Pdf Optimizing Quantum Variational Circuits With Deep Reinforcement

Pdf Optimizing Quantum Variational Circuits With Deep Reinforcement In this work, we evaluate the potential of contemporary methods in deep reinforcement learning to augment gradient based optimization routines in quantum variational circuits. In this work, we evaluate the potential of contemporary methods in deep reinforcement learning to augment gradient based optimization routines in quantum variational circuits.

Variational Quantum Circuits And Deep Reinforcement Learning Deepai We showcase the performance of our algorithm on the problem of estimating the ground state energy of lithium hydride (lih) in various config urations. in this well known benchmark problem, we achieve chemical accuracy and state of the art results in terms of circuit depth. Thus, it is necessary to design feasible quantum algorithms for quantum machine learning for noisy intermediate scale quantum (nisq) devices. this work explores variational quantum circuits for deep reinforcement learning. Download the full pdf of optimizing quantum variational circuits with deep reinforcement. includes comprehensive summary, implementation details, and key takeaways.owen lockwood. This work reshapes classical deep reinforcement learning algorithms like experience replay and target network into a representation of variational quantum circuits, and uses a quantum information encoding scheme to reduce the number of model parameters compared to classical neural networks.

Optimizing Vehicular Networks With Variational Quantum Circuits Based Download the full pdf of optimizing quantum variational circuits with deep reinforcement. includes comprehensive summary, implementation details, and key takeaways.owen lockwood. This work reshapes classical deep reinforcement learning algorithms like experience replay and target network into a representation of variational quantum circuits, and uses a quantum information encoding scheme to reduce the number of model parameters compared to classical neural networks. We find numerically that shallow quantum circuits acting on very few qubits are competitive with deep neural networks on well established benchmarking environments. We present our techniques for encoding classical data for a quantum variational circuit, we further explore pure and hybrid quantum algorithms for dqn and double dqn. In this study, a variational quantum circuit (vqc) was proposed using amplitude encoding to overcome the limited qubit number barrier and use the advantages of quantum computing more efficiently.