We introduce a framework for distributed quantum inference under communication constraints. In our model, $m$ distributed nodes each receive one copy of an unknown $d$-dimensional quantum state $\rho$, before communicating via a constrained one-way communication channel with a central node, which aims to infer some pro...
Kenny Chen, Mina Doosti, R. Sweke et al.· 0 citations
It is proved that a resource-constrained learner cannot gain any advantage through classical interaction with an untrusted prover, and it is shown that for the vast majority of testing and learning problems, a memory-constrained quantum algorithm cannot overcome its limitations via classical communication with a memory...
Matthias C. Caro, J. Eisert, M. Hinsche et al.· arXiv.org· 4 citations
In recent years, the utility of parameterized quantum circuits as function approximators has been widely studied. In the context of reinforcement learning, this approach has led to variational quantum algorithms such as quantum Q-learning. While these methods show promising empirical results, and can provide provable a...
Pablo Rodriguez-Grasa, Sofiène Jerbi, Mikel Sanz et al.· 0 citations
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