How SemiQon Is Bringing Quantum Computing to Silicon Chips: A Deep Dive With Janne Lehtinen*

Over the past decades, the semiconductor industry has proven that it can manufacture transistors and microprocessors at scale. The challenge now is how to make it work for building large-scale, error-free quantum processors. 

We had the pleasure of interviewing Janne Lehtinen, chief science officer and co-founder of SemiQon, about why silicon chips offer a great technology platform not just for microprocessors but also for quantum processors, why placing control electronics on the same silicon chip as the qubits is a game changer but also tricky, and why collaborating with researchers is not just good for testing more devices:

*Sponsored post—we greatly appreciate the support from SemiQon

Future of Computing News

🦾 TSMC plans semiconductor factory in Dresden: Chancellor Scholz symbolically breaks ground (Die Sachsen)

🦾 AMD buying server maker ZT Systems for $4.9 billion as chipmakers strengthen AI capabilities (ABC News)

🤖 Mistral-NeMo-Minitron 8B: NVIDIA Releases Small Language Model With State-of-the-Art Accuracy (Nvidia)

🧬 These 'living computers' are made from human neurons — and you can rent one for $500 a month (Live Science)

📈 Arm Stock Is Pricier Than Nvidia’s. It Shouldn’t Be (The Information)

⚛️ 99% fidelity of two-qubit gates by Diraq: Assessment of the errors of high-fidelity two-qubit gates in silicon quantum dots (Nature)

⚛️ Pushing the Boundaries of Quantum Error Correction with an Inside Look at IBM’s Latest Success (The Quantum Insider)

Funding News

🤖⚙️ BeyondMath Raises $8.5 Million to Revolutionize Physics-Based Engineering with Groundbreaking AI-Powered Simulation Platform (Yahoo! Finance)

🤖⚙️ Trace Machina Secures $4.7 Million Seed Round, Launches to Develop Simulation Infrastructure for Physical-World AI (AIthority)

🥶 Zurich-based Apheros secures €1.65 million to cool down data centers via metal foams (EU Startups)

🔐 Fabric Cryptography raises $33m to unleash advanced cryptography with a new kind of chip (GlobeNewswire)

🦾 Local investors double Shanghai chip fund to $2B (Mobile World Live)

LUBIS EDA: Shaping the Future of Verification for Microchips

LUBIS EDA develops software for verifying microchips to eliminate bugs and ensure flawless performance. Founded by Tobias Ludwig, Max Birtel, and Michael Schwarz in 2021, their approach enhances the reliability of microchips and streamlines the entire development process before the first transistor is etched on a silicon wafer. LUBIS EDA has gone through the EIC accelerator program and is now part of the most recent Intel Ignite cohort in Europe. 

Lakera: Shaping the Future of Securing AI Models

Lakera offers the leading real-time security platform for generative AI applications. Like a protective layer, the platform shields AI models against threats like data loss or toxic outputs without compromising user experience. Founded by David Haber, Matthias Kraft, and Mateo Rojas-Carulla in 2021, the company recently announced a $20M Series A led by Atomico and joined by Citi Ventures, Dropbox Ventures, and existing investors including redalpine, Fly Ventures, and Inovia Capital.

Good Reads

Can AI Scaling Continue Through 2030? (EPOCH AI)

“We investigate the scalability of AI training runs. We identify electric power, chip manufacturing, data and latency as constraints. We conclude that 2e29 FLOP training runs will likely be feasible by 2030.”

North America added a whole Silicon Valley’s worth of data center inventory this year. It’s not enough. (Sherwood)

“All of Silicon Valley has 459 MW of data center supply, while those main markets have a total of 5,689 MW. That’s up 10% from a year ago and about double what it was five years ago.”

Counterdiabatic driving or how to not lose your marbles (Kipu Quantum on LinkedIn)

“Rather than giving up on NISQ computers, we are confident that achieving quantum advantage with NISQ processor is possible within next 12 months. In other words, NISQ advantage (NISQA) is core to Kipu Quantum technologies.”

A Comprehensive Overview of Large Language Models (arXiv)

“This review article is intended to not only provide a systematic survey but also a quick comprehensive reference for the researchers and practitioners to draw insights from extensive informative summaries of the existing works to advance the LLM research.”

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