6 papers
The Binomial Channel: On Capacity, Optimal Inputs, and Beta-Binomial Approximation
Antonino Favano, Mohammadamin Baniasadi, Ian Zieder +2
We study the binomial channel with input alphabet and output alphabet . We investigate its capacity and the structure of the capacity-achieving input and outp…
JGRA: Jacobian Geometry Robustness Assessment in NISQ Noise-Aware Quantum Neural Networks
Gianluca Scanu, Luca Barletta, Stefano Rini
The NISQ era places stringent constraints on quantum computation, where noise and decoherence fundamentally limit performance. In classical deep learning, model robustness and resi…
Best-First Ordered Statistics Decoding of Quantum LDPC Codes
Michele Banfi, Marco Ferrari, Antonino Favano +2
Belief Propagation (BP) followed by Ordered Statistics Decoding (OSD) has emerged as the gold standard for decoding quantum low-density parity-check (QLDPC) codes. Recent advanceme…
An Improved Lower Bound on Support Size of Capacity-Achieving Inputs for the Binomial Channel: Extended version
Mohammadamin Baniasadi, Luca Barletta, Alex Dytso
We study the binomial channel and the structure of its capacity-achieving input and output distributions. It is known that the capacity-achieving input distribution is discrete and…
PolarZero: A Reinforcement Learning Approach for Low-Complexity Polarization Kernel Design
Yi-Ting Hong, Stefano Rini, Luca Barletta
Polar codes with large kernels can achieve improved error exponents but are challenging to design with low decoding complexity. This work investigates kernel construction under rec…
Reinforcement Learning-Aided Design of Efficient Polarization Kernels
Yi-Ting Hong, Stefano Rini, Luca Barletta
Polar codes with large kernels achieve optimal error exponents but are difficult to construct when low decoding complexity is also required. We address this challenge under recursi…