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From the 1 of 13 linked papers with an AI index.

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13 papers

quant-ph2026

Observable Estimation in the Absence of Classical Verification

Samantha V. Barron, Bradley Mitchell, Vinay Tripathi +45

The paper proposes a framework to independently validate observable estimates from quantum computers when classical benchmarks are unavailable, using experiments such as the operat…

quant-ph2026

Repetition-code-based readout error detection and correction across hardware platforms and generations

Csaba Czabán, Orsolya Kálmán, Sergey N. Filippov +1

Readout errors are one of the dominant sources of noise in current quantum processors, limiting both expectation-value estimation and sampling-based applications. Since they affect…

quant-ph2026

Clifford Volume and Free Fermion Volume: Complementary Scalable Benchmarks for Quantum Computers

Attila Portik, Orsolya Kálmán, Thomas Monz +1

As quantum computing advances toward the late-NISQ and early fault-tolerant eras, scalable and platform-independent benchmarks are essential for quantifying computational capacity…

quant-ph2026

Classical simulation of free-fermionic dynamics and quantum chemistry with magic input

Changhun Oh, Michał Oszmaniec, Oliver Reardon-Smith +1

Establishing the precise computational boundary between classically tractable fermionic systems and those capable of genuine quantum advantage is a central challenge in quantum sim…

quant-ph2026

General framework for anticoncentration and linear cross-entropy benchmarking in photonic quantum advantage experiments

Zoltán Kolarovszki, Ágoston Kaposi, Zoltán Zimborás +1

Photonic architectures are one of the leading platforms for demonstrating quantum computational advantage, with Boson Sampling and Gaussian Boson Sampling as the primary schemes. Y…

quant-ph2026

Generative modeling with Gaussian Boson Sampling: classically trainable Bosonic Born Machines

Zoltán Kolarovszki, Bence Bakó, Michał Oszmaniec +2

Quantum generative modeling has emerged as a promising application of quantum computers, aiming to model complex probability distributions beyond the reach of classical methods. In…