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20162026
most citedThe randomized measurement toolbox

374 citations · 708 across the 33 of their papers we have counts for

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Showing 2025 · quant-phShow all

10 papers · 2 filters

quant-ph2025

A Rigorous Quantum Framework for Inequality-Constrained and Multi-Objective Binary Optimization: Quadratic Cost Functions and Empirical Evaluations

Sebastian Egginger, Kristina Kirova, Sonja Bruckner +2

The prospect of quantum solutions for complicated optimization problems is contingent on mapping the original problem onto a tractable quantum energy landscape, e.g. an Ising-type…

quant-ph2025

A Rigorous Quantum Framework for Inequality-Constrained and Multi-Objective Binary Optimization

Sebastian Egginger, Kristina Kirova, Sonja Bruckner +2

Encoding combinatorial optimization problems into physically meaningful Hamiltonians with tractable energy landscapes forms the foundation of quantum optimization. Numerous works h…

quant-ph2025

An infinite hierarchy of multi-copy quantum learning tasks

Jan Nöller, Viet T. Tran, Mariami Gachechiladze +1

Learning properties of quantum states from measurement data is a fundamental challenge in quantum information. The sample complexity of such tasks depends crucially on the measurem…

quant-ph2025

The Sound of Entanglement

Enar de Dios Rodríguez, Philipp Haslinger, Johannes Kofler +5

The advent of quantum physics has revolutionized our understanding of the universe, replacing the deterministic framework of classical physics with a paradigm dominated by intrinsi…

quant-ph2025

One, Two, Three: One empirical evaluation of a two-copy shadow tomography scheme with triple efficiency

Viet T. Tran, Richard Kueng

Shadow tomography protocols have recently emerged as powerful tools for efficient quantum state learning, aiming to reconstruct expectation values of observables with fewer resourc…

quant-ph2025

Fast quantum measurement tomography with optimal error bounds

Leonardo Zambrano, Sergi Ramos-Calderer, Richard Kueng

We present a two-step protocol for quantum measurement tomography that is light on classical co-processing cost and still achieves optimal sample complexity. Given measurement data…