5 papers
Comment on "Spin-1/2 Kagome Heisenberg Antiferromagnet: Machine Learning Discovery of the Spinon Pair-Density-Wave Ground State"
Helia Kamal, Dominik Kufel, DinhDuy Vu +2
A recent article [Phys. Rev. X 15, 011047 (2025)] utilizes group-equivariant convolutional neural networks to study the ground state of the kagome Heisenberg antiferromagnet. On th…
Hardness of recognizing phases of matter
Thomas Schuster, Dominik Kufel, Norman Y. Yao +1
We prove that recognizing the phase of matter of an unknown quantum state is quantum computationally hard. More specifically, we show that the quantum computational time of any pha…
Optimizing the dynamical preparation of quantum spin lakes on the ruby lattice
DinhDuy Vu, Dominik S. Kufel, Jack Kemp +3
Quantum spin liquids are elusive long-range entangled states. Motivated by experiments in Rydberg quantum simulators, recent excitement has centered on the possibility of dynamical…
Approximately-symmetric neural networks for quantum spin liquids
Dominik S. Kufel, Jack Kemp, DinhDuy Vu +3
We propose and analyze a family of approximately-symmetric neural networks for quantum spin liquid problems. These tailored architectures are parameter-efficient, scalable, and sig…
Spin squeezing in an ensemble of nitrogen-vacancy centers in diamond
Weijie Wu, Emily J. Davis, Lillian B. Hughes +10
Spin squeezed states provide a seminal example of how the structure of quantum mechanical correlations can be controlled to produce metrologically useful entanglement. Such squeeze…