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Found 22 papers, 3 papers with code
Qandle: Accelerating State Vector Simulation Using Gate-Matrix Caching and Circuit Splitting
To address the computational complexity associated with state-vector simulation for quantum circuits, we propose a combination of advanced techniques to accelerate circuit execution.
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.
Aquarium: A Comprehensive Framework for Exploring Predator-Prey Dynamics through Multi-Agent Reinforcement Learning Algorithms
Recent advances in Multi-Agent Reinforcement Learning have prompted the modeling of intricate interactions between agents in simulated environments.
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.
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.
ClusterComm: Discrete Communication in Decentralized MARL using Internal Representation Clustering
In the realm of Multi-Agent Reinforcement Learning (MARL), prevailing approaches exhibit shortcomings in aligning with human learning, robustness, and scalability.
Challenges for Reinforcement Learning in Quantum Circuit Design
Quantum computing (QC) in the current NISQ era is still limited in size and precision.
Towards Efficient Quantum Anomaly Detection: One-Class SVMs using Variable Subsampling and Randomized Measurements
Quantum computing, with its potential to enhance various machine learning tasks, allows significant advancements in kernel calculation and model 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).
Multi-Agent Quantum Reinforcement Learning using Evolutionary Optimization
We showed that our Variational Quantum Circuit approaches perform significantly better compared to a neural network with a similar amount of trainable parameters.
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.
Efficient and Accurate Tree Detection from 3D Point Clouds through Paid Crowdsourcing
Accurate tree detection is of growing importance in applications such as urban planning, forest inventory, and environmental monitoring.
Improving Primate Sounds Classification using Binary Presorting for Deep Learning
In the field of wildlife observation and conservation, approaches involving machine learning on audio recordings are becoming increasingly popular.
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.
Learning to Participate through Trading of Reward Shares
Enabling autonomous agents to act cooperatively is an important step to integrate artificial intelligence in our daily lives.
Compression of GPS Trajectories using Autoencoders
The ubiquitous availability of mobile devices capable of location tracking led to a significant rise in the collection of GPS data.
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).
Constructing Organism Networks from Collaborative Self-Replicators
We introduce organism networks, which function like a single neural network but are composed of several neural particle networks; while each particle network fulfils the role of a single weight application within the organism network, it is also trained to self-replicate its own weights.
VoronoiPatches: Evaluating A New Data Augmentation Method
We primarily utilize non-linear recombination of information within an image, fragmenting and occluding small information patches.
Decentralized scheduling through an adaptive, trading-based multi-agent system
In this environment, reinforcement learning agents learn to trade successfully.
Multi-agent Reinforcement Learning
reinforcement-learning
+2
Quantifying Multimodality in World Models
In this work, we analyze existing and propose new metrics for the detection and quantification of multimodal uncertainty in RL based World Models.
The Hessigheim 3D (H3D) Benchmark on Semantic Segmentation of High-Resolution 3D Point Clouds and Textured Meshes from UAV LiDAR and Multi-View-Stereo
Automated semantic segmentation and object detection are of great importance in geospatial data analysis.
