collaborators

5 papers

quant-ph2026

A hidden bottleneck in classical and quantum linear reservoir computing

Johannes Nokkala, Federico Centrone, Francesco Arzani

We identify a hidden bottleneck in the information processing capacity of linear reservoir computers. When the measured features evolve linearly in the reservoir and the output is…

quant-ph2026

Stability of Continuous Time Quantum Walks in Complex Networks

Adithya L J, Johannes Nokkala, Jyrki Piilo +1

We investigate the stability of continuous-time quantum walks (CTQW) across cycle, complete, star, Erdős-Rényi, small-world, and scale-free topologies under energy-based intrinsi…

quant-ph2025

Smarter Usage of Measurement Statistics Can Greatly Improve Continuous Variable Quantum Reservoir Computing

Markku Hahto, Johannes Nokkala

Quantum reservoir computing is a machine learning scheme in which a quantum system is used to perform information processing. A prospective approach to its physical realization is…

quant-ph2025

Transfer and routing of Gaussian states through quantum complex networks with and without community structure

Markku Hahto, Johannes Nokkala, Guillermo García-Pérez +2

The goal in quantum state transfer is to avoid the need to physically transport carriers of quantum information. This is achieved by using a suitably engineered Hamiltonian that in…

quant-ph2025

Retrieving past quantum features with deep hybrid classical-quantum reservoir computing

Johannes Nokkala, Gian Luca Giorgi, Roberta Zambrini

Machine learning techniques have achieved impressive results in recent years and the possibility of harnessing the power of quantum physics opens new promising avenues to speed up…