Inspired by quantum theory and reinforcement learning, a new framework of learning in unknown probabilistic environment is proposed. Several simulated experiments are given; the results demonstrate the robustness of the new algorithm for some complex problems. Also we generalized the Grover algorithm to improve the rate of converging to an optimal path. in other words, the new generalized algorithm helps to increase the probability of selecting good actions with better weights\u27 adjustments. © 2013 World Scientific Publishing Company
With the advent of real-world quantum computing, the idea that parametrized quantum computations can...
We use reinforcement learning techniques to optimize the Quantum Approximate Optimization Algorithm ...
The balance between exploration and exploitation is a key problem for reinforcement learning methods...
Reinforcement learning is one of the fastest growing areas in machine learning, and has obtained gre...
Reinforcement learning is one of the fastest growing areas in machine learning, and has obtained gre...
Reinforcement Learning is at the core of a recent revolution in Artificial Intelligence. Simultaneou...
We present a full implementation and simulation of a novel quantum reinforcement learning (RL) metho...
In this paper, we present implementations of an annealing-based and a gate-based quantum computing a...
In this paper, we present implementations of an annealing-based and a gate-based quantum computing a...
In this paper, we present implementations of an annealing-based and a gate-based quantum computing a...
In this paper, we present implementations of an annealing-based and a gate-based quantum computing a...
In this paper, we present implementations of an annealing-based and a gate-based quantum computing a...
Machine learning techniques provide a remarkable tool for advancing scientific research, and this ar...
Reinforcement learning models require a choice rule for assigning probabilities to actions during le...
We use reinforcement learning techniques to optimize the Quantum Approximate Optimization Algorithm ...
With the advent of real-world quantum computing, the idea that parametrized quantum computations can...
We use reinforcement learning techniques to optimize the Quantum Approximate Optimization Algorithm ...
The balance between exploration and exploitation is a key problem for reinforcement learning methods...
Reinforcement learning is one of the fastest growing areas in machine learning, and has obtained gre...
Reinforcement learning is one of the fastest growing areas in machine learning, and has obtained gre...
Reinforcement Learning is at the core of a recent revolution in Artificial Intelligence. Simultaneou...
We present a full implementation and simulation of a novel quantum reinforcement learning (RL) metho...
In this paper, we present implementations of an annealing-based and a gate-based quantum computing a...
In this paper, we present implementations of an annealing-based and a gate-based quantum computing a...
In this paper, we present implementations of an annealing-based and a gate-based quantum computing a...
In this paper, we present implementations of an annealing-based and a gate-based quantum computing a...
In this paper, we present implementations of an annealing-based and a gate-based quantum computing a...
Machine learning techniques provide a remarkable tool for advancing scientific research, and this ar...
Reinforcement learning models require a choice rule for assigning probabilities to actions during le...
We use reinforcement learning techniques to optimize the Quantum Approximate Optimization Algorithm ...
With the advent of real-world quantum computing, the idea that parametrized quantum computations can...
We use reinforcement learning techniques to optimize the Quantum Approximate Optimization Algorithm ...
The balance between exploration and exploitation is a key problem for reinforcement learning methods...