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Improved Performance of Multi-Angle Quantum Approximate Optimization Algorithm (ma-QAOA) Compared to QAOA On Simulation and Experimental Hardware Platforms

Improved Performance of Multi-Angle Quantum Approximate Opti...

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International Communication Systems and Networks and Workshops, COMSNETS

作者： Vandit Srivastava Rohith P Sanyam Singhal Debanjan Bhowmik Department of Electrical Engineering Indian Institute of Technology Delhi New Delhi India Dept. of Electrical Engineering Indian Institute of Technology Delhi New Delhi India Department of Physics Indian Institute of Technology Bombay Mumbai Maharashtra India Department of Electrical Engineering Indian Institute of Technology Bombay Mumbai Maharashtra India

ISBN: (数字)9798331531195

ISBN: (纸本)9798331531201

Multi-angle quantum approximate optimization algorithm (ma-QAOA) has been proposed as an alternative to regular QAOA for solving optimization problems using noisy intermediate scale quantum (NISQ) hardware. Here, we first implement QAOA and ma-QAOA algorithms to solve MaxCut on weighted and unweighted versions of the Erdős–Rényi (ER) and random cubic (RC) graphs of 6 and 10 nodes. Quantum circuit parameters are updated iteratively through classical feedback with the quantum circuit for forward pass modelled on Qiskit simulator without noise models. Then, the quantum circuit for the final run with updated parameters is implemented on Qiskit simulator without noise models, Qiskit simulator with experimentally benchmarked noise models (fake back-end), and also on actual experimental quantum hardware (IBM’s 127-qubit quantum processor ibm brisbane). We show that for most graph instances, ma-QAOA performs better than QAOA (in terms of approximation ratio (AR)) and needs less stages in the quantum circuit for all three aforementioned methods. Finally, we extend our results up to 14-node RC graphs while restricting ourselves to Qiskit simulation without noise (both for parameter update and final run) and show that for most instances, ma-QAOA again needs much less stages compared to QAOA to obtain reasonable AR.

关键词： Communication systems Noise Benchmark testing approximation algorithms Hardware Noise measurement Quantum circuit Integrated circuit modeling Optimization

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Learning-Aided Kalman Tracking in Biased Dynamic Systems: The Case of Cable-Driven Robots for Surgery

Learning-Aided Kalman Tracking in Biased Dynamic Systems: Th...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Linoy Ketashvili Shachar Ashkenasy Ilana Nisky Nir Shlezinger School of ECE Ben-Gurion University of the Negev Beer-Sheva Israel Department of BME Ben-Gurion University of the Negev Israel

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

In cable-driven robots, the actuation is transmitted via long cables for control and manipulation of the end-effectors. The cables introduce biases due to non-linear tension, creating a challenge for accurate modeling and localization. This paper presents a novel data-driven algorithm that addresses these biases in partially known dynamic systems, focusing on cable-driven robots. The proposed approach integrates classic Kalman filtering with deep learning to enhance tracking and overcome the limitations of existing state-space modeling. Our algorithm learns from data to explicitly track the robot’s end-effector, while implicitly tracking its cable-induced bias. We demonstrate the effectiveness of the algorithm on two different robotic manipulators: a planar two-link robotic manipulator, and the Raven surgical robot, constituting testbeds for dynamic systems where the bias arises from time-varying cable tension. Our experimental results reveal that while conventional methods like the Extended Kalman Filter struggle with pose tracking due to system biases, our algorithm successfully localizes the robot’s end-effector and uncovers underlying biases, showing significant advancements in predictive capabilities and dynamic system understanding.

关键词： Heuristic algorithms Signal processing algorithms Prediction algorithms approximation algorithms End effectors Kalman filters Dynamical systems Robots Manipulator dynamics Time-varying systems

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Synthesis of Single Qutrit Circuits from Clifford+R

arXiv

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arXiv 2025年

作者： Gustafson, Erik J. Lamm, Henry Liu, Diyi Murairi, Edison M. Zhu, Shuchen BataviaIL60510 United States Fermi National Accelerator Laboratory BataviaIL60510 United States NASA Ames Research Center Moffett FieldCA94035 United States Mountain ViewCA94043 United States School of Mathematics University of Minnesota-Twin Cities MinneapolisMN55414 United States Department of Physics The George Washington University WashingtonDC20052 United States Department of Mathematics Duke University DurhamNC27708 United States

We present two deterministic algorithms to approximate single-qutrit gates. These algorithms utilize the Clifford + R group to find the best approximation of diagonal rotations. The first algorithm exhaustively searches over the group;while the second algorithm searches only for Householder reflections. The exhaustive search algorithm yields an average R count of 2.193(11) + 8.621(7) log10(1/Ε), albeit with a time complexity of O(Ε−4.4). The Householder search algorithm results in a larger average R count of 3.20(13) + 10.77(3) log10(1/Ε) at a reduced time complexity of O(Ε−0.42), greatly extending the reach in Ε. These costs correspond asymptotically to 35% and 69% more non-Clifford gates compared to synthesizing the same unitary with two qubits. Such initial results are encouraging for using the R gate as the non-transversal gate for qutrit-based computation. © 2025, CC BY.

关键词： approximation algorithms

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Maximum Coverage k-Antichains and Chains: A Greedy Approach

arXiv

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arXiv 2025年

作者： Cáceres, Manuel Grigorjew, Andreas Jiamjitrak, Wanchote Po Tomescu, Alexandru I. Department of Computer Science Aalto University Finland University of Helsinki Finland

Given an input acyclic digraph G = (V, E) and a positive integer k, the problem of Maximum Coverage k-Antichains (resp., Chains) denoted as MA-k (resp., MC-k) asks to find k sets of pairwise unreachable vertices, known as antichains (resp., k subsequences of paths, known as chains), maximizing the number of vertices covered by these antichains (resp. chains). While MC-k has been recently solved in (almost) optimal O(|E|1+o(1)) time [Kogan and Parter, ICALP 2022], the fastest known algorithm for MA-k is a recent (k|E|)1+o(1)-time solution [Kogan and Parter, ESA 2024] as well as a 1/2 approximation running in |E|1+o(1) time in the same paper. In this paper, we leverage a paths-based proof of the Greene-Kleitman (GK) theorem with the help of the greedy algorithm for set cover and recent advances on fast algorithms for flows and shortest paths to obtain the following results for MA-k: • The first (exact) algorithm running in |E|1+o(1) time, hence independent in k. • A randomized algorithm running in Õ(αk|E|) time1, where αk is the size of the optimal solution. That is, a near-linear parameterized running time, generalizing the result of [Mäkinen et al., ACM TALG] obtained for k = 1. • An approximation algorithm running in time O(α21|V | + (α1 + k)|E|) with approximation ratio of (1 − 1/e) > 0.63 > 1/2. Our last two solutions rely on the use of greedy set cover, first exploited in [Felsner et al., Order 2003] for chains, which we now apply to antichains. We complement these results with two examples (one for chains and one for antichains) showing that, for every k ≥ 2, greedy misses a 1/4 portion of the optimal coverage. We also show that greedy is a Ω(log |V |) factor away from minimality when required to cover all vertices: previously unknown for sets of chains or antichains. Copyright © 2025, The Authors. All rights reserved.

关键词： approximation algorithms

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Semi-Recursive Convolution Schemes for Macromodeling of Time-Invariant Linear Circuits

Semi-Recursive Convolution Schemes for Macromodeling of Time...

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European Conference on Antennas and Propagation, EuCAP

作者： Giuseppe Pettanice Roberto Valentini Piergiuseppe Di Marco Fortunato Santucci Giulio Antonini University of L’ Aquila: DISIM L'Aquila Italy Universityof L'Aquila: DIllE L'Aquila Italy

ISBN: (数字)9788831299107

ISBN: (纸本)9798350366327

Time-domain macromodeling is a powerful technique for generating compact models of linear time-invariant (LTI) circuits. A common approach involves computing the port impulse response at the gates of the LTI system to evaluate convo-lution integrals, though this is often computationally demanding. To address this, a segment fast convolution (SFC) method has been recently introduced, leveraging piecewise constant (PW C) approximations of impulse responses to speed up the computation. This work proposes an extension of this method, which relies on a piecewise linear (PWL) approximation of the port impulse responses of LTI circuits. Additionally, a quasi-recursive algorithm for the convolution calculation is developed, which allows the reduction of the required number of operations and achieves significant speed improvements.

关键词： Linear systems Convolution Voltage Receivers Logic gates Linear circuits approximation algorithms Computational efficiency Time-domain analysis Integrated circuit modeling

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H-VLO: Hybrid LiDAR-Camera Fusion For Self-Supervised Odometry

H-VLO: Hybrid LiDAR-Camera Fusion For Self-Supervised Odomet...

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IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

作者： Aydemir, Eren Fetic, Naida Unel, Mustafa Sabanci Univ Fac Engn & Nat Sci Istanbul Turkey Ford Otosan Prod Dev Istanbul Turkey

ISBN: (数字)9781665479271

ISBN: (纸本)9781665479271

In this paper, we propose a hybrid visual-LiDAR odometry (H-VLO) framework that fuses predicted visual depth map and completed LiDAR map. Compared to the previous visual-LiDAR odometry methods, our approach leverages 2D feature matching and 3D association by utilizing deep depth map, deep flow map and deep LiDAR depth completion networks. Rather than extraction of the depth values from LiDAR measurements for each visual feature, our method first densifies a LiDAR scan with a deep depth completion network and then fuses it with visual deep depth map estimation in a Bayesian framework. This method reduces pose estimation drift by improving feature-to-feature and point-to-feature matching, as well as scale recovery. The evaluations on the public KITTI odometry benchmark show that our technique achieves better or at least comparable estimates than the state-of-the-art visual-LiDAR and monocular visual odometry approaches.

关键词： Visualization Laser radar Three-dimensional displays Fuses approximation algorithms Feature extraction Cameras

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Genetic Algorithm-based QAOA Training improved with Fuzzy Clustering

Genetic Algorithm-based QAOA Training improved with Fuzzy Cl...

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Multidisciplinary Computational Intelligence Incubators (MCII), IEEE Symposium for

作者： Giovanni Acampora Angela Chiatto Autilia Vitiello Department of Physics "Ettore Pancini" University of Naples Federico II Naples Italy

ISBN: (数字)9798331508395

ISBN: (纸本)9798331508401

The Quantum Approximate Optimization Algorithm (QAOA) is emerging as a key method in quantum optimization. Operating within the framework of variational quantum circuits, QAOA entails training a parameterized quantum circuit through classical optimizers to effectively address combinatorial optimization problems. In this context, evolutionary algorithms such as genetic algorithms are emerging as suitable methodologies to search the optimal parameters for QAOA circuits. However, unfortunately, the training process results in a computationally intensive task, and, as a consequence, it is crucial to explore innovative strategies to address this issue. This work introduces a novel approach based on Fuzzy C-means algorithm to opportunely initialize the population of the genetic algorithms employed as optimizers for QAOA and, as a consequence, enhance the training. Experimental results demonstrate that the proposed approach significantly improves the performance of genetic algorithms-based training for QAOA.

关键词： Training Noise Clustering algorithms Evolutionary computation approximation algorithms Hardware Proposals Quantum circuit Optimization Genetic algorithms

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A UCA-Based Orbital Angular Momentum Solution for Integrated Sensing and Communication Systems

A UCA-Based Orbital Angular Momentum Solution for Integrated...

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IEEE Conference on Wireless Communications and Networking

作者： Yihong Zheng Shumin Wang Xinghe Chu Wei Zheng Zhiqun Hu Zhaoming Lu Beijing Key Laboratory of Network System Architecture and Convergence Beijing China Beijing University of Posts and Telecommunications Beijing China Beijing Laboratory of Advanced Information Networks Beijing China

ISBN: (数字)9798350368369

ISBN: (纸本)9798350368376

In the sixth generation (6G) Internet of Things (IoT), integrated sensing and communication (ISAC) emerges as a key technology, which is expected to significantly enhance the perception capabilities and spectrum efficiency of base stations (BSs). It holds potential for applications in unmanned aerial vehicle (UAV) monitoring, vehicle positioning, and crowd detection. However, developing an integrated waveform that efficiently conserves spectrum resources while maintaining lower complexity remains challenging. This paper designs an ISAC system that utilizes orbital angular momentum (OAM) waves generated by a uniform circular array (UCA), which enhances communication and sensing capabilities by allocating distinct modes. This paper proposes a multimode multiplexing communication scheme based on a single UCA and an OAM-circular reception method (OAM-CRM) for the two-dimensional direction-of-arrival (DOA) estimation of targets, encompassing both azimuth and elevation angles. Additionally, an OAM-different modes (OAMDM) algorithm is designed to optimize communication and sensing performance across various mode sets. Simulation results verify the effectiveness of the OAM ISAC system and demonstrate the superior performance of the proposed algorithms compared to conventional methods.

关键词： Multiplexing Orbital calculations Direction-of-arrival estimation Accuracy Simulation Estimation Integrated sensing and communication approximation algorithms Autonomous aerial vehicles Internet of Things

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Reconciling AMP algorithms derived from Belief Propagation or the Large System Limit Bethe Free Energy

Reconciling AMP Algorithms derived from Belief Propagation o...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Zilu Zhao Fangqing Xiao Christo K. Thomas Dirk Slock Communication Systems Department EURECOM France NEWS Lab Virginia Tech VA USA

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

When derived from the Bethe Free Energy (BFE) of the Generalized Linear Model (GLM), Approximate Message Passing (AMP) algorithms combine two asymptotic Large System Limit (LSL) simplifications which are asymptotic Gaussianity of extrinsics and large random matrix theory based asymptotic variance computations. In the provably convergent AMBGAMP algorithm, a LSL version of the BFE is derived. In Expectation Propagation (EP) style minimization, the LSL BFE cost function is augmented with Lagrangian terms for mean and variance consistency constraints, augmented with a quadratic version of the mean constraints as in the Method of Multipliers (MM). The mean Lagrange multipliers then get updated ADMM-style (Alternating Direction of MM). In this approach, the weights of the MM terms need to be carefully chosen, which is not part of the MM philosophy, and the Lagrange multipliers have no particular meaning. On the other hand, AMP can be derived by directly introducing LSL simplifications in the Belief Propagtion (BP) algorithm that minimizes the original GLM BFE. This allows to relate extrinsic messages to posterior pdfs by first-order Taylor series expansion based perturbations. We also apply LSL approximations to the variances of the various Gaussians involved, which in fact leads to a rederivation of a fundamental LSL theorem describing the deterministic limit of posterior variances. We show that this LSL version of BP leads to BFE modifications that correspond to the augmented Lagrangian of the LSL BFE, explaining its weights and Lagrange Multipliers. These insights should facilitate the extension of AMP to more complex settings such as bilinear models.

关键词： Philosophical considerations Perturbation methods Message passing Signal processing algorithms Signal processing Probability density function approximation algorithms Taylor series Minimization Speech processing

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An Ensemble Learning Method Based on Neighborhood Granularity Discrimination Index and Its Application in Software Defect Prediction

An Ensemble Learning Method Based on Neighborhood Granularit...

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IEEE International Conference on Software Analysis, Evolution and Reengineering (SANER)

作者： Yuqi Sha Feng Jiang Qiang Hu Yifan He College of Information Science and Technology Qingdao University of Science and Technology Qingdao P. R. China Institute of Cosmetic Regulatory Science Beijing Technology and Business University Beijing P.R. China

ISBN: (数字)9798331535100

ISBN: (纸本)9798331535117

Software defect prediction (SDP) is a primary field of study in software engineering, aiming to optimize test resource allocation by highlighting the defect-prone software modules. Over the last few years, ensemble learning method has been extensively adopted in SDP. However, how to strengthen the diversity of base learners is an issue in ensemble learning. In this paper, we consider the problem of diversity in the eye of feature space perturbation. First, we propose the notion of neighborhood granularity discrimination index (NGDI), by combining the neighborhood knowledge granularity with the neighborhood discrimination index within the framework of neighborhood rough sets. NGDI can not only measure the uncertainty of feature subsets' discriminant capability, but also characterize the granularity of neighborhood knowledge induced by feature subsets. Second, we propose an ensemble learning algorithm, EL-NGDI, established on the NGDI. ELNGDI disturbs the feature space using multiple NGDI-based neighborhood approximate reducts. Third, we use ELNGDI to predict software defects. ELNGDI and the Synthetic Minority Oversampling Technique (SMOTE) are combined in order to handle the class imbalance issue in SDP, and propose a mechanism called SMOTE-ELNGDI. Experimental results on 20 datasets demonstrate that ELNGDI effectively improves the performance of SDP compared with existing ensemble learning methods.

关键词： Uncertainty Software algorithms Diversity reception Rough sets Prediction algorithms approximation algorithms Software Ensemble learning Indexes Software engineering

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