Search Results for author:
Found 15 papers, 7 papers with code
Guided-SPSA: Simultaneous Perturbation Stochastic Approximation assisted by the Parameter Shift Rule
The computational complexity, in terms of the number of circuit evaluations required for gradient estimation by the parameter-shift rule, scales linearly with the number of parameters in VQCs.
Warm-Start Variational Quantum Policy Iteration
This objective can be achieved using policy iteration, which requires to solve a typically large linear system of equations.
Comprehensive Library of Variational LSE Solvers
Linear systems of equations can be found in various mathematical domains, as well as in the field of machine learning.
Qiskit-Torch-Module: Fast Prototyping of Quantum Neural Networks
Quantum computer simulation software is an integral tool for the research efforts in the quantum computing community.
C-MCTS: Safe Planning with Monte Carlo Tree Search
We propose Constrained MCTS (C-MCTS), which estimates cost using a safety critic that is trained with Temporal Difference learning in an offline phase prior to agent deployment.
BCQQ: Batch-Constraint Quantum Q-Learning with Cyclic Data Re-uploading
Deep reinforcement learning (DRL) often requires a large number of data and environment interactions, making the training process time-consuming.
An Empirical Comparison of Optimizers for Quantum Machine Learning with SPSA-based Gradients
We introduce a novel approach that uses the approximated gradient from SPSA in combination with state-of-the-art gradient-based classical optimizers.
Quantum Natural Policy Gradients: Towards Sample-Efficient Reinforcement Learning
Reinforcement learning is a growing field in AI with a lot of potential.
A Survey on Quantum Reinforcement Learning
Quantum reinforcement learning is an emerging field at the intersection of quantum computing and machine learning.
Efficient Beam Search for Initial Access Using Collaborative Filtering
Our algorithm is based on a recommender system that associates groups (i. e., UEs) and preferences (i. e., beams from a codebook) based on a training data set.
Driver Dojo: A Benchmark for Generalizable Reinforcement Learning for Autonomous Driving
Reinforcement learning (RL) has shown to reach super human-level performance across a wide range of tasks.
Incremental Data-Uploading for Full-Quantum Classification
Encoding high dimensional data into a quantum circuit for a NISQ device without any loss of information is not trivial and brings a lot of challenges.
How to Learn from Risk: Explicit Risk-Utility Reinforcement Learning for Efficient and Safe Driving Strategies
This paper proposes SafeDQN, which allows to make the behavior of autonomous vehicles safe and interpretable while still being efficient.
An Introduction to Multi-Agent Reinforcement Learning and Review of its Application to Autonomous Mobility
Multi-Agent Reinforcement Learning (MARL) is a research field that aims to find optimal solutions for multiple agents that interact with each other.
Uncovering Instabilities in Variational-Quantum Deep Q-Networks
In this work, we examine a class of hybrid quantum-classical RL algorithms that we collectively refer to as variational quantum deep Q-networks (VQ-DQN).
