<HashMap><database>biostudies-literature</database><scores/><additional><omics_type>Unknown</omics_type><volume>645(8080)</volume><submitter>Kalinin KP</submitter><pubmed_abstract>Artificial intelligence (AI) and combinatorial optimization drive applications across science and industry, but their increasing energy demands challenge the sustainability of digital computing. Most unconventional computing systems&lt;sup>1-7&lt;/sup> target either AI or optimization workloads and rely on frequent, energy-intensive digital conversions, limiting efficiency. These systems also face application-hardware mismatches, whether handling memory-bottlenecked neural models, mapping real-world optimization problems or contending with inherent analog noise. Here we introduce an analog optical computer (AOC) that combines analog electronics and three-dimensional optics to accelerate AI inference and combinatorial optimization in a single platform. This dual-domain capability is enabled by a </pubmed_abstract><journal>Nature</journal><pagination>354-361</pagination><full_dataset_link>https://www.ebi.ac.uk/biostudies/studies/S-EPMC12422976</full_dataset_link><repository>biostudies-literature</repository><pubmed_title>Analog optical computer for AI inference and combinatorial optimization.</pubmed_title><pmcid>PMC12422976</pmcid><pubmed_authors>Rowstron A</pubmed_authors><pubmed_authors>Clegg JH</pubmed_authors><pubmed_authors>Canakci B</pubmed_authors><pubmed_authors>Rajmohan S</pubmed_authors><pubmed_authors>Kremer H</pubmed_authors><pubmed_authors>Khedekar S</pubmed_authors><pubmed_authors>Kelly DJ</pubmed_authors><pubmed_authors>Falck F</pubmed_authors><pubmed_authors>Ruhle V</pubmed_authors><pubmed_authors>Ballani H</pubmed_authors><pubmed_authors>Gladrow J</pubmed_authors><pubmed_authors>Kalinin KP</pubmed_authors><pubmed_authors>O'Shea G</pubmed_authors><pubmed_authors>Berloff NG</pubmed_authors><pubmed_authors>Cletheroe D</pubmed_authors><pubmed_authors>Braine L</pubmed_authors><pubmed_authors>Gkantsidis C</pubmed_authors><pubmed_authors>Parmigiani F</pubmed_authors><pubmed_authors>Brennan G</pubmed_authors><pubmed_authors>Rahmani B</pubmed_authors><pubmed_authors>Chu J</pubmed_authors><pubmed_authors>Hansen M</pubmed_authors><pubmed_authors>Kleewein J</pubmed_authors><pubmed_authors>Pickup L</pubmed_authors></additional><is_claimable>false</is_claimable><name>Analog optical computer for AI inference and combinatorial optimization.</name><description>Artificial intelligence (AI) and combinatorial optimization drive applications across science and industry, but their increasing energy demands challenge the sustainability of digital computing. Most unconventional computing systems&lt;sup>1-7&lt;/sup> target either AI or optimization workloads and rely on frequent, energy-intensive digital conversions, limiting efficiency. These systems also face application-hardware mismatches, whether handling memory-bottlenecked neural models, mapping real-world optimization problems or contending with inherent analog noise. Here we introduce an analog optical computer (AOC) that combines analog electronics and three-dimensional optics to accelerate AI inference and combinatorial optimization in a single platform. This dual-domain capability is enabled by a </description><dates><release>2025-01-01T00:00:00Z</release><publication>2025 Sep</publication><modification>2026-06-03T02:21:27.56Z</modification><creation>2026-04-23T03:10:24.413Z</creation></dates><accession>S-EPMC12422976</accession><cross_references><pubmed>40903585</pubmed><doi>10.1038/s41586-025-09430-z</doi></cross_references></HashMap>