We made a major update to the Future of Computing blog this week: a new logo, a new newsletter tool, and a new Discord, where you can meet fellow computing geeks (see below). Now, on to this week’s interview:

How to Evaluate Quantum Processor’s Performance: In Conversation With Himadri Majumdar*

Not all quantum processors are created equal. While everyone aims to increase the fidelity of quantum states and lower the error rate, many other metrics also play an important role in building large-scale quantum computers and delivering commercial value.

SemiQon builds quantum processors by trapping electrons or holes within silicon and using their spins as qubits, called silicon spin qubits. Since its founding as a spinout from the Finnish research institute VTT in 2023 and our last interview on Future of Computing, SemiQon has successfully completed several fab runs and started shipping the first 4-qubit chips to research collaborators.

This article is part of a series of deep dives into SemiQon’s advancements, and we had the pleasure of speaking again with Himadri Majumdar, CEO and co-founder of SemiQon, about what quantum hardware manufacturers and end users should keep in mind when evaluating quantum processors:

*Sponsored post—we greatly appreciate the support from SemiQon

Future of Computing News

⚛️ Round Two: Announcing The Startups Joining QAI’s 2nd Cohort Accelerator Program (QAI Ventures)

⚛️ From the 2nd batch, we've already interviewed QCentroid: Shaping the Future of Using Quantum Algorithms

⚛️ 10 powerful simulation modules: HQS introduces HQStage, a modular quantum simulation and quantum computing toolkit (QBN)

🦾 To expand the manufacturing capacity within the EU: EU Chips Act Set to Attract Over €100 Billion in Private Investment by 2030 (PYMNTS/CPI)

🤖 New LLM by Mistral: Codestral: Hello, World! (Mistral)

🦾 52 percent better ray tracing, 30 percent less power: Arm says its next-gen mobile GPU will be its most ‘performant and efficient’ (The Verge)

🤖 Don't you like glue on your pizza? Google scrambles to manually remove weird AI answers in search (The Verge)

Funding News

🤖 Elon Musk’s xAI raises $6 billion to fund its race against ChatGPT and all the rest (The Verge)

🤖 Engineering AI Platform Neural Concept Raises $27 Million Series B to Further Accelerate Global Growth (BusinessWire)

🤖 Nvidia co-leads Series A round for AI startup Twelve Labs (Korean Economic Daily)

🤖 Steve Jurvetson’s Future Ventures targets AI with $169M fund (PitchBook)

🧠 Swiss AI chip startup Synthara raises $11M to make in-memory computing accessible (Tech Funding News)

⚛️ Delft-based Q*Bird raises €2.5 million to accelerate growth of its quantum security tech (EU Startups)

⚡️NcodiN secures €3.5 M for optical interposer tech to meet the needs of HPC and AI (Tech EU)

⚡️ Learn more about their tech from our interview with NcodiN: Shaping the Future of Optical Data Communications between Chiplets

🤖 Cool GitHub Projects

YOLOv10: Real-Time End-to-End Object Detection

Minimal and clean examples of machine learning algorithm implementations

Reproducing GPT-2 (124M) in llm.c in 90 minutes for $20

Join Our New Discord Community

… and connect with fellow computing geeks:

https://future-of-computing.com/discord/

Good Reads

🤖 AI Integration and Modularization (Stratechery)

"The first takeaway from this analysis is that Google’s strategy truly is unique: they are, as Nadella noted, the Apple of AI. The bigger question is if this matters: as I noted above, integration has proven to be a sustainable differentiation in (1) the consumer market, where the buyer is the user, and thus values the user experience benefits that come from integration, and when (2) those user experience benefits are manifested in devices."

🤖 What We Learned from a Year of Building with LLMs (Part I) (Oreilly)

"We recommend starting with prompting when developing new applications. It’s easy to both underestimate and overestimate its importance. It’s underestimated because the right prompting techniques, when used correctly, can get us very far. It’s overestimated because even prompt-based applications require significant engineering around the prompt to work well."

🤖 Consensus is 'Google Scholar meets GPT' (Cerebral Valley)

"If you're a small company with only a couple thousand documents and want to throw a vector database on top of it and do some generation, that's not too difficult to set up. But if you want to search through hundreds of millions of extremely dense technical documents like academic papers, and it has to be super fine-grained with the very best research at the top every single time, building a search engine that does that is extremely difficult."

🖥️ Essays on programming I think about a lot (Ben Kuhn)

"Every so often I read an essay that I end up thinking about, and citing in conversation, over and over again. Here’s my index of all the ones of those I can remember! I’ll try to keep it up to date as I think of more."

🖥️ Writing a Unix clone in about a month (Drew DeVault)

"On April 21st, I set out to see how much of a Unix-like operating system for x86_64 targets that I could put together in about a month. The result is Bunnix. Not including days I didn’t work on Bunnix for one reason or another, I spent 27 days on this project."

🦾 Beyond Moore’s law: specialised processors and modular chips (Bell Whether)

"In September 2013, Apple launched its iPhone 5S with a new kind of brain called the A7. The chip featured an innovative architecture integrating different types of processor on a single die. A die is a small semiconductor block housing a functional circuit, and where there had only been Central Processing Units (CPUs), there were now also Graphics Processing Units (GPUs), enabling real-time graphics for HDR photos and immersive games like Infinity Blade III. That move was a subtle yet consequential shift into a new approach called heterogeneous computing."

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