Unbounded randomness from uncharacterized sources
Marco Avesani, Hamid Tebyanian, Paolo Villoresi, Giuseppe Vallone

TL;DR
This paper introduces a novel Source-Device-Independent protocol utilizing POVMs to certify an unbounded amount of randomness from fixed-dimensional quantum systems, supported by theoretical bounds and experimental validation.
Contribution
It proposes a new POVM-based protocol that can arbitrarily increase certified randomness regardless of system dimension, with analytical bounds and experimental demonstration.
Findings
Derived tight lower bounds on quantum conditional min-entropy using POVM structure.
Experimental validation with a photonic setup employing polarization-encoded qubits and POVMs.
Achieved certification of randomness with POVMs up to 6 outcomes.
Abstract
Randomness is a central feature of quantum mechanics and an invaluable resource for both classical and quantum technologies. Commonly, in Device-Independent and Semi-Device-Independent scenarios, randomness is certified using projective measurements and the amount of certified randomness is bounded by the dimension of the measured quantum system. In this work, we propose a new Source-Device-Independent protocol, based on Positive Operator Valued Measurement (POVM), which can arbitrarily increase the number of certified bits for any fixed dimension. A tight lower-bound on the quantum conditional min-entropy is derived using only the POVM structure and the experimental expectation values, taking into account the quantum side-information. For symmetrical POVM measurements on the Bloch sphere we have derived closed-form analytical bounds. Finally, we experimentally demonstrate our method…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsQuantum Information and Cryptography · Neural Networks and Reservoir Computing · Random lasers and scattering media
