Reconquering Bell sampling on qudits: stabilizer learning and testing, quantum pseudorandomness bounds, and more
Read the original on arXiv Machine Learning →The Flow has not summarised this story yet — read it at arXiv Machine Learning.
The Flow has not summarised this story yet — read it at arXiv Machine Learning.
arXiv:2607. 02444v1 Announce Type: cross Abstract: We study stabilizer state testing and learning with limited coherent quantum memory.
We study stabilizer state testing and learning with limited coherent quantum memory. Here an algorithm sequentially receives copies of an unknown $n$-qubit state, but may keep only $k$ qubits of coherent quantum memory between measurements.
arXiv:2606. 12211v1 Announce Type: cross Abstract: A central principle in quantum machine learning is that an ansatz should be expressive enough to represent the quantum data of interest.
arXiv:2606. 02655v1 Announce Type: cross Abstract: External regret certifies stability only against replacing one's behavior by a fixed alternative.
arXiv:2604. 22627v2 Announce Type: replace-cross Abstract: Joint measurements on multiple copies of a quantum state provide access to nonlinear observables such as $\operatorname{tr}(\rho^t)$, but whether replica number marks a sharp information-theoretic resource boundary has remained unclear.
arXiv:2606. 25170v1 Announce Type: cross Abstract: We study PAC learning in tabular discounted Markov decision processes with exogenous i.