Papers Quantum Machine Learning
“Quantum Machine Learning” 태그가 달린 논문 945편 · 필터 해제
Adversarial Robustness Guarantees for Quantum Classifiers
Despite their ever more widespread deployment throughout society, machine learning algorithms remain critically vulnerable to being spoofed by subtle adversarial tampering with their input data. The prospect of near-term…
Adversarial RobustnessQuantum Machine LearningProspects of Privacy Advantage in Quantum Machine Learning
Ensuring data privacy in machine learning models is critical, particularly in distributed settings where model gradients are typically shared among multiple parties to allow collaborative learning. Motivated by the incre…
Quantum Machine LearningUniversal replication of chaotic characteristics by classical and quantum machine learning
Replicating chaotic characteristics of non-linear dynamics by machine learning (ML) has recently drawn wide attentions. In this work, we propose that a ML model, trained to predict the state one-step-ahead from several l…
Quantum Machine LearningFeature Importance and Explainability in Quantum Machine Learning
Many Machine Learning (ML) models are referred to as black box models, providing no real insights into why a prediction is made. Feature importance and explainability are important for increasing transparency and trust i…
Feature ImportanceQuantum Machine LearningGraph Neural Networks for Parameterized Quantum Circuits Expressibility Estimation
Parameterized quantum circuits (PQCs) are fundamental to quantum machine learning (QML), quantum optimization, and variational quantum algorithms (VQAs). The expressibility of PQCs is a measure that determines their capa…
Quantum Machine LearningSeparable Power of Classical and Quantum Learning Protocols Through the Lens of No-Free-Lunch Theorem
The No-Free-Lunch (NFL) theorem, which quantifies problem- and data-independent generalization errors regardless of the optimization process, provides a foundational framework for comprehending diverse learning protocols…
AttributeQuantum Machine LearningDiabetic Retinopathy Detection Using Quantum Transfer Learning
Diabetic Retinopathy (DR), a prevalent complication in diabetes patients, can lead to vision impairment due to lesions formed on the retina. Detecting DR at an advanced stage often results in irreversible blindness. The …
Diabetic Retinopathy DetectionQuantum Machine LearningTransfer LearningQuantum AI for Alzheimer's disease early screening
Quantum machine learning is a new research field combining quantum information science and machine learning. Quantum computing technologies appear to be particularly well-suited for addressing problems in the health sect…
Quantum Machine LearningExpanding the Horizon: Enabling Hybrid Quantum Transfer Learning for Long-Tailed Chest X-Ray Classification
Quantum machine learning (QML) has the potential for improving the multi-label classification of rare, albeit critical, diseases in large-scale chest x-ray (CXR) datasets due to theoretical quantum advantages over classi…
Binary ClassificationClassificationMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATION+3Machine Learning for Quantum Computing Specialists
Quantum machine learning (QML) is a promising early use case for quantum computing. There has been progress in the last five years from theoretical studies and numerical simulations to proof of concepts. Use cases demons…
Quantum Machine LearningPermutation-equivariant quantum convolutional neural networks
The Symmetric group $S_{n}$ manifests itself in large classes of quantum systems as the invariance of certain characteristics of a quantum state with respect to permuting the qubits. The subgroups of $S_{n}$ arise, among…
Quantum Machine LearningQuantum Patch-Based Autoencoder for Anomaly Segmentation
Quantum Machine Learning investigates the possibility of quantum computers enhancing Machine Learning algorithms. Anomaly segmentation is a fundamental task in various domains to identify irregularities at sample level a…
Anomaly SegmentationQuantum Machine LearningSegmentationConstructing Optimal Noise Channels for Enhanced Robustness in Quantum Machine Learning
With the rapid advancement of Quantum Machine Learning (QML), the critical need to enhance security measures against adversarial attacks and protect QML models becomes increasingly evident. In this work, we outline the c…
Quantum Machine LearningGuided-SPSA: Simultaneous Perturbation Stochastic Approximation assisted by the Parameter Shift Rule
The study of variational quantum algorithms (VQCs) has received significant attention from the quantum computing community in recent years. These hybrid algorithms, utilizing both classical and quantum components, are we…
Quantum Machine LearningGuardians of the Quantum GAN
Quantum Generative Adversarial Networks (qGANs) are at the forefront of image-generating quantum machine learning models. To accommodate the growing demand for Noisy Intermediate-Scale Quantum (NISQ) devices to train and…
Quantum Machine LearningA Comparative Analysis of Adversarial Robustness for Quantum and Classical Machine Learning Models
Quantum machine learning (QML) continues to be an area of tremendous interest from research and industry. While QML models have been shown to be vulnerable to adversarial attacks much in the same manner as classical mach…
Adversarial RobustnessQuantum Machine LearningPristiQ: A Co-Design Framework for Preserving Data Security of Quantum Learning in the Cloud
Benefiting from cloud computing, today's early-stage quantum computers can be remotely accessed via the cloud services, known as Quantum-as-a-Service (QaaS). However, it poses a high risk of data leakage in quantum machi…
Cloud ComputingQuantum Machine LearningOn-board classification of underwater images using hybrid classical-quantum CNN based method
Underwater images taken from autonomous underwater vehicles (AUV's) often suffer from low light, high turbidity, poor contrast, motion-blur and excessive light scattering and hence require image enhancement techniques fo…
Autonomous VehiclesGPUHybrid Machine Learningimage-classification+5A Modified Depolarization Approach for Efficient Quantum Machine Learning
Quantum Computing in the Noisy Intermediate-Scale Quantum (NISQ) era has shown promising applications in machine learning, optimization, and cryptography. Despite the progress, challenges persist due to system noise, err…
Quantum Machine LearningQuantum Circuit $C^*$-algebra Net
This paper introduces quantum circuit $C^*$-algebra net, which provides a connection between $C^*$-algebra nets proposed in classical machine learning and quantum circuits. Using $C^*$-algebra, a generalization of the sp…
image-classificationImage ClassificationQuantum Machine Learning