This paper trains a simulated quantum reinforcement learning model with different levels of mid-circuit measurements, and investigates its performance in the cartpole environment, finding that the used quantum reinforcement circuit has a very variable performance, which causes the performance impact of the mid-circuit measurements to be limited if present.
Training quantum neural networks (QNNs) on near-term hardware remains hampered by two compounding difficulties: the exponential vanishing of gradient variance known as the barren plateau, and the $\mathcal{O}(L \cdot 2^n)$ time and memory cost of differentiating through an $n$-qubit, $L$-layer circuit. We propose RLQ-G...
This paper repurposes uniformly controlled rotations from static state preparation and static data encoding into a state-encoding interface for quantum neural networks, elevating the process of integrating classical states into quantum neural networks to an independent method-ological layer and achieves a more stable p...
Jun-Chen Han, Feng-Tao Xiang, Hao Shi et al.· Chinese Physics B· 0 citations
One of the most important initial steps in quantum computing is high-fidelity quantum state preparation, because errors in it can affect the final computational result. Therefore, one of the major challenges is to design a quantum circuit that can achieve the desired target state accurately with a smaller number of gat...
Om Bhamare, Aryan Jadhav, Manas Shinde et al.· 0 citations
A new methodology is developed for quantum machine learning which enables variational quantum circuits to learn linear and non-linear solution operators to differential equations, leveraging the quantum universal approximation theorem.
A reinforcement learning framework that embeds a deterministic Commutation-and-Reduction (CR) algorithm directly into the training environment, enabling the agent to focus its learning capacity on the non-trivial optimizations where reinforcement learning adds real value.
Khoa Dang Tao, Sumin Jin, M. Raza et al.· 0 citations
This work studies the simplified setting of reinforcement learning with a uniform generative model in which uniformly random state-action samples are available, and provides finite sample guarantees for classical kernelized Fitted Q-Iteration, with classical kernels designed to match the inductive bias of particular pa...
Pablo Rodriguez-Grasa, Sofiène Jerbi, Mikel Sanz et al.· 0 citations
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