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Found 9 papers, 4 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.
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.
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.
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.
CROP: Towards Distributional-Shift Robust Reinforcement Learning using Compact Reshaped Observation Processing
Current state-of-the-art approaches for generalization apply data augmentation techniques to increase the diversity of training data.
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.
VoronoiPatches: Evaluating A New Data Augmentation Method
We primarily utilize non-linear recombination of information within an image, fragmenting and occluding small information patches.
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.
Case-Based Inverse Reinforcement Learning Using Temporal Coherence
As a prior, we assume that the higher-level strategy is to reach an unknown target state area, which we hypothesize is a valid prior for many domains in Reinforcement Learning.
