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20182026
most citedTensor network approaches for learning non-linear dynamical laws

11 citations · 36 across the 17 of their papers we have counts for

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quant-ph2025

A measurement-driven quantum algorithm for SAT: Performance guarantees via spectral gaps and measurement parallelization

Franz J. Schreiber, Maximilian J. Kramer, Alexander Nietner +1

The Boolean satisfiability problem (SAT) is of central importance in both theory and practice. Yet, most provable guarantees for quantum algorithms rely exclusively on Grover-type…

quant-ph2025

Clifford testing: algorithms and lower bounds

Marcel Hinsche, Zongbo Bao, Philippe van Dordrecht +3

We consider the problem of Clifford testing, which asks whether a black-box -qubit unitary is a Clifford unitary or at least -far from every Clifford unitary. We gi…

quant-ph20253 cited

Mind the gaps: The fraught road to quantum advantage

Jens Eisert, John Preskill

Quantum computing is advancing rapidly, yet substantial gaps separate today's noisy intermediate-scale quantum (NISQ) devices from tomorrow's fault-tolerant application-scale quant…

quant-ph2025

Phase shadow: A noise-tolerant path to global quantum property estimation

Qingyue Zhang, Dayue Qin, Zhou You +3

Measuring global quantum properties-such as the fidelity to complex multipartite states-is both an essential and experimentally challenging task. Classical shadow estimation offers…

quant-ph20257 cited

In the shadow of the Hadamard test: Using the garbage state for good and further modifications

Paul K. Faehrmann, Jens Eisert, Richard Kueng

The Hadamard test is naturally suited for the intermediate regime between the current era of noisy quantum devices and complete fault tolerance. Its applications use measurements o…

quant-ph2025

The abelian state hidden subgroup problem: Learning stabilizer groups and beyond

Marcel Hinsche, Jens Eisert, Jose Carrasco

Identifying the symmetry properties of quantum states is a central theme in quantum information theory and quantum many-body physics. In this work, we investigate quantum learning…