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Suppressing Counter-Rotating Errors for Fast Single-Qubit Gates with Fluxonium

PRX Quantum 2024年第4期5卷 040342-040342页

作者： David A. Rower Leon Ding Helin Zhang Max Hays Junyoung An Patrick M. Harrington Ilan T. Rosen Jeffrey M. Gertler Thomas M. Hazard Department of Physics Massachusetts Institute of Technology Cambridge Massachusetts 02139 USA Research Laboratory of Electronics Massachusetts Institute of Technology Cambridge Massachusetts 02139 USA Present address: Atlantic Quantum Cambridge MA 02139 USA. Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology Cambridge Massachusetts 02139 USA Present address: HRL Laboratories Malibu CA 90265 USA. Lincoln Laboratory Massachusetts Institute of Technology Lexington Massachusetts 02421 USA

Qubit decoherence unavoidably degrades the fidelity of quantum logic gates. Accordingly, realizing gates that are as fast as possible is a guiding principle for qubit control, necessitating protocols for mitigating error channels that become significant as gate time is decreased. One such error channel arises from the counter-rotating component of strong, linearly polarized drives. This error channel is particularly important when gate times approach the qubit Larmor period and represents the dominant source of infidelity for sufficiently fast single-qubit gates with low-frequency qubits such as fluxonium. In this work, we develop and demonstrate two complementary protocols for mitigating this error channel. The first protocol realizes circularly polarized driving in circuit QED through simultaneous charge and flux control. The second protocol—commensurate pulses—leverages the coherent and periodic nature of counter-rotating fields to regularize their contributions to gates, enabling single-qubit gate fidelities reliably exceeding 99.997%. This protocol is platform independent and requires no additional calibration overhead. This work establishes straightforward strategies for mitigating counter-rotating effects from strong drives in circuit QED and other platforms, which we expect to be helpful in the effort to realize high-fidelity control for fault-tolerant quantum computing.

关键词： Qubits

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A 7nm-Based Decodable Self-Resetting Regfile Circuit

A 7nm-Based Decodable Self-Resetting Regfile Circuit

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International Conference on ASIC

作者： Wanlong Zhao Yuejun Zhang Mingze Ren Liang Wen Pengjun Wang Faculty of Electrical Engineering and Computer Science Ningbo University Ningbo China Zhejiang Key Laboratory of Mobile Network Application Technology Ningbo University Ningbo China Department of Electronic Technology China Coast Guard Academy Ningbo China College of Electrical and Electronic Engineering Wenzhou University Wenzhou China

This paper presents a novel design scheme for a decodable self-resetting Regfile circuit that aims to reduce the power consumption caused by massive data and high computing power. The scheme consists of three steps: first, it analyzes the working principle and the energy consumption process of the decoding circuit; second, it proposes a self-resetting decoding circuit structure that uses the clock to control the decoding state and automatically resets when the clock is low, thus avoiding the extra power consumption due to the inversion of the memory cell state during the non-working period of the decoding circuit; third, it implements a two-read, two-write, decodable self-resetting Regfile circuit using a full-custom design method under the TSMC 7nm FinFET process. The experimental results demonstrate that the circuit has an area of 5593μm 2 and an energy-delay product of 0.41pJ•nS, and it can operate stably at 4GHz under various temperatures and process corner conditions.

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Emhiformer: An Enhanced Multi-Hypothesis Interaction Transformer for 3d Human Pose Estimation in Video

SSRN

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SSRN 2023年

作者： Xiang, Xuezhi Zhang, Kaixu Qiao, Yulong Saddik, Abdulmotaleb El School of Information and Communication Engineering Harbin Engineering University Harbin150006 China Key Laboratory of Advanced Marine Communication and Information Technology Ministry of Industry and Information Technology Harbin150001 China School of Electrical Engineering and Computer Science University of Ottawa OttawaONK1N 6N5 Canada

Monocular 3D human pose estimation is a challenging task because of depth ambiguity. Recent methods exploit spatio-temporal information and generate different hypothesises for simulating diverse solutions to solve that. However, these methods don't fully extract spatio-temporal information and the relationships of each hypothesis. To ease these limitations, we propose Enhanced Multi-Hypothesis Interaction Transformer(EMHIFormer) to model 3D pose with better performance. In detail, we build connections between Transformer layers to integrate spatio-temporal information from previous layer and establish more comprehensive hypothesises. Furthermore, a cross-hypothesis model is proposed to strengthen the relationships among various hypothesises. We also design an enhanced regression head to adaptively adjust channel weights and export final output. Experiments are conducted on Human3.6M and MPI-INF-3DHP to evaluate EMHIFormer. Results show EMHIFormer obtains competitive performance on Human3.6M and achieve state-of-the-art result on MPI-INF-3DHP. Compared with MHFormer, we outperform it by 0.5% MPJPE on Human3.6M dataset and 46.0% MPJPE on MPI-INF-3DHP. © 2023, The Authors. All rights reserved.

关键词： 3D modeling

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Modeling Inter-Intra Heterogeneity for Graph Federated Learning

arXiv

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arXiv 2024年

作者： Yu, Wentao Chen, Shuo Tong, Yongxin Gu, Tianlong Gong, Chen School of Computer Science and Engineering Nanjing University of Science and Technology China Center for Advanced Intelligence Project RIKEN Japan State Key Laboratory of Complex & Critical Software Environment Beihang University China Jinan University China Department of Automation Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University China

Heterogeneity is a fundamental and challenging issue in federated learning, especially for the graph data due to the complex relationships among the graph nodes. To deal with the heterogeneity, lots of existing methods perform the weighted federation based on their calculated similarities between pairwise clients (i.e., subgraphs). However, their inter-subgraph similarities estimated with the outputs of local models are less reliable, because the final outputs of local models may not comprehensively represent the real distribution of subgraph data. In addition, they ignore the critical intra-heterogeneity which usually exists within each subgraph itself. To address these issues, we propose a novel Federated learning method by integrally modeling the Inter-Intra Heterogeneity (FedIIH). For the inter-subgraph relationship, we propose a novel hierarchical variational model to infer the whole distribution of subgraph data in a multi-level form, so that we can accurately characterize the inter-subgraph similarities with the global perspective. For the intra-heterogeneity, we disentangle the subgraph into multiple latent factors and partition the model parameters into multiple parts, where each part corresponds to a single latent factor. Our FedIIH not only properly computes the distribution similarities between subgraphs, but also learns disentangled representations that are robust to irrelevant factors within subgraphs, so that it successfully considers the inter- and intra- heterogeneity simultaneously. Extensive experiments on six homophilic and five heterophilic graph datasets in both non-overlapping and overlapping settings demonstrate the effectiveness of our method when compared with nine state-of-the-art methods. Specifically, FedIIH averagely outperforms the second-best method by a large margin of 5.79% on all heterophilic datasets. Code is available at https://***/blgpb/FedIIH. Copyright © 2024, The Authors. All rights reserved.

关键词： Federated learning

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Melanoma Detection using a Random Forest Algorithm 10

Melanoma Detection using a Random Forest Algorithm

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10th E-Health and Bioengineering Conference, EHB 2022

作者： Damian, Felicia-Anisoara Moldovanu, Simona Moraru, Luminita The Modelling & Simulation Laboratory University Dunarea de Jos of Galati UGAL Galati Romania Department of Computer Science and Information Technology Faculty of Automation Computers Electrical Engineering and Electronics University Dunarea de Jos of Galati UGAL Galati Romania Department of Chemistry Physics and Environment Faculty of Sciences and Environment University Dunarea de Jos of Galati UGAL Galati Romania

ISBN: (纸本)9781665485579

In recent years, statistical evidence has identified melanoma as a major cause of skin cancer deaths. In this study, we propose a skin analysis lesions based on colour features (C) and Hu (H) moments to differentiate benign (nevi) and malignant (melanoma) lesions and, a classification based on Random Forest algorithm. Colour features are found as follows. Pre-processing for hair removing using the Dull-razor method;image segmentation based on the optimal colour threshold set on each RGB colour channel (red, green, blue);23 colour features extraction using the minimum and maximum RGB intensity and Hu moments;Random Forest (RF) algorithm to accurately differentiate benign and malignant skin lesions. This study used three datasets: 7-Point, Med-Node, PH2. RF classifier performance in terms of accuracy was: 94% for the 7-point dataset with H5, H7, H6, H2, C08 selected as relevant features;98.1% for the Med-Node dataset and H7, H5, H6, H2, H1 ACC the relevant features;and, 96.5% for the PH2 dataset and H5, H6, H7, C03, H1 selected as relevant features, respectively. Shape features based on Hu moments are found to improve the classification performance of the skin lesions compared to the colour features. © 2022 IEEE.

关键词： Image segmentation

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electrically Programmable Non-Volatile Silicon Photonic Content Addressable Memory (CAM) Cell

Electrically Programmable Non-Volatile Silicon Photonic Cont...

引用

Conference on Lasers and Electro-Optics (CLEO)

作者： Chuanyu Lian Hongyi Sun Yi-Siou Huang Steven A. Vitale Juejun Hu Ichiro Takeuchi Nathan Youngblood Christos Vagionas Carlos A. Ríos Ocampo Department of Materials Science & Engineering University of Maryland College Park MD USA Institute for Research in Electronics and Applied Physics University of Maryland College Park MD USA Advanced Materials and Microsystems Group MIT Lincoln Laboratory Lexington MA USA Department of Materials Science & Engineering MIT Cambridge MA USA Department of Physics Quantum Materials Center University of Maryland College Park MD USA Electrical & Computer Engineering Department The University of Pittsburgh Pittsburgh PA USA Department of Informatics Center for Interdisciplinary Research and Innovation Aristotle University of Thessaloniki Thessaloniki Greece

We experimentally demonstrate the first electrically programmable, non-volatile silicon photonic content addressable memory cell using Sb 2 Se3 phase change material on microring resonators, opening the path for light... 详细信息

ISBN: (纸本)9798350369311

关键词： Phase change materials Associative memory Nonvolatile memory Lasers and electrooptics Silicon photonics Table lookup Resonators Electro-optical waveguides

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AFDM-Enabled Integrated Sensing and Communication: Theoretical Framework and Pilot Design

arXiv

引用

arXiv 2025年

作者： Zhang, Fan Wang, Zhaocheng Mao, Tianqi Jiao, Tianyu Zhuo, Yinxiao Wen, Miaowen Xiang, Wei Chen, Sheng Karagiannidis, George K. Department of Electronic Engineering Tsinghua University Beijing100084 China State Key Laboratory of CNS/ATM Beijing Institute of Technology Beijing100081 China Zhuhai519088 China School of Electronic and Information Engineering South China University of Technology Guangzhou China School of Computing Engineering and Mathematical Sciences La Trobe University MelbourneVIC3086 Australia School of Electronics and Computer Science University of Southampton SouthamptonSO17 1BJ United Kingdom Department of Electrical and Computer Engineering Aristotle University of Thessaloniki Greece

The integrated sensing and communication (ISAC) has been envisioned as one representative usage scenario of sixth-generation (6G) network. However, the unprecedented characteristics of 6G, especially the doubly dispersive channel, make classical ISAC waveforms rather challenging to guarantee a desirable performance level. The recently proposed affine frequency division multiplexing (AFDM) can attain full diversity even under doubly dispersive effects, thus becoming a competitive candidate for next-generation ISAC waveforms. Relevant investigations are still at an early stage, which involve only straightforward design lacking explicit theoretical analysis. This paper provides an in-depth investigation on AFDM waveform design for ISAC applications. Specifically, the closed-form Crámer-Rao bounds of target detection for AFDM are derived, followed by a demonstration on its merits over existing counterparts. Furthermore, we formulate the ambiguity function of the pilot-assisted AFDM waveform for the first time, revealing conditions for stable sensing performance. To further enhance both the communication and sensing performance of the AFDM waveform, we propose a novel pilot design by exploiting the characteristics of AFDM signals. The proposed design is analytically validated to be capable of optimizing the ambiguity function property and channel estimation accuracy simultaneously as well as overcoming the sensing and channel estimation range limitation originated from the pilot spacing. Numerical results have verified the superiority of the proposed pilot design in terms of dual-functional performance. Copyright © 2025, The Authors. All rights reserved.

关键词： Cramer-Rao bounds

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Improved Classification of Benign and Malignant Breast Lesions Based on Contrast Manipulation and Gradient Map 10

Improved Classification of Benign and Malignant Breast Lesio...

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10th E-Health and Bioengineering Conference, EHB 2022

作者： Nastase, Iulia-Nela Anghelache Moldovanu, Simona Moraru, Luminita The Modelling & Simulation Laboratory University Dunarea de Jos of Galati UGAL Galati Romania Department of Computer Science and Information Technology Faculty of Automation Computers Electrical Engineering and Electronics University Dunarea de Jos of Galati UGAL Galati Romania Department of Chemistry Physics and Environment Faculty of Sciences and Environment University Dunarea de Jos of Galati UGAL Galati Romania

ISBN: (纸本)9781665485579

This study proposes an approach to enhance the contrast breast ultrasound (BUS) images with the preservation of the finer details. The main idea is to use the relevant and most important features in the classification of BUS images as benign or malignant. Among the contrast manipulation techniques, both linear under- and overexposure methods, contrast limiting adaptive histogram equalization (CLAHE) method and homomorphic filtering are used. The gradient maps of original images are generated for further analysis. Also, the gradient maps of original images are improved by the local mean subtraction and divisive normalization. Eight structural features are extracted from each gradient map image type. The extracted features feed a Random Forest (RF) classifier to determine their relevance for BUS images classification. An accuracy of 97.12% has been obtained for improved gradient map images and 93.03% for gradient map images. © 2022 IEEE.

关键词： Image classification

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Nonreciprocal spin-charge interconversion in topological insulator/ferromagnet heterostructures

arXiv

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arXiv 2025年

作者： Shi, Shuyuan Liu, Enlong Hu, Fanrui Shi, Guoyi Manchon, Aurélien Yang, Hyunsoo Department of Electrical and Computer Engineering National University of Singapore 117583 Singapore Fert Beijing Institute MIIT Key Laboratory of Spintronics School of Integrated Circuit Science and Engineering Beihang University Beijing100191 China National Key Laboratory of Spintronics Hangzhou International Innovation Institute Beihang University Hangzhou311115 China Aix-Marseille Université CNRS CINaM Marseille France

The process of spin-charge interconversion is critical in modern spintronics. Nonetheless, experiments conducted on a wide variety of magnetic heterostructures consistently report that charge-to-spin and spin-to-charge conversion efficiencies can be vastly different, especially in the case of topological insulators (TI). This discrepancy between the two "reciprocal" effects remains unexplained, hampering the development of spin devices based on spin-charge conversion. In this study, we investigate both spin-charge and charge-spin interconversion processes in TI Bi2Te3/Py and Pt/Py bilayers experimentally using spin-torque ferromagnetic resonance and spin pumping techniques. We find that the measured charge-to-spin conversion efficiency (C-S) in TI/Py is ~26 times larger than the measured spin-to-charge conversion efficiency (S-C), whereas C-S and S-C are comparable in the case of Pt/Py. Using a theoretical model enforcing Onsager reciprocity, we show that spin-to-charge and charge-to-spin conversions in bilayers are genuinely inequivalent, and explain our results as arising from the distinct spin current leakage that takes place during the interconversion. This work clarifies previous conflicting reports on spin-charge interconversion processes and highlights the potential of interface engineering to achieve efficient spin transport in TI-based ferromagnetic heterostructures, paving the way for highly efficient spintronic devices. Copyright © 2025, The Authors. All rights reserved.

关键词： Topological insulators

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Battle Against Fluctuating Quantum Noise: Compression-Aided Framework to Enable Robust Quantum Neural Network

arXiv

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arXiv 2023年

作者： Hu, Zhirui Lin, Youzuo Guan, Qiang Jiang, Weiwen Electrical and Computer Engineering Department George Mason University FairfaxVA22030 United States Quantum Science and Engineering Center George Mason University FairfaxVA22030 United States Earth and Environmental Sciences Division Los Alamos National Laboratory NM87545 United States Department of Computer Science Kent State University 800 E Summit St KentOH44240 United States

Recently, we have been witnessing the scale-up of superconducting quantum computers;however, the noise of quantum bits (qubits) is still an obstacle for real-world applications to leveraging the power of quantum computing. Although there exist error mitigation or error-aware designs for quantum applications, the inherent fluctuation of noise (a.k.a., instability) can easily collapse the performance of error-aware designs. What’s worse, users can even not be aware of the performance degradation caused by the change in noise. To address both issues, in this paper we use Quantum Neural Network (QNN) as a vehicle to present a novel compression-aided framework, namely QuCAD, which will adapt a trained QNN to fluctuating quantum noise. In addition, with the historical calibration (noise) data, our framework will build a model repository offline, which will significantly reduce the optimization time in the online adaption process. Emulation results on an earthquake detection dataset show that QuCAD can achieve 14.91% accuracy gain on average in 146 days over a noise-aware training approach. For the execution on a 7-qubit IBM quantum processor, ibm-jakarta, QuCAD can consistently achieve 12.52% accuracy gain on earthquake detection. © 2023, CC BY-NC-ND.

关键词： Quantum noise

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