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20212026
most citedTraining variational quantum algorithms is NP-hard

428 citations · 559 across the 22 of their papers we have counts for

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Showing 2026Show all

5 papers · 1 filter

quant-ph2026

Infinite magic and the emergence of the continuum

Leo Shaposhnik, Lorenzo Leone, Lennart Bittel +3

Which features of a quantum state are needed for a relativistic quantum field theory to emerge from a lattice system? We address this question by establishing a quantitative relati…

quant-ph2026

Adaptivity is all you need: Optimal stabilizer learning using just single-copy measurements

L. Bittel, J. Eisert, W. Gong +2

Stabilizer states are central to quantum computing, underlying quantum error correction, benchmarking, and efficient classical simulation. Yet their learnability exhibits a strikin…

quant-ph2026

Efficient learning of Clifford disentanglers and typical -doped unitaries with exponentially more gates

Gerard Aguilar, Sofiene Jerbi, Jens Eisert +1

Highly entangled and highly non-stabilizer quantum states need not be hard to learn. We give efficient algorithms for testing and recovering hidden tensor-product structure in unkn…

quant-ph2026

Fermionic entropy: an efficiently measurable strong monotone for non-Gaussianity

Lorenzo Leone, Lennart Bittel

Fermionic Gaussian states form a central class of classically tractable quantum states, while fermionic non-Gaussianity provides the resource required to go beyond free-fermion dyn…

quant-ph2026

Approximate sampling from decoded quantum interferometry via Markov chain Monte Carlo methods

Elies Gil-Fuster, Matan Ninio, Lennart Bittel +4

Optimization problems are among the leading candidates for industrially relevant quantum advantage. Decoded quantum interferometry (DQI) has been proposed to tackle approximate opt…