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Found 7 papers, 2 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.
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.
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.
A Survey on Quantum Reinforcement Learning
Quantum reinforcement learning is an emerging field at the intersection of quantum computing and machine learning.
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.
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).
