10 citations · 10 across the 6 of their papers we have counts for
16 papers · 1 filter
Opportunities and limitations of explaining quantum machine learning
Elies Gil-Fuster, Jonas R. Naujoks, Grégoire Montavon +3
A common trait of many machine learning models is that it is often difficult to understand and explain what caused the model to produce the given output. While the explainability o…
An unconditional distribution learning advantage with shallow quantum circuits
N. Pirnay, S. Jerbi, J. -P. Seifert +1
One of the core challenges of research in quantum computing is concerned with the question whether quantum advantages can be found for near-term quantum circuits that have implicat…
Learning quantum states of continuous variable systems
Francesco Anna Mele, Antonio Anna Mele, Lennart Bittel +5
Quantum state tomography, aimed at deriving a classical description of an unknown state from measurement data, is a fundamental task in quantum physics. In this work, we analyse th…
Simulating quantum chaos without chaos
Andi Gu, Yihui Quek, Susanne Yelin +2
Quantum chaos is a quantum many-body phenomenon that is associated with a number of intricate properties, such as level repulsion in energy spectra or distinct scalings of out-of-t…
Hardware-Tailored Diagonalization Circuits
Daniel Miller, Laurin E. Fischer, Kyano Levi +5
A central building block of many quantum algorithms is the diagonalization of Pauli operators. Although it is always possible to construct a quantum circuit that simultaneously dia…
An introduction to infinite projected entangled-pair state methods for variational ground state simulations using automatic differentiation
Jan Naumann, Erik Lennart Weerda, Matteo Rizzi +2
Tensor networks capture large classes of ground states of phases of quantum matter faithfully and efficiently. Their manipulation and contraction has remained a challenge over the…