A5021414038- Quantum Computing Algorithms and Architecture
- Quantum Information and Cryptography
- Neural Networks and Reservoir Computing
- Advancements in Semiconductor Devices and Circuit Design
- Stochastic Gradient Optimization Techniques
- Stock Market Forecasting Methods
- Quantum and electron transport phenomena
- Particle physics theoretical and experimental studies
- Particle Detector Development and Performance
- Neural Networks and Applications
- Quantum Mechanics and Applications
- Explainable Artificial Intelligence (XAI)
- Advanced Memory and Neural Computing
- Privacy-Preserving Technologies in Data
- Anomaly Detection Techniques and Applications
- Computational Physics and Python Applications
- Market Dynamics and Volatility
- Adversarial Robustness in Machine Learning
- High-Energy Particle Collisions Research
- Molecular Communication and Nanonetworks
- Topic Modeling
- Retinal Imaging and Analysis
- Quantum-Dot Cellular Automata
- Blockchain Technology Applications and Security
- Fault Detection and Control Systems
Wells Fargo (United States)
2023-2025
Brookhaven National Laboratory
2020-2024
Computational Physics (United States)
2021
Fermi National Accelerator Laboratory
2021
University of Wisconsin–Madison
2021
National Center for Theoretical Sciences
2019-2020
National Taiwan University
2019-2020
The state-of-the-art machine learning approaches are based on classical von Neumann computing architectures and have been widely used in many industrial academic domains. With the recent development of quantum computing, researchers tech-giants attempted new circuits for tasks. However, existing platforms hard to simulate deep models or problems because intractability circuits. Thus, it is necessary design feasible algorithms noisy intermediate scale (NISQ) devices. This work explores...
We propose a novel decentralized feature extraction approach in federated learning to address privacy-preservation issues for speech recognition. It is built upon quantum convolutional neural network (QCNN) composed of circuit encoder extraction, and recurrent (RNN) based end-to-end acoustic model (AM). To enhance parameter protection architecture, an input first up-streamed computing server extract Mel-spectrogram, the corresponding features are encoded using algorithm with random...
Long short-term memory (LSTM) is a kind of recurrent neural networks (RNN) for sequence and temporal dependency data modeling its effectiveness has been extensively established. In this work, we propose hybrid quantum-classical model LSTM, which dub QLSTM. We demonstrate that the proposed successfully learns several kinds data. particular, show certain testing cases, quantum version LSTM converges faster, or equivalently, reaches better accuracy, than classical counterpart. Due to...
This paper presents a quantum convolutional neural network (QCNN) for the classification of high energy physics events. The proposed model is tested using simulated dataset from Deep Underground Neutrino Experiment. architecture demonstrates an advantage learning faster than classical networks (CNNs) under similar number parameters. In addition to convergence, QCNN achieves greater test accuracy compared CNNs. Based on our results numerical simulations, it promising direction apply and other...
Distributed training across several quantum computers could significantly improve the time and if we share learned model, not data, it potentially data privacy as would happen where is located. One of potential schemes to achieve this property federated learning (FL), which consists clients or local nodes on their own a central node aggregate models collected from those nodes. However, best our knowledge, no work has been done in machine (QML) federation setting yet. In work, present hybrid...
Quantum machine learning could possibly become a valuable alternative to classical for applications in High Energy Physics by offering computational speed-ups. In this study, we employ support vector with quantum kernel estimator (QSVM-Kernel method) recent LHC flagship physics analysis: $t\bar{t}H$ (Higgs boson production association top quark pair). our simulation study using up 20 qubits and 50000 events, the QSVM-Kernel method performs as well its counterparts three different platforms...
One of the major objectives experimental programs at Large Hadron Collider (LHC) is discovery new physics. This requires identification rare signals in immense backgrounds. Using machine learning algorithms greatly enhances our ability to achieve this objective. With progress quantum technologies, could become a powerful tool for data analysis high energy In study, using IBM gate-model computing systems, we employ variational classifier method two recent LHC flagship physics analyses: (Higgs...
In this paper, we discuss the initial attempts at boosting understanding human language based on deep-learning models with quantum computing. We successfully train a quantum-enhanced Long Short-Term Memory network to perform parts-of-speech tagging task via numerical simulations. Moreover, Transformer is proposed sentiment analysis existing dataset.
Abstract Quantum machine learning (QML) can complement the growing trend of using learned models for a myriad classification tasks, from image recognition to natural speech processing. There exists potential quantum advantage due intractability operations on classical computer. Many datasets used in are crowd sourced or contain some private information, but best our knowledge, no current QML equipped with privacy-preserving features. This raises concerns as it is paramount that do not expose...
Abstract Recent advances in classical reinforcement learning (RL) and quantum computation point to a promising direction for performing RL on computer. However, potential applications are limited by the number of qubits available modern devices. Here, we present two frameworks deep tasks using gradient-free evolutionary optimization. First, apply amplitude encoding scheme Cart-Pole problem, where demonstrate advantage parameter saving encoding. Second, propose hybrid framework agents...
We introduce a hybrid model combining quantum-inspired tensor network and variational quantum circuit to perform supervised learning tasks. This architecture allows for the classical parts of be trained simultaneously, providing an end-to-end training framework. show that compared principal component analysis, based on matrix product state with low bond dimensions performs better as feature extractor input data in binary ternary classification MNIST Fashion-MNIST datasets. The is highly...
The rapid development of quantum computing has demonstrated many unique characteristics advantages, such as richer feature representation and more secured protection on model parameters. This work proposes a vertical federated learning architecture based variational circuits to demonstrate the competitive performance quantum-enhanced pre-trained BERT for text classification. In particular, our proposed hybrid classical-quantum consists novel random temporal convolution (QTC) framework...
Quantum federated learning (QFL) is a novel framework that integrates the advantages of classical (FL) with computational power quantum technologies. This includes computing and machine (QML), enabling QFL to handle high-dimensional complex data. can be deployed over both communication networks in order benefit from information-theoretic security levels surpassing traditional FL frameworks. In this paper, we provide first comprehensive investigation challenges opportunities QFL. We...
Abstract Quantum federated learning (QFL) can facilitate collaborative across multiple clients using quantum machine (QML) models, while preserving data privacy. Although recent advances in QFL span different tasks like classification leveraging several types, no prior work has focused on developing a framework that utilizes temporal to approximate functions useful analyze the performance of distributed sensing networks. In this paper, novel is first integrate long short-term memory (QLSTM)...
The preservation of privacy is a critical concern in the implementation artificial intelligence on sensitive training data. There are several techniques to preserve data but quantum computations inherently more secure due nocloning theorem, resulting most desirable computational platform top potential advantages. have been prior works protecting by Quantum Federated Learning (QFL) and Differential Privacy (QDP) studied independently. However, best our knowledge, no work has addressed both...
Recent advances in quantum computing have drawn considerable attention to building realistic application for and using computers. However, designing a suitable circuit architecture requires expert knowledge. For example, it is non-trivial design gate sequence generating particular state with as fewer gates possible. We propose search framework the power of deep reinforcement learning (DRL) address this challenge. In proposed framework, DRL agent can only access Pauli-$X$, $Y$, $Z$...
Recent advances in quantum computing (QC) and machine learning (ML) have drawn significant attention to the development of (QML). Reinforcement (RL) is one ML paradigms which can be used solve complex sequential decision making problems. Classical RL has been shown capable various challenging tasks. However, algorithms world are still their infancy. One challenges yet how train partially observable environments. In this paper, we approach challenge through building QRL agents with recurrent...
This work analyzes transfer learning of the Variational Quantum Circuit (VQC). Our framework begins with a pretrained VQC configured in one domain and calculates transition 1-parameter unitary subgroups required for new domain. A formalism is established to investigate adaptability capability under analysis loss bounds. theory observes knowledge VQCs provides heuristic interpretation mechanism. An analytical fine-tuning method derived attain optimal adaptations similar domains.
The rapid progress in quantum computing (QC) and machine learning (ML) has attracted growing attention, prompting extensive research into (QML) algorithms to solve diverse complex problems. Designing high-performance QML models demands expert-level proficiency, which remains a significant obstacle the broader adoption of QML. A few major hurdles include crafting effective data encoding techniques parameterized circuits, both are crucial performance models. Additionally, measurement phase is...
Recent advancements have highlighted the limitations of current quantum systems, particularly restricted number qubits available on near-term devices. This constraint greatly inhibits range applications that can leverage computers. Moreover, as increase, computational complexity grows exponentially, posing additional challenges. Consequently, there is an urgent need to use efficiently and mitigate both present future complexities. To address this, existing attempt integrate classical systems...
The utility of machine learning has rapidly expanded in the last two decades and presented an ethical challenge. Papernot et. al. developed a technique, known as Private Aggregation Teacher Ensembles (PATE) to enable federated which multiple distributed teachers are trained on disjoint data sets. This study is first apply PATE ensemble quantum neural networks (QNN) pave new way ensuring privacy (QML).