Invited - Optical single-pixel sensing for Nonlinear Ising Machines

Photonic Ising machines leverage large-scale parallelism for solving large combinatorial problems, yet multiple minima hamper Metropolis-based algorithm. A double single-pixel detection approach enables adiabatic energetic transitions from nonlocal to local Hamiltonians, finding the ground state of complex landscapes.

Authors

Institutions

Publication Details

Journal
EPJ Web of Conferences
Published
2025-01-01
DOI
https://doi.org/10.1051/epjconf/202533512002
Primary Topic
Quantum Information and Cryptography
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Invited - Optical single-pixel sensing for Nonlinear Ising Machines

Luke Peters, Marco Peccianti, Vittorio Cecconi, Juan Sebastian Totero Gongora et al.
EPJ Web of Conferences
Quantum Information and Cryptography
article

Invited - Optical single-pixel sensing for Nonlinear Ising Machines

Luke Peters, Marco Peccianti, Vittorio Cecconi, Juan Sebastian Totero Gongora, Luana Olivieri, Alessia Pasquazi, Andrew Cooper
article en

Abstract

Photonic Ising machines leverage large-scale parallelism for solving large combinatorial problems, yet multiple minima hamper Metropolis-based algorithm. A double single-pixel detection approach enables adiabatic energetic transitions from nonlocal to local Hamiltonians, finding the ground state of complex landscapes.

EPJ Web of ConferencesVol. 335
Loughborough University (GB)
Openalex Percentile: Top 87%
Quantum Information and Cryptography
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.