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Quantum Neural Network Compression

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Quantum Neural Network Compression

IEEE International Conference on computer-Aided Design

作者： Zhirui Hu Peiyan Dong Zhepeng Wang Youzuo Lin Yanzhi Wang Weiwen Jiang Electrical and Computer Engineering Department George Mason University Fairfax Virginia United States Quantum Science and Engineering Center George Mason University Fairfax Virginia United States Department of Electrical and Computer Engineering Northeastern University Boston MA United States Earth and Environmental Sciences Division Los Alamos National Laboratory NM United States

Model compression, such as pruning and quantization, has been widely applied to optimize neural networks on resource-limited classical devices. Recently, there are growing interest in variational quantum circuits (VQC), that is, a type of neural network on quantum computers (a.k.a., quantum neural networks). It is well known that the near-term quantum devices have high noise and limited resources (i.e., quantum bits, qubits); yet, how to compress quantum neural networks has not been thoroughly studied. One might think it is straightforward to apply the classical compression techniques to quantum scenarios. However, this paper reveals that there exist differences between the compression of quantum and classical neural networks. Based on our observations, we claim that the compilation/traspilation has to be involved in the compression process. On top of this, we propose the very first systematical framework, namely CompVQC, to compress quantum neural networks (QNNs). In CompVQC, the key component is a novel compression algorithm, which is based on the alternating direction method of multipliers (ADMM) approach. Experiments demonstrate the advantage of the CompVQC, reducing the circuit depth (almost over 2.5×) with a negligible accuracy drop (<1%), which outperforms other competitors. Another promising truth is our CompVQC can indeed promote the robustness of the QNN on the near-term noisy quantum devices.

关键词： Quantization (signal) Neural networks Qubit Neural network compression Machine learning Robustness Noise measurement

State Derivative Normalization for Continuous-Time Deep Neural Networks

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arXiv 2024年

作者： Weigand, Jonas Beintema, Gerben I. Ulmen, Jonas Görges, Daniel Tóth, Roland Schoukens, Maarten Ruskowski, Martin Chair of Machine Tools and Control Systems RPTU Kaiserslautern The German Research Center for Artificial Intelligence Kaiserslautern Germany Group The Department of Electrical Engineering Eindhoven University of Technology Netherlands The Systems and Control Laboratory The Institute for Computer Science and Control Budapest Hungary Institute for Electromobility RPTU Kaiserslautern Germany

The importance of proper data normalization for deep neural networks is well known. However, in continuous-time state-space model estimation, it has been observed that improper normalization of either the hidden state or hidden state derivative of the model estimate, or even of the time interval can lead to numerical and optimization challenges with deep learning based methods. This results in a reduced model quality. In this contribution, we show that these three normalization tasks are inherently coupled. Due to the existence of this coupling, we propose a solution to all three normalization challenges by introducing a normalization constant at the state derivative level. We show that the appropriate choice of the normalization constant is related to the dynamics of the to-be-identified system and we derive multiple methods of obtaining an effective normalization constant. We compare and discuss all the normalization strategies on a benchmark problem based on experimental data from a cascaded tanks system and compare our results with other methods of the identification literature. © 2024, CC BY-SA.

关键词： Ordinary differential equations

Characterizing RNA Pseudouridylation by Convolutional Neural Networks

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Genomics, Proteomics & Bioinformatics 2021年第5期19卷 815-833页

作者： Xuan He Sai Zhang Yanqing Zhang Zhixin Lei Tao Jiang Jianyang Zeng Institute for Interdisciplinary Information Sciences Tsinghua UniversityBeijing 100084China Peking-Tsinghua Center for Life Sciences Peking UniversityBeijing 100871China Academy for Advanced Interdisciplinary Studies Peking UniversityBeijing 100871China Department of Computer Science and Engineering University of CaliforniaRiversideCA 92521USA MOE Key Lab of Bioinformatics and Bioinformatics Division BNRIST/Department of Computer Science and TechnologyTsinghua UniversityBeijing 100084China Institute of Integrative Genome Biology University of CaliforniaRiversideCA 92521USA MOE Key Laboratory of Bioinformatics Tsinghua UniversityBeijing 100084China

Pseudouridine(Ψ)is the most prevalent post-transcriptional RNA modification and is widespread in small cellular RNAs and m ***,the functions,mechanisms,and precise distribution ofΨs(especially in m RNAs)still remain largely *** landscape ofΨs across the transcriptome has not yet been fully ***,we present a highly effective model based on a convolutional neural network(CNN),called Pseudo Uridy Lation Site Estimator(PULSE),to analyze large-scale profiling data ofΨsites and characterize the contextual sequence features of ***,consisting of two alternatively-stacked convolution and pooling layers followed by a fully-connected neural network,can automatically learn the hidden patterns of pseudouridylation from the local sequence *** validation tests demonstrated that PULSE can outperform other state-of-the-art prediction methods and achieve high prediction accuracy,thus enabling us to further characterize the transcriptome-wide landscape ofΨ*** further showed that the prediction results derived from PULSE can provide novel insights into understanding the functional roles of pseudouridylation,such as the regulations of RNA secondary structure,codon usage,translation,and RNA stability,and the connection to single nucleotide *** source code and final model for PULSE are available at https://***/mlcb-thu/PULSE.

关键词： Pseudouridylation Convolution neural network Sequence motif Translation RNA stability

Dephasing in Fluxonium Qubits from Coherent Quantum Phase Slips

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arXiv 2024年

作者： Randeria, Mallika T. Hazard, Thomas M. Di Paolo, Agustin Azar, Kate Hays, Max Ding, Leon An, Junyoung Gingras, Michael Niedzielski, Bethany M. Stickler, Hannah Grover, Jeffrey A. Yoder, Jonilyn L. Schwartz, Mollie E. Oliver, William D. Serniak, Kyle Lincoln Laboratory Massachusetts Institute of Technology LexingtonMA02421 United States Research Laboratory of Electronics Massachusetts Institute of Technology CambridgeMA02139 United States Department of Physics Massachusetts Institute of Technology CambridgeMA02139 United States Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology CambridgeMA02139 United States

Phase slips occur across all Josephson junctions (JJs) at a rate that increases with the impedance of the junction. In superconducting qubits composed of JJ-array superinductors - such as fluxonium - phase slips in the array can lead to decoherence. In particular, phase-slip processes at the individual array junctions can coherently interfere, each with an Aharonov-Casher phase that depends on the offset charges of the array islands. These coherent quantum phase slips (CQPS) perturbatively modify the qubit frequency, and therefore charge noise on the array islands will lead to dephasing. By varying the impedance of the array junctions, we design a set of fluxonium qubits in which the expected phase-slip rate within the JJ-array changes by several orders of magnitude. We characterize the coherence times of these qubits and demonstrate that the scaling of CQPS-induced dephasing rates agrees with our theoretical model. Furthermore, we perform noise spectroscopy of two qubits in regimes dominated by either CQPS or flux noise. We find the noise power spectrum associated with CQPS dephasing appears to be featureless at low frequencies and not 1/f. Numerical simulations indicate this behavior is consistent with charge noise generated by charge-parity fluctuations within the array. Our findings broadly inform JJ-array-design tradeoffs, relevant for the numerous superconducting qubit designs employing JJ-array superinductors. Copyright © 2024, The Authors. All rights reserved.

关键词： Qubits

Real-Time Implementation of a Spiking Neural Network-Based Control: An Application for the Ball and Plate System

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Real-Time Implementation of a Spiking Neural Network-Based C...

American Control Conference (ACC)

作者： Diego Chavez Arana Omar A. Garcia A Ignacio Rubio Scola Eduardo S. Espinoza Luis Rodolfo Garcia Carrillo Andrew Sornborger Klipsch School of Electrical and Computer Engineering New Mexico State University Las Cruces NM USA Center for Research and Advanced Studies National Polytechnic Institute Mexico City Mexico Department of Industrial Products Engineering National Institute of Industrial Technology CONICET and National University of Rosario Argentina Los Alamos National Laboratory Los Alamos NM USA

ISBN: (数字)9798350382655

ISBN: (纸本)9798350382662

We present a neuromorphic computing control architecture for the problem of stabilizing a benchmark sub-actuated system: the ball and plate platform. The proposed architecture makes use of Spiking Neural Networks (SNNs) as an alternative to traditional von Neumann computing. The Neural engineering Framework (NEF) is adopted to encode the SNN-based controller to accomplish position and trajectory tasks. Simulation results and a real-time implementation of the proposed SNN controller are presented over a homemade ball and plate prototype. The effectiveness of the proposed neuromorphic controller is demonstrated, even in situations where the system is affected by external impulsive disturbances.

关键词： Neuromorphic engineering Simulation Prototypes Neural engineering computer architecture Spiking neural networks Control systems

FAS-assisted NOMA Short-Packet Communication Systems

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arXiv 2023年

作者： Zheng, Jianchao Wu, Tuo Lai, Xiazhi Pan, Cunhua Elkashlan, Maged Wong, Kai-Kit The School of Computer Science and Engineering Huizhou University Huizhou516000 China The School of Electronic Engineering and Computer Science Queen Mary University of London LondonE1 4NS United Kingdom The School of Computer Science Guangdong University of Education Guangdong Guangzhou China The National Mobile Communications Research Laboratory Southeast University Nanjing210096 China The Department of Electronic and Electrical Engineering University College London LondonWC1E 6BT United Kingdom The Yonsei Frontier Laboratory The School of Integrated Technology Yonsei University Seoul03722 Korea Republic of

In this paper, we investigate a fluid antenna system (FAS)-assisted downlink non-orthogonal multiple access (NOMA) for short-packet communications. The base station (BS) adopts a single fixed antenna, while both the central user (CU) and the cell-edge user (CEU) are equipped with a FAS. Each FAS comprises N flexible positions (also known as ports), linked to N arbitrarily correlated Rayleigh fading channels. We derive expressions for the average block error rate (BLER) of the FAS-assisted NOMA system and provide asymptotic BLER expressions. We determine that the diversity order for CU and CEU is N, indicating that the system performance can be considerably improved by increasing N. Simulation results validate the great performance of FAS. © 2023, CC BY.

关键词： Antennas

Flat-band (de)localization emulated with a superconducting qubit array

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arXiv 2024年

作者： Rosen, Ilan T. Muschinske, Sarah Barrett, Cora N. Rower, David A. Das, Rabindra Kim, David K. Niedzielski, Bethany M. Schuldt, Meghan Serniak, Kyle Schwartz, Mollie E. Yoder, Jonilyn L. Grover, Jeffrey A. Oliver, William D. Research Laboratory of Electronics Massachusetts Institute of Technology CambridgeMA02139 United States Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology CambridgeMA02139 United States Department of Physics Massachusetts Institute of Technology CambridgeMA02139 United States Lincoln Laboratory Massachusetts Institute of Technology LexingtonMA02421 United States

Arrays of coupled superconducting qubits are analog quantum simulators able to emulate a wide range of tight-binding models in parameter regimes that are difficult to access or adjust in natural materials. In this work, we use a superconducting qubit array to emulate a tight-binding model on the rhombic lattice, which features flat bands. Enabled by broad adjustability of the dispersion of the energy bands and of on-site disorder, we examine regimes where flat-band localization and Anderson localization compete. We observe disorder-induced localization for dispersive bands and disorder-induced delocalization for flat bands. Remarkably, we find a sudden transition between the two regimes and, in its vicinity, the semblance of quantum critical scaling. Copyright © 2024, The Authors. All rights reserved.

关键词： Qubits

Time-varying Nonlinear Effects in Terahertz Generation

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arXiv 2024年

作者： Lu, Yongchang Zhang, Xueqian Qiu, Haidi Niu, Li Chen, Xieyu Xu, Quan Zhang, Weili Zhang, Shuang Han, Jiaguang Center for Terahertz waves College of Precision Instrument and Optoelectronics Engineering Tianjin University The Key Laboratory of Optoelectronics Information and Technology Ministry of Education Tianjin300072 China School of Electrical and Computer Engineering Oklahoma State University StillwaterOK74078 United States Department of Physics Faculty of Science University of Hong Kong Hong Kong

Time-varying effects have unveiled new possibilities for manipulating electromagnetic waves through the temporal dimension. In this study, we experimentally explore these effects in the nonlinear optical process of terahertz (THz) generation using optically pumped indium tin oxide (ITO) films. The ultrafast carrier dynamics in the ITO film endow the second-order nonlinear susceptibility (χ(2)) with sub-picosecond temporal evolution, establishing a temporal boundary for the generated THz waves. We observe significant amplitude and frequency modulations in the THz generation at various transients, attributed to the time-varying complex amplitude of the χ(2). Moreover, we also observed polarization modulations when further exploiting the tensor properties of χ(2). This work advances the exploration of time-varying effects into the nonlinear regime through frequency down-conversion, effectively transferring the strong time-varying material response from the near-infrared (NIR) band to the THz band. These findings open up new opportunities for realizing time-varying phenomena that specifically require single-cycle modulation. Copyright © 2024, The Authors. All rights reserved.

关键词： Terahertz waves

A New IoT-Based Smart Greenhouse for Cultivation of Water Convolvulus

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A New IoT-Based Smart Greenhouse for Cultivation of Water Co...

13th International electrical engineering Congress, iEECON 2025

作者： Akkaraekthalin, Prayoot Somwong, Tanee Chudpooti, Nonchanutt Hirun, Nuttawut Meesomklin, Suwaluck King Mongkut's University of Technology North Bangkok Faculty of Engineering Department of Electrical and Computer Engineering Bangkok10800 Thailand Rajamangala University of Technology Suvarnabhumi Faculty of Engineering and Architecture Department of Electronic and Telecommunications Engineering Nonthaburi11000 Thailand King Mongkut's University of Technology North Bangkok Faculty of Applied Science Department of Industrial Physics and Medical Instrumentation Bangkok10800 Thailand King Mongkut's University of Technology North Bangkok College of Industrial Technology Department of Electronics Engineering Technology Bangkok10800 Thailand

ISBN: (纸本)9798331543952

This paper presents a new IOT system for monitoring and controlling plant cultivation in greenhouse system. The system aims to regulate and monitor the environmental conditions suitable for plant cultivation in real-time. The system utilizes sensors to measure environmental conditions, such as soil moisture sensor, light intensity sensor, temperature sensor, and air humidity sensor. The Node MCU ESP32 is used for processing and controlling these operations. The system is designed to send notifications through Line application and allows farmers to monitor the system's performance and plant growth in real-time via a smartphone application. It can utilize the Blynk IoT application on a smartphone for remotely monitoring and controlling the environmental conditions and provides historical data. This experiment demonstrates that the designed system can successfully cultivate plants and promote better growth compared to traditional cultivation methods with a 13.38% improvement. Additionally, it provides convenience and ease of management for plant cultivation. © 2025 IEEE.

关键词： greenhouse system IOT smart agriculture Smart farming water convolvulus