Showing posts with label QSCA. Show all posts
Showing posts with label QSCA. Show all posts

Sunday, March 29, 2026

Google Quantum AI Expands to Neutral Atoms. I’ve Covered Both Platforms. Here’s the Context.

Source: Building superconducting and neutral atom quantum computers | Google Quantum AI | March 24, 2026

Google Quantum AI announced last week that it is adding neutral atom computing to its existing superconducting program. I covered both platforms in my qubit series earlier this year, so this is a good moment to connect the dots.

I wrote about superconducting qubits in January and neutral atom qubits in February. Google’s announcement is a practical illustration of why both matter, and why no single platform has won.

Superconducting systems have scaled to circuits with millions of gate and measurement cycles, each running in about a microsecond. Neutral atom arrays have reached roughly 10,000 qubits, but their cycle times run in milliseconds. The tradeoff breaks cleanly along two axes: superconducting scales more readily in circuit depth; neutral atoms scale more readily in qubit count. Google is betting that running both in parallel gets to commercially useful hardware faster than doubling down on one.

Key tradeoffs between superconducting and neutral atom qubit platforms.

To lead the neutral atom effort, Google hired Dr. Adam Kaufman from JILA and NIST in Boulder, Colorado. He retains his CU Boulder faculty appointment. Boulder is a credible home for this work: it hosts the NSF Q-SEnSE Institute, the National Quantum Nanofab, and the U.S. EDA Quantum TechHub. Google also noted its continued collaboration with QuEra, the neutral atom startup whose researchers built much of the foundational methodology.

Google also said it expects commercially relevant quantum computers based on superconducting technology by the end of this decade. Adding neutral atoms to the portfolio is not a hedge on that timeline; it is a way to cover problem types that each architecture handles differently.

If you want the technical foundation for what Google announced, my January and February posts are a great place to start.

Sunday, March 22, 2026

Writing Is How I Learn

Writing is how I learn. Not a side effect of learning, the mechanism itself. It's only taken me 60 years or so to realize. Yeah it took me a little longer than it should have to figure out, partly because my mother was an English teacher. Growing up with that in the house, writing felt like an assignment, something to be graded and corrected. I guess I avoided it for years.

Now I cannot imagine working without it.

The habit really started in college. I was never a yellow highlighter. I took detailed notes in class, then went back and rewrote them, filling in gaps, looking up anything I had not fully understood. I was not studying. I was writing my way to comprehension. I just did not recognize it as writing at the time.

Looking back at over 800 blog posts since 2005, I cannot identify a single topic I learned in school, at least not directly. Along the way I wrote five textbooks. Each one forced the same process at a longer scale: find the gaps, trace the logic, write until it holds. What came after was anything but familiar. But the foundation school set has mattered more than I appreciated at the time.

Circuit analysis taught me how to trace cause and effect through a system. Signals and systems gave me a way to think about information in motion. Mathematics gave me a tolerance for abstraction. Physics gave me an instinct for what is physically possible and what is not. None of those subjects mapped directly to telecommunications, networking, cybersecurity, AI, or quantum computing. But without them, I would have had nothing to connect the new ideas to. Every emerging technology I have written about made more sense because of something I learned in a classroom decades earlier, even when the connection was not obvious at first.

My first real test was the transition from the plain old telephone service network to internet protocol. POTS was settled territory: copper pairs, circuit switching, predictable behavior. IP emerged as none of those things. The protocols were still being written, the standards contested, and the people producing the documentation were often the same people building the systems. There was no textbook that had caught up. Writing about it forced me to work through the logic myself, trace the signal path, understand why packet switching broke the assumptions that circuit switching had held for a century. Reading alone would not have gotten me there.

Each technology that followed emerged the same way. Networking protocols solidified and cybersecurity emerged alongside them, then ahead of them. AI emerged from research labs into practice before most organizations knew what to do with it. Quantum computing is emerging now, still settling on its own vocabulary, its own benchmarks, its own honest assessment of what it can and cannot do. I came to each as an outsider working from preprints, conference proceedings, vendor white papers, and conversations with people who were themselves still figuring it out.

That turned out to be the ideal condition for writing. When there is no established explanation to defer to, you have to build your own. For me, the act of building it is where the learning happens.

Every draft reveals what I actually understand and what I have been skimming over. Missing causal links surface immediately. So do circular definitions I had mistaken for insight. Quantum is no different. I understood qubits loosely until I wrote a series on qubit technologies for a general audience. Six posts in, I understood the field in a way that a month of reading had not produced.

My mother would note that I came around eventually :)

This is pretty much how I learn. Researchers call it elaborative encoding: building understanding by reconstructing information in your own words rather than receiving it passively. The VARK model would place me in the read/write category, though writing as a cognitive tool goes beyond simple preference. It is the mechanism by which I connect new ideas to existing foundations.

Not everyone learns this way, and that is a really important point. Some people think through conversation. Others need to build something physical, draw a diagram, or hear an explanation out loud before it clicks. The method matters less than finding the one that works for you and committing to it. For me, writing is that method. It always has been, even when I did not want to admit it.

Writing also serves readers who learn differently. A well-constructed post gives the visual learner a structure to follow, the read/write learner direct access to the logic, and the reflective learner something to push back against. I write to learn, but if the post does its job, someone with a completely different learning style picks something useful out of it too. That possibility has kept me writing for 21 years.

If you work in a technical field and you are not writing, start. Pick something you half-understand and write until you fully do.

This past week that came full circle. Someone at the Quantum Supply Chain Accelerator site walkthrough at STCC Technology Park mentioned my quantum computing posts. It was an encouragement to keep writing.