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Found 13 papers, 4 papers with code
A Reinforcement Learning Environment for Directed Quantum Circuit Synthesis
With recent advancements in quantum computing technology, optimizing quantum circuits and ensuring reliable quantum state preparation have become increasingly vital.
Quantum Denoising Diffusion Models
In recent years, machine learning models like DALL-E, Craiyon, and Stable Diffusion have gained significant attention for their ability to generate high-resolution images from concise descriptions.
Quantum Advantage Actor-Critic for Reinforcement Learning
In this work, we propose a novel quantum reinforcement learning approach that combines the Advantage Actor-Critic algorithm with variational quantum circuits by substituting parts of the classical components.
Challenges for Reinforcement Learning in Quantum Circuit Design
Quantum computing (QC) in the current NISQ era is still limited in size and precision.
Improving Parameter Training for VQEs by Sequential Hamiltonian Assembly
A central challenge in quantum machine learning is the design and training of parameterized quantum circuits (PQCs).
Towards Transfer Learning for Large-Scale Image Classification Using Annealing-based Quantum Boltzmann Machines
Quantum Transfer Learning (QTL) recently gained popularity as a hybrid quantum-classical approach for image classification tasks by efficiently combining the feature extraction capabilities of large Convolutional Neural Networks with the potential benefits of Quantum Machine Learning (QML).
Disentangling Quantum and Classical Contributions in Hybrid Quantum Machine Learning Architectures
We propose a novel hybrid architecture: instead of utilizing a pre-trained network for compression, we employ an autoencoder to derive a compressed version of the input data.
Applying QNLP to sentiment analysis in finance
As an application domain where the slightest qualitative improvements can yield immense value, finance is a promising candidate for early quantum advantage.
Weight Re-Mapping for Variational Quantum Algorithms
Inspired by the remarkable success of artificial neural networks across a broad spectrum of AI tasks, variational quantum circuits (VQCs) have recently seen an upsurge in quantum machine learning applications.
SEQUENT: Towards Traceable Quantum Machine Learning using Sequential Quantum Enhanced Training
However, as high-dimensional real-world applications are not yet feasible to be solved using purely quantum hardware, hybrid methods using both classical and quantum machine learning paradigms have been proposed.
NISQ-ready community detection based on separation-node identification
The analysis of network structure is essential to many scientific areas, ranging from biology to sociology.
Improving Convergence for Quantum Variational Classifiers using Weight Re-Mapping
In recent years, quantum machine learning has seen a substantial increase in the use of variational quantum circuits (VQCs).
Black Box Optimization Using QUBO and the Cross Entropy Method
A common approach to realising BBO is to learn a surrogate model which approximates the target black-box function which can then be solved via white-box optimization methods.
