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World Congress 2026 Europe • Jul 10, 2026 • Session details

Photonic Computing: Programming a New Class of AI Accelerators (incl. Live Coding)

Christoph Lohrmann , Utz Bacher

Photonic computing bypasses GPU constraints by using light to process operations natively, cutting model sizes in half. Watch us live code standard PyTorch models directly to optical chips.

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#1 about 2 min

Transitioning from quantum to photonic computing paradigms

The strategic shift from quantum processing experiments to designing deployable optical accelerators solves early manufacturing difficulties.

#2 about 2 min

Physical constraints of classical AI hardware scaling

Massive datacenter energy consumption reveals how conventional CMOS transistors struggle against escalating data transfer burdens.

#3 about 1 min

Modulating light through lithium niobate photonic crystals

Running low voltages directly across wafer structures successfully alters light refractive traits without generating friction heat.

#4 about 2 min

Encoding analog data across multidimensional light states

Mapping complex variables directly against light wavelengths guarantees instantaneous math updates independent of clock tick rhythms.

#5 about 2 min

Hardware design of native processing unit integrations

Combining PCIe form factors with translation circuits connects cutting-edge optical silicon directly to standard server operating systems.

#6 about 1 min

Eliminating complex data center cooling footprint limits

Swapping kilowatt chipsets for milliwatt lasers permits massive computational cluster scaling devoid of specialized cooling dependencies.

#7 about 2 min

Validating standard neural networks on photonic silicon

Confirming traditional visual routing topologies over light pathways validates practical image recognition without restructuring conventional model theories.

#8 about 4 min

Deploying non-linear mathematical operations natively through physics

Relying on physical optics instead of digital estimation reduces necessary AI model parameter structures when performing Fourier analysis.

#9 about 2 min

Compiling PyTorch environments for advanced time forecasting

Adapting diffusion logic natively from standard ML frameworks reduces dependency on niche hardware interaction languages.

#10 about 4 min

Mapping network convolutions through native Fourier domains

Keeping signal throughput insulated inside continuous analog bounds avoids sluggish performance penalties inflicted by conversion hops.

#11 about 3 min

Leveraging inherent stateful components for recurrent architectures

Exploring naturally retaining wave oscillators supplies unique passive memory blocks ideal for iterative state machine tasks.

#12 about 3 min

Exposing native functionality via the MLIR compiler

Hooking low-level algorithms into standardized infrastructure bridges abstract tensor declarations with the actual linear accelerator hardware.

#13 about 3 min

Demonstrating live MNIST digit mapping through Python

Initializing pre-built forward propagation modules directly from visual programming interfaces applies native hardware sorting immediately.

#14 about 2 min

Hardware availability through datacenter partners and SDKs

Launching specialized silicon clusters onto publicly available platforms invites remote engineering experimentation scaling parallel workflow requests.

#15 about 3 min

Resolving signal noise variables across hardware converters

Pairing finely tuned conversion arrays manages expected accuracy offsets encountered when transitioning across the physical analog barriers.

Matching moments

2:42 min

Dissecting artificial intelligence layers from compute to applications

Christian Nagel Christian Nagel +3 · WWC Europe 2026

3:27 min

Exploring physical limits and alternatives to silicon chips

Stephen Jones · Coffee With Developers

2:21 min

Transitioning to accelerated computing for application developers

Ankit Patel Ankit Patel · WWC 2024

1:19 min

Accelerating artificial intelligence algorithms using quantum mathematical superposition

Alexander Wallner Alexander Wallner +3 · WWC 2024

8:17 min

Practical applications and hybrid solutions for quantum computing advantage

Alexander Glätzle Alexander Glätzle +3 · WWC 2025

4:06 min

Combining quantum, neural, and classical computing paradigms

Stephen Jones · Coffee With Developers

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