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Found 17 papers, 5 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.
REACT: Revealing Evolutionary Action Consequence Trajectories for Interpretable Reinforcement Learning
To enhance the interpretability of Reinforcement Learning (RL), we propose Revealing Evolutionary Action Consequence Trajectories (REACT).
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 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).
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.
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.
DIRECT: Learning from Sparse and Shifting Rewards using Discriminative Reward Co-Training
We propose discriminative reward co-training (DIRECT) as an extension to deep reinforcement learning algorithms.
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.
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.
Capturing Dependencies within Machine Learning via a Formal Process Model
We define a comprehensive SD process model for ML that encompasses most tasks and artifacts described in the literature in a consistent way.
VAST: Value Function Factorization with Variable Agent Sub-Teams
We evaluate VAST in three multi-agent domains and show that VAST can significantly outperform state-of-the-art VFF, when the number of agents is sufficiently large.
Towards Multi-Agent Reinforcement Learning using Quantum Boltzmann Machines
Reinforcement learning has driven impressive advances in machine learning.
BIG-bench Machine Learning
Multi-agent Reinforcement Learning
+2
Benchmarking Surrogate-Assisted Genetic Recommender Systems
The surrogate is used to recommend new items to the user, which are then evaluated according to the user's liking and subsequently removed from the search space.
