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- ⚛️🦾 Modular Quantum Computing, Optical Neuromorphic Computing, and Why Most AI Chips Aren’t Cost-Effective
⚛️🦾 Modular Quantum Computing, Optical Neuromorphic Computing, and Why Most AI Chips Aren’t Cost-Effective
A Newsletter for Computing Geeks, Entrepreneurs, and STEM Graduates
Benjamin Wolba
November 29, 2024
In partnership with
Welinq: Shaping the Future of Modular Quantum Computing
Quantum computing is getting a lot of attention because of its revolutionary approach to processing information. However, the challenge in realizing its potential lies not just in computation but in scaling.
Integrating more qubits into a single quantum processor gets increasingly difficult beyond a few thousand qubits.
But what if you could connect several quantum processors into one large-scale, multicore quantum computer with enough qubits to solve practical problems?
Welinq was founded in 2022 by Tom Darras, Julien Laurat, and Eleni Diamanti to build highly efficient quantum links that will allow connecting quantum processors end-to-end, paving the way to scalable quantum computing. In 2023, Welinq raised a €5M round led by Quantonation and joined by Runa Capital.
Future of Computing News
🦾 First ever CMOS transistor fully optimized for cryogenic conditions released by SemiQon
🦾 Has Google's Tensor project failed? Would future Pixels be better off returning to Snapdragon? (AndroidAuthority)
🦾 China Hawks are Manufacturing an AI Arms Race (Lesswrong)
🦾 HBM Options Increase As AI Demand Soars: But manufacturing reliable 3D DRAM stacks with good yield is complex and costly (Semiconductor Engineering)
⚡️🦾 Lightmatter turns to GlobalFoundries to bring photonics interconnect to market in 2025 (DCD)
⚡️ Micro-LEDs for neuromorphic computing: Researchers successfully test micro-LED technology for tomorrow's artificial intelligence (TU Braunschweig)
💽💎 Diamond optical discs could store data for millions of years: Researchers used lasers to make space for files on an atomic level (Popular Science)
⚡️ Q.ANT Launches Photonic Processor (Optica)
⚡️ How superior is computing with light? (All-About-Industries)
🤖 Introducing the Model Context Protocol (Anthropic)
🤖 OLMo 2: The best fully open language model to date (Ai2)
Funding News
🦾 Dynanic, a Brno, Czech Republic-based chip tech startup, raised €550K in Pre-Seed funding (Finsmes)
We’re excited that Keith Witek, COO of Tenstorrent, will be joining us in Munich at the 𝗙𝘂𝘁𝘂𝗿𝗲 𝗼𝗳 𝗖𝗼𝗺𝗽𝘂𝘁𝗶𝗻𝗴 𝗖𝗼𝗻𝗳𝗲𝗿𝗲𝗻𝗰𝗲 to deliver a keynote on "Open vs Closed - The Beauty of Open Source and RISC-V as the Next CPU Solution of Choice"
The conference will feature industry-leading keynotes and 30 start-up pitches across fields like classical semiconductors, photonics, and quantum computing, addressing today's most pressing challenges in advancing AI and computing.
Get your ticket here:
Good Reads
As researcher and ARC prize co-founder François Chollet provocatively argues, the intense focus on scaling LLMs may have actually “set back progress towards AGI by quite a few years, probably like 5-10 years.”
“o1 reasoners are the most exciting models since the original GPT-4. They prove what I predicted earlier this year in my AI Search: The Bitter-er Lesson paper: we can get models to think for longer instead of building bigger models.
It's too bad they suck on problems you should care about.”
“Carefully reading the whitepaper reveals that performance mode refers to the regime where the entire model can be stored in on-chip SRAM. Those of you who remember my article on Groq probably know where this is going. If you want to fit a large model entirely in on-chip SRAM, you’re going to need a lot of chips, which makes your infrastructure much more expensive.”
“Nonlinear optical responses in two-dimensional (2D) materials can build free-space optical neuromorphic computing systems. Ensuring the high performance and the tunability of the system is essential to encode diverse functions.”
“As per a study by the International Data Corporation (IDC), the global data sphere is projected to grow from 33 zettabytes (ZB) in 2018 to 175 ZB by 2025. … In the search for better solutions than traditional silicon-based computing and data storage systems, one of the potential answers may lie within the building blocks of the genetic code: DNA molecules.”
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