Quantum Complete Graph Neural Network for Jet Discrimination in High Energy Physics
The authors propose the Quantum Complete Graph Neural Network (QCGNN) for jet discrimination, showcasing a polynomial speedup over classical counterparts due to quantum parallelism.
Effiziente Messung und Analyse von Quantum Convolutional Neural Networks für die Erkennung translationssymmetrischer Daten
Effiziente Parallelisierung von Quantum Convolutional Neural Networks verbessert die Messungseffizienz und beschleunigt das Lernen.
Jet-Diskriminierung mit Quantum Complete Graph Neural Network
Quantum Complete Graph Neural Network (QCGNN) bietet eine effiziente Methode für das Lernen unstrukturierter Jets in der Hochenergiephysik.
A Comprehensive Review of Quantum Machine Learning: From Noisy Intermediate-Scale Quantum (NISQ) to Fault-Tolerant Quantum Computation
Quantum machine learning, which involves running machine learning algorithms on quantum devices, has significant potential but faces challenges in the current Noisy Intermediate-Scale Quantum (NISQ) era. This review provides a comprehensive overview of the various concepts and techniques that have emerged in the field, including Variational Quantum Algorithms (VQA), Quantum Neural Tangent Kernel (QNTK), and the issue of barren plateaus. It also explores the potential of Fault-Tolerant Quantum Computation (FTQC) algorithms and their applications in quantum machine learning.
Efficient Quantum Machine Learning through a Modified Depolarization Approach
A modified representation of the depolarization channel using only two Kraus operators based on the X and Z Pauli matrices reduces the computational complexity from six to four matrix multiplications per channel execution, enabling more efficient and scalable simulations of quantum circuits under depolarization noise.
Quantum Machine Learning: Exploring Emerging Techniques and Applications
Quantum machine learning (QML) is a promising early use case for quantum computing, with recent progress from theoretical studies and numerical simulations to proof-of-concept demonstrations on contemporary quantum devices.
Unconditional Quantum Advantage in Machine Learning Using Entanglement
This paper demonstrates a provable and robust quantum advantage in machine learning tasks by leveraging entanglement to reduce communication complexity, achieving superior performance in expressivity, inference speed, and training efficiency compared to classical models.
Quantum Kolmogorov-Arnold Networks: An Implementation Strategy Using Quantum Signal Processing Circuits
This paper proposes a novel method for implementing Kolmogorov-Arnold Networks (KANs) on quantum computers by leveraging quantum signal processing (QSP) circuits, potentially enabling more robust and efficient quantum machine learning.
Training a Quantum Circuit Born Machine with Error Mitigation for Generative Learning on a Photonic Quantum Processor
This research demonstrates that a specific error mitigation technique called "recycling mitigation" significantly improves the training of Quantum Circuit Born Machines (QCBMs) for generative learning tasks on a photonic quantum processor, even in the presence of significant photon loss.
Linear Regression with Quantum Annealing Using a Bosonic System
This paper proposes a novel method for performing linear regression using quantum annealing with continuous variables, leveraging a bosonic system to overcome the limitations of qubit-based approaches and achieve higher accuracy without increasing qubit requirements.
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