro-Optical Engineering National Taipei University of Technology Taipei Taiwan Department of Physics Fu Jen Catholic University No.510 Zhongzheng Rd. Xinzhuang Dist. New Taipei City242062 Taiwan Department of Engineering and Technology School of Computing and Engineering University of Huddersfield Queensgate HuddersfieldHD1 3DH United Kingdom Graduate Institute of Applied Science and Engineering Fu Jen Catholic University New Taipei City242062 Taiwan Department of Electronic Engineering Ming Chi University of Technology No. 84 Gungjuan Rd. Taishan Dist New Taipei City24301 Taiwan

This study explores the application of metallic silver coatings in optical telescopes. The silver film demonstrates high reflectance across the visible to the infrared spectral range and exhibits excellent adhesion properties. X-ray photoelectron spectroscopy (XPS) analyzes the bonding properties of the two interfaces above and below the silver layer. The main bonds identified in the silver interfaces were Ag–O, Ag–Si, and Ag–Al2O3. The covalent character Ag–O bond is crucial for layer adhesion, as it causes a shift to the high binding energy of Ag0 in the Ag3d XPS spectrum. Enhanced silver multilayers retaining their reflectance and adhesion are essential for space optics in harsh environments. © 2024 Elsevier B.V.

关键词： X ray photoelectron spectroscopy

Discovering latent class labels for multi-label learning 29

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Discovering latent class labels for multi-label learning

29th International Joint Conference on Artificial Intelligence, IJCAI 2020

作者： Huang, Jun Xu, Linchuan Wang, Jing Feng, Lei Yamanishi, Kenji Graduate School of Information Science and Technology University of Tokyo Japan School of Computer Science and Technology Anhui University of Technology China School of Computing and Mathematical Sciences University of Greenwich United Kingdom School of Computer Science and Engineering Nanyang Technological University Singapore

ISBN: (纸本)9780999241165

Existing multi-label learning (MLL) approaches mainly assume all the labels are observed and construct classification models with a fixed set of target labels (known labels). However, in some real applications, multiple latent labels may exist outside this set and hidden in the data, especially for large-scale data sets. Discovering and exploring the latent labels hidden in the data may not only find interesting knowledge but also help us to build a more robust learning model. In this paper, a novel approach named DLCL (i.e., Discovering Latent Class Labels for MLL) is proposed which can not only discover the latent labels in the training data but also predict new instances with the latent and known labels simultaneously. Extensive experiments show a competitive performance of DLCL against other state-of-the-art MLL approaches. © 2020 Inst. Sci. inf., Univ. Defence in Belgrade. All rights reserved.

关键词： Learning systems