Topological Qubits: A Different Way to Build a Quantum Computer

Earlier posts here covered five qubit platforms: superconducting, trapped ion, photonic, neutral atom, and silicon spin. Each one answers the same question differently: how do you isolate a quantum system long enough to do useful computation? Topological qubits are the sixth approach, and the most contested. They exist at the intersection of materials science, condensed matter physics, and a twenty-year bet by Microsoft that the rest of the field is solving the wrong problem.

Every qubit in a quantum computer is fragile. Superconducting qubits, the kind Google and IBM use, operate near absolute zero and still lose coherence in microseconds. Gate error rates run between 0.1% and 1%, which sounds small until you consider that a useful fault-tolerant quantum computer may need error rates below one in a million. The industry has spent years stacking error correction on top of error correction, adding physical qubits to protect logical ones.

Topological qubits take a different approach. Instead of fighting noise with redundancy, they aim to make the qubit itself resistant to local disturbances. The physics relies on Majorana zero modes, exotic quasiparticles that store quantum information non-locally across two spatially separated points. Because the information is spread out, a local disturbance at one point cannot corrupt the qubit on its own. Topology, the branch of math that describes properties preserved under continuous deformation, gives the qubit its protection. You would have to disturb both ends of the system simultaneously to flip the state, which is far less likely than a single-point noise event.

Microsoft has pursued this path for nearly two decades through its Station Q research group. In February 2025, the company announced Majorana 1, described as the world's first quantum processor built on a topological core architecture. The chip used indium arsenide and aluminum, a hybrid superconductor-semiconductor platform. The research appeared in Nature in February 2025. The announcement drew immediate scrutiny. Independent researchers questioned the Topological Gap Protocol Microsoft uses to confirm the presence of Majorana modes, a University of St Andrews physicist published a challenge to its validity in March 2025, and Scientific American noted that Microsoft had previously retracted a high-profile Nature paper in 2021 after outside experts found the data could have come from material imperfections rather than topological qubits.

On June 2, 2026, Microsoft announced Majorana 2 at its Build 2026 conference. The new chip replaces aluminum with lead in the superconducting material stack and redesigns the semiconductor structure. Microsoft reports a mean qubit lifetime of 20 seconds, with some measurements exceeding one minute. That is a claimed 1,000-fold improvement over Majorana 1. Gate operations run at one microsecond, and the qubit footprint is 1/100th of a millimeter. The company now targets a commercially useful scalable quantum computer by 2029, moved up from a prior estimate of 2033. The materials iteration was accelerated with Microsoft Discovery, the company's agentic AI platform for scientific research.

The physics community's response has been consistent with the pattern from Majorana 1. Outside experts say the topological approach still lacks sufficient independent verification. The 20-second coherence time is striking if accurate, since superconducting qubits typically decohere in around 100 microseconds. But coherence time alone does not confirm the topological mechanism Microsoft claims is responsible for it. The company has a history of bold announcements followed by retraction or significant revision, and that history shapes how the community reads each new result.

Compared to the five platforms covered in earlier posts, topological qubits occupy a unique position. Superconducting qubits and silicon spin qubits are fabricated systems with well-characterized error mechanisms. Trapped ions and neutral atoms offer long coherence times but slow gates. Photonic qubits avoid decoherence but struggle with deterministic interactions. Topological qubits, if the physics holds, would offer built-in error protection that reduces the overhead for fault tolerance substantially. IBM's Condor chip reached 1,121 superconducting qubits in 2023. Microsoft is betting that the right number is not more physical qubits but better ones, and that topological protection is how you get there.

Whether Majorana 2 represents genuine progress or another contested milestone, Microsoft is the only major commercial player publishing peer-reviewed claims on topological qubit architecture. The debate in the physics community is real, the skepticism is well-founded, and the potential, if the approach works, remains significant. If you want the background on the other five platforms before going deeper on this one, the earlier posts are linked at the top of this page.

When Quantum Hardware Drifts, NVIDIA’s New AI Steps In

Quantum computers do not fail in the way most people expect. They do not crash or throw errors in any obvious way. Instead, they drift. Qubits lose coherence, coupling parameters shift, and gate fidelities degrade without warning. Catching and correcting that drift is the job of calibration and error correction, traditionally a manual, slow, expert-intensive process. NVIDIA Ising is NVIDIA's attempt to close that gap with AI.

NVIDIA Ising is a model family, training framework, and open cookbook released in April 2026. It targets two specific problems: quantum processor calibration and real-time error correction decoding. Both problems block the path to fault-tolerant quantum computing, and both have resisted scaling by brute force. NVIDIA is betting that AI, specifically vision-language models and 3D convolutional neural networks, can do what hardware iteration alone cannot.

Autonomous calibration via vision-language models

The calibration side uses a vision-language model called Ising Calibration 1, a 35-billion-parameter Mixture-of-Experts (MoE) model based on Qwen3.5-35B-A3B. It reads calibration plots directly, the exact same visual plots a human engineer would study, and operates inside an agentic workflow to automate processor bring-up and retuning.

NVIDIA benchmarked the model against the QCalEval dataset, which contains 243 samples across 87 scenario types covering superconducting qubits and neutral atoms. Evaluating six core question types, Ising Calibration 1 achieved a 74.7% zero-shot average, outperforming general-purpose frontier models like GPT-4o, Claude 3.5 Sonnet, and Gemini 1.5 Pro pressed into service. These numbers reflect the power of a model fine-tuned specifically for this domain rather than a general-purpose AI model.

Real-time quantum error correction

The decoding side addresses quantum error correction (QEC), where latency is not a convenience issue; it is a hard barrier. A surface code decoder that cannot keep up with syndrome extraction in real time becomes a fatal bottleneck for the entire processor.

NVIDIA's Ising Decoder SurfaceCode 1 uses a 3D CNN architecture to handle syndrome data across both space and time, a foundational requirement for lattice surgery operations. NVIDIA offers two variants. The Fast variant delivers 2.5x lower latency than PyMatching at d=13, p=0.003. The Accurate variant trades minor latency to deliver up to 3x better logical error rates compared to traditional decoding benchmarks. Both weights and an open training framework are hosted on Hugging Face, allowing hardware teams to fine-tune the decoders to their own noise models.

An open ecosystem for quantum builders

The training framework is fully open. NVIDIA published the QCalEval benchmark and the Ising Decoding training code on GitHub. Quantum hardware teams can adapt both the calibration agent and the decoder to their own QPU noise profiles rather than accepting a rigid, one-size-fits-all model. The Quantum Calibration Agent Blueprint provides an end-to-end starting point using the NVIDIA NeMo Agent Toolkit.

For quantum hardware builders and operators, the operational impact is concrete. Bringing up a new QPU or retuning after drift currently requires skilled engineers reading plots and adjusting parameters manually. Ising Calibration 1 runs that loop autonomously, compressing the time between hardware changes and operational readiness. The decoder result is even more immediately measurable: PyMatching is the current production standard, and the Ising Decoder beats it on both latency and accuracy at d=13. This is not a research result waiting for productization; it is a component you can drop directly into a CUDA-Q QEC pipeline today.

What this means for the quantum workforce

The workforce implications are worth spelling out. The quantum field has long assumed that the talent gap is primarily a physics problem, that you need more quantum physicists. NVIDIA Ising points to a second gap: AI and software engineers who can work at the quantum-classical interface. Ising Calibration 1 is a vision-language model. The decoder is a 3D CNN trained on syndrome data. These are standard deep learning architectures applied to a specific domain. A person who understands noise modeling, real-time inference pipelines, and model fine-tuning can contribute to the quantum software stack without a physics PhD.

For workforce development programs targeting the quantum supply chain, Ising gives you a concrete reference architecture for what the adjacent skill set looks like. The gap is not only physicists. It is technicians and engineers who can work with QPU data, fine-tune models to specific noise profiles, and integrate AI components into hardware pipelines. Community colleges and technical programs can point to this stack, VLMs for calibration, CNNs for decoding, agentic workflows for automation, as a curriculum target that does not require starting from quantum mechanics.

The gap between current NISQ devices and fault-tolerant quantum computers is not primarily a physics problem at this point. Calibration drift and decoder latency are engineering problems, and AI has a track record of closing engineering gaps faster than iterative hardware design does. NVIDIA Ising is a focused, tool-level response to two specific chokepoints. The benchmark numbers are strong. The tooling is open. If the results hold against real QPU data in production, this is a significant piece of the fault-tolerance stack, and a clear signal about where the next wave of quantum workforce demand is headed.

The Ferrari That Jony Ive Helped Build

I drive a Tesla now. Before that I had a Porsche Macan, and one of the things I miss is the cold start. The V6 would fire at a high idle, near 2,200 rpm with the exhaust valves open, hold there for 30 or 40 seconds, then settle into a normal idle. You felt it in your chest a beat before you heard it. Some mornings I started the thing just to listen. I taught 40 miles away at the University of Hartford, about 5 miles to the highway on the way home, and the part I waited for was getting on it at the Route 91 on-ramp. The Macan would dig in and snarl through the acceleration. The Tesla does the same merge faster and says nothing. Most days that's fine. Some days, on an empty road, I want internal combustion back.

Ferrari just showed its first electric car, the Luce, Italian for light. Four motors, one per wheel, 1,050 cv, zero to 100 km/h in 2.5 seconds, a 122 kWh battery on 800 volts, about 530 km of range. It also weighs 2,260 kg, seats five, and has a hatchback. 

What got my attention was the design credit. Ferrari handed the inside and outside to LoveFrom, Jony Ive and Marc Newson's studio. Ive ran design at Apple for twenty years. You can see it in the cabin: clean surfaces, glass buttons, real switches you can feel, a passenger shell that sits almost on its own inside the body. Ferrari has never let an outside firm shape a car like this. That alone is news.

They know about the sound, too. The Luce mounts an accelerometer at the center of the rear axle, reads the real frequencies coming off the spinning parts, then equalizes and amplifies them like a guitar pickup. So it isn't a fake V8 played through the stereo. It's the actual car, turned up. The motors use a Halbach array in the rotor, borrowed from Formula 1, and the front pair spins to 30,000 rpm.

Not everyone agrees the bet is worth making. Lamborghini scrapped its first EV, the Lanzador, in February, with CEO Stephan Winkelmann saying demand for an electric supercar was close to zero and calling EV work an expensive hobby. The Lanzador comes back later as a plug-in hybrid instead. Part of his reasoning was the exact thing I keep talking about: their buyers want engine sound and mechanical feedback, and the battery cars don't deliver it yet. After the Luce reveal, Ferrari's stock dropped and Winkelmann went on CNBC to say canceling was the right call. So one company is amplifying real vibration through a sensor, and the other decided the problem isn't solved and walked away.

So Ferrari looked at the same gap I feel every morning and tried to fill it with a sensor and some signal processing instead of an exhaust pipe. I doubt it gives you the cold start, that high-idle bark before the valves close, or the snarl of jumping on it on a highway on-ramp. You can't fake combustion you don't have. But I'm pretty sure I’d rather hear what the car is really doing than a recording of an engine nobody built. But…. I'll reserve judgment until I sit in one.

Apple’s Corecrypto Proof And A Next Phase Of Quantum Migration

In the late 1990s, a new kind of encryption called elliptic curve cryptography started showing up in phones, websites, and game consoles. The math behind it was solid. The code that ran the math was not. Engineers made small mistakes deep inside the software, and attackers found them. In 2010, Sony lost the master signing key for the PlayStation 3 because the code reused a random number it was supposed to generate fresh every time. Once that key leaked, anyone could load any software they wanted onto the console. The math worked. The code did not.

I wrote in April about the two paths to post-quantum cryptography, software and hardware, and how moving everything over will take more than a decade. Post-quantum cryptography, or PQC, is the new family of encryption designed to survive future quantum computers. Today’s encryption depends on math problems that classical computers cannot solve in any reasonable time. A large enough quantum computer running Shor’s algorithm makes those problems easy.

On May 22, Apple released a major update to corecrypto, the encryption software running on more than 2.5 billion Apple devices. The update includes the new post-quantum algorithms standardized by NIST, called ML-KEM (for key exchange) and ML-DSA (for digital signatures). What makes this release different is that Apple also published a mathematical proof that the code does exactly what the standard says it should do. No surprises. No hidden mistakes. The technique is called formal verification, and it has been used for decades in chip design and aerospace software. Seeing it applied to cryptography on consumer devices is new.

It often takes years of work and very specialized math skills. Apple’s proof runs more than 50,000 steps and already caught a bug that normal testing would have missed. Apple open-sourced the tools, including software built by Galois Inc., so other companies can do the same thing. iMessage, Signal, Chrome, and Cloudflare are already shipping post-quantum protections, and more are coming.

For decades the pattern was ship first, fix later. The quantum migration is too important and too complicated for that. Apple just showed what doing it right looks like. The next step for us academics - colleges and training programs must prepare engineers who will do it.

From Petri Dishes to Quantum Proteins

Gemini Generated Image

I’ve written here a bit about my undergrad degree in microbiology, The path to that degree came with a strong dose of biochemistry along the way. UMass Amherst, mid-1970s. I worked through enzyme kinetics, the Michaelis-Menten equation, and the standard biochem textbook of the day, Lehninger's Biochemistry. Then I spent a few years in a hospital clinical lab, plating cultures and squinting at gram stains, before I shifted into electrical engineering. The proteins we now talk about in quantum computing papers were, back then, things you drew mechanisms for on an exam and tried not to confuse.

When I saw the May 19 Phys.org piece on a 12,635-atom protein simulation, the old biochem brain lit up. Cleveland Clinic, RIKEN, and IBM used a quantum-centric supercomputing workflow to model two real proteins, T4-Lysozyme and Trypsin, sitting in water with the small molecules they bind to. Trypsin is a serine protease from your pancreas. It cuts other proteins at specific spots, after the amino acids lysine and arginine. T4-Lysozyme is an enzyme that breaks down peptidoglycan, the mesh that holds bacterial cell walls together. Both are fundamental biochem topics.

So.... what does "simulate a protein" mean? A protein is a folded chain of atoms. It does its job because of its shape and because of how the electrons inside it are arranged. Simulating it means using physics to figure out where every electron is and how it is moving. That information is what tells you whether a drug molecule will fit into the protein and stick, or just bounce off. The bigger the protein, the harder that calculation gets, and the difficulty grows fast.

Six months earlier, the same team did a 303-atom Trp-cage, a tiny model protein biochemists use as a folding test case. This run jumped to 12,635 atoms. That is a 40-fold increase in size and a 210-fold gain in accuracy on a key step. The hardware was IBM's 156-qubit Heron r2 quantum processors at Cleveland Clinic and RIKEN, working alongside Fugaku and Miyabi-G, two of the largest classical supercomputers in Japan. The team ran the quantum machines for more than 100 hours and collected 1.3 billion measurements.

So why is quantum better here? Think about it this way. Electrons in a molecule don't sit still like marbles. They behave like little waves that overlap and influence each other. To keep track of just one electron on a regular computer, you need to store some numbers describing its wave. Add a second electron that interacts with the first, and you don't double the work, you square it. Add a third, you cube it. The work doubles with every electron you add. Ten electrons is easy. Fifty electrons fills up a big supercomputer. A hundred electrons would need more memory than there are atoms in the Earth. A protein has thousands.

A quantum computer is built out of the same kind of stuff the electrons are made of. Each qubit is itself a tiny quantum object that can hold the wave behavior directly. So instead of storing a giant list of numbers to fake the physics, the qubits just do the physics. Fifty electrons need about fifty qubits, not a supercomputer. One hundred electrons need about one hundred qubits. The problem stops blowing up. Richard Feynman pointed this out in 1981: if you want to simulate nature, use a computer made of the same stuff as nature.

Back to classical computers for a moment. There is a method that gets the right answer exactly, called full configuration interaction. It is the gold standard. The problem is that every electron you add explodes the work, which is exactly the blowup I just described. It works fine for a water molecule. It is impossible for a protein.

So computational chemists fake it. They use an approximation called density functional theory, or DFT, which gives a fast, decent answer for most of a molecule. DFT is the workhorse of computational biochemistry today. It has a known weak spot, though. It struggles in the active site of an enzyme, the small pocket where the actual chemistry happens and where a drug would bind. That is the one place you need the answer to be right, and it is the one place DFT gets shaky.

That is where the hybrid setup comes in. Use the quantum computer on the small, tricky pocket where DFT fails. Use the classical supercomputer on the rest of the protein and the surrounding water, where DFT works fine. Each machine does the part it is good at. Neither could do this protein alone. Together they can.

Hybrid classical-quantum is how real chemistry gets done in the near term, not pure quantum and not pure classical. Biochemistry is one of the applications pulling the field forward, especially structure-based drug design. And atom count is becoming the benchmark people compare against, the way transistor count once was for chips.

Beaming Power From a Cessna

Back in 2023 I remember reading this writeup on Caltech's Space Solar Power Demonstrator. They beamed a small amount of microwave power between panels on a satellite in LEO. Neat physics, but I have heard the space-based solar pitch for thirty years and the engineering never closes. Launch mass, spectrum rights, pointing accuracy, regulatory approval for a multi-megawatt beam over populated areas. Something in that list always kills it.

Then I read this Spectrum article about Overview Energy. They flew a Cessna turboprop over Pennsylvania at 5,000 meters in 70-knot crosswinds and held a power beam on a ground receiver the whole time. Watts, not kilowatts, but the first time anyone has done it from a moving platform at altitude. They beamed near-infrared light, not microwaves. That choice is the part worth looking at.

Microwaves have two problems for ground power delivery. The good bands between 2 and 20 GHz are already spoken for. 5G, GPS, satellite links, weather radar, military. You will not get a license to transmit megawatts in any of them. The second problem is beam spread. Diffraction limits how tight you can focus, and at microwave wavelengths the spot on the ground is kilometers across even from a big aperture. That means a rectenna farm the size of a small city.

Optical wavelengths change the math. Same aperture, beam spot drops from kilometers to meters at GEO range. No spectrum fight either, since power transmission at optical frequencies is not allocated the way RF is. The receiver question gets interesting too. Overview wants to drop the IR onto existing utility-scale PV farms. Silicon is not tuned for a single IR line so you lose efficiency at the receiver, but you also skip building a new class of ground infrastructure and skip the permitting fight that comes with it. Whether the efficiency hit is worth the deployment savings depends on numbers we have not seen yet.

Now the scaling, which is what every engineer reading this already knows is the hard part. A working demo at the bench is one thing. A working demo a thousand or a million times bigger is a different machine, with different failure modes, different cost curves, and different second-order effects that did not exist at small scale. Heat dissipation that is trivial in a benchtop laser becomes the dominant design problem in a megawatt source. Vibration modes that did not matter in a fixed lab fixture wreck pointing accuracy on a 100-meter deployed structure. Manufacturing tolerances that were acceptable on one unit are not survivable across the thousands of components a flight article needs. Cost per watt that pencils at the prototype scale almost never holds when you push three or four orders of magnitude. This is the part of engineering that nobody outside the field appreciates and everyone inside it has been burned by.

Apply that to Overview. The Cessna run was watts. DARPA's July 2025 demo pushed 800 watts across 8.6 km for half a minute. Overview's roadmap calls for megawatts from GEO by 2030 and gigawatts later. Five to nine orders of magnitude. Continuous operation instead of a 30-second pulse. Pointing accuracy in the microradian range from 36,000 km up, on a satellite that heats and cools every orbit and flexes accordingly. The Cessna proves they can track a target from a moving platform. GEO needs the same trick done a thousand times better, with thermal management for a source running at a million times the optical power.

The spacecraft itself is the other hard part. To collect useful sunlight you need a big aperture. A big aperture does not fit in a rocket fairing, so it has to fold for launch and deploy in orbit. JWST showed that can work. JWST also showed how close that kind of mission comes to dying. Add debris survivability over a 20-year design life, station-keeping fuel budgets, and disposal at end of life, and you have a spacecraft program as risky as the beam.

So where does that leave me. Still skeptical, but less than I was. The 2023 Caltech work was a physics check. The Cessna flight is a systems check. Source, beam control, pointing, tracking, and a PV-style receiver, all running together on a moving platform in real weather. If Overview gets a LEO demonstrator up and lands a kilowatt on the ground from a folded-then-deployed aperture, people will be paying close attention.

What Jensen Huang Thinks Smart Looks Like Now

Years back I had a student who was terrible at exams. Multiple choice, timed problems, placement exams, all of it. He barely squeaked by in lecture. In lab he was someone else. He would stand next to a partner who was building a circuit and just watch. Then he would point at a cap and ask what happens if it fails open. He was usually right before the thing ever got powered up.

He got a field tech job after he graduated. Two years in, customers were calling and asking for him by name. He still cannot tell you the formula for a low-pass filter off the top of his head. He can tell you, on the phone, that the problem you think you have is not the one you actually have.

I thought about him this week reading about Jensen Huang on Jodi Shelton’s A Bit Personal Podcast. She asked him who the smartest person he has ever met is. He would not answer. He said the question itself is the wrong one now.

Huang’s point: the kind of smart we have always rewarded, the technical problem solver, is becoming a commodity. He used software programming as the example. For decades that was the smart-person job. Now it is the first thing AI is doing well.

So what does he think smart looks like going forward? Three things stacked. Technical understanding, human empathy, and the ability to pick up on what nobody is saying. That last one he called seeing around corners. It comes from data, analysis, first principles, life experience, wisdom, and reading the people in the room. People who have that mix tend to spot problems before they happen.

This is part of why I started doing oral exams in my Circuits class. A written test will tell me a student got the right number. It will not tell me whether they know why they used mesh instead of nodal, what a time constant actually means, or where the sign error came from. Sit them down and make them talk through it and you find out fast. 

Most of what engineering school grades on is the part AI is taking over. Timed problem sets. Multiple choice. Take-home work that gets judged on how polished it looks. The stuff Huang is talking about, reading a situation, asking the question nobody else thought to ask, knowing something is off before the numbers say so, is harder to grade. It is also what is going to matter.

My old student would have bombed every test Huang is calling outdated. He read a lab bench the way some people read a chessboard. Twenty years ago that made him a good tech. Today it makes him the kind of engineer you cannot swap out for a chatbot.

From $14 Billion to $125 Million: What Killed Chegg

I had a Chegg account for years. For my electrical engineering classes, I would check my  own homework solutions using Chegg before handing them to students, just to double check my work. It was a good tool and worth the money at the time.

Then one night I tried Google Gemini on the same kind of problem. It gave me the answer, walked through the steps, and did a better job explaining what was going on. I sat there for a minute and thought, why am I paying for Chegg? I cancelled the next morning. These days Gemini is what I lean on for problem solving in my classes.

Turns out a lot of people made the same call. Chegg was worth around $14 billion in 2021. Today it is worth about $125 million. The company laid off almost half its remaining staff last October. Revenue last quarter dropped 42% from the year before. The CEO points the finger at ChatGPT and at Google's new AI answers that show up right at the top of the search page. Students get their answer before they ever click through to a paywall.

Khan Academy is a different story but feels some of the same heat. It is a nonprofit, so there is no stock price to watch. A few years back they rolled out Khanmigo, an AI tutor, and got it in front of more than 1.4 million students. On paper, big win. In April, Sal Khan admitted most students barely touched it. The reason is not hard to figure out. Free AI is already open in another tab. Kids are not going to switch over to a separate tutor when help is right there.

Both companies were built to sit between a student and an answer. AI now sits closer to the student than either of them. When that happens, the middleman business gets thin in a hurry.

The Oral Exam Experiment Worked

Last fall I posted that I was dropping homework from the grade book and adding an oral portion to every exam in the spring. Students were running Engineering homework problems through Gemini and handing in solutions they could not explain when I asked. I was grading a chatbot, so I stopped.

Spring semester is over. It worked. The oral portion runs about ten minutes per student. They pick one problem from their written work and walk me through it. Why mesh and not nodal. What the time constant tells you about the circuit. Where the negative sign came from. I learn more about what a student actually understands in that ten minutes than I used to learn from a semester of graded homework.

A new Lumina Foundation-Gallup study says 57% of US college students use AI in their coursework at least weekly, and one in five use it every day. At the same time, 53% say their school discourages or prohibits it. Daily use is highest among men and among business, tech, and engineering students. The students avoiding AI mostly cite ethical concerns and school policy, so the ones following the rules are falling behind on a tool they will use the rest of their careers.

My position on AI in education is simple. If we are preparing students for the jobs they are about to take, AI has to be in every class. Every engineering job they walk into will expect them to use these tools well. A program that prohibits AI is training students for a job market that no longer exists. The work is not to keep AI out of the classroom. The work is to teach students how to use it, where it fails, and when to check it against first principles.

That still leaves the assessment problem. If students use AI on everything, how do you know what they understand? You change how you measure them. Oral exams catch what written work cannot. In-class paper problems catch it. Hands-on labs, where a student wires a circuit on a breadboard, takes scope measurements, and explains what they are seeing, catch it cold. Take-home essays graded on polish do not catch anything anymore.

The AI can solve the circuit. It cannot explain why this student chose the loop they chose, and it cannot wire the breadboard when the lab is due at five. That is what we should be assessing, and that is the work employers are hiring for.

Car Buyer vs User

Image Google Gemini Generated

I stopped at a dealership recently to get some parts for my wife’s car and, as always, took a peek in the showroom. I ended up looking at stickers and counting the screens in a mid-range SUV. Four. One in the dash, one in the middle, one for the passenger, one in the back. A salesperson grabbed me and started walking me through them. I stopped paying attention about 30 seconds in. Paddle shifters, massage seats, gester controls, ten drive modes, ambient lighting, self parking, some kind of fragrance thing. I nodded. I would use none of it.

Apparently I am not alone. The JD Power 2024 Tech Experience Index rates the so-called advanced driver features near the bottom of what owners actually value. Gesture controls hit 43 problems per 100 vehicles, which is a polite way of saying they do not work. People pay for this stuff, give up on it, and then never bring it up again until they trade the car in.

I keep thinking about the iPhone in this context. Two billion of them have shipped. None came with a manual. Kids figure them out before they can read. Apple kept taking things away. The home button. The headphone jack. Each cut made the phone easier. Carmakers do the opposite. They keep piling features on and call it progress.

I drive a 2020 Tesla Model 3. The software is great. The maps work, the updates show up overnight, the menus make sense. The car also has a turn signal stalk, a gear stalk, and two scroll wheels on the wheel. Tesla removed all of those in the 2024 Highland refresh. You signal now by pressing a button on the spoke. You shift gears by swiping a slider on the screen. Owners are paying around $400 for aftermarket kits to put the stalks back in. Tesla rolled out a version in China with the stalk returned. The best software in the industry could not save the worst ergonomic decision in the industry.

A few years ago BMW went the other way and tried to charge for hardware. Eighteen dollars a month to turn on the heated seats already wired into the car. About 90 percent of BMWs leave the factory with the hardware in place. Customers were essentially being charged twice. BMW killed it in September 2023. New Jersey introduced a bill to make hardware paywalls illegal on cars, which tells you how badly that went over.

None of this is accidental. Cars get designed for the showroom because that is where the sale happens. Three knobs and two screens looks cheap next to haptic glass and 47 menu layers. Marketing wants the acronym count on the window sticker. So you walk in as a buyer, feel reassured by the complexity, sign, and drive home. About ten minutes later you become the user. The user wants the heat on without looking at a screen, the mirrors adjusted without a settings tree, and the left turn signaled with one finger (not that one! )

Things are starting to turn. Mazda kept the knobs and gets credit for it every time someone reviews the car. Volkswagen pulled the buttons, watched sales slip, and admitted in 2025 the buttons are coming back. Their design chief said the reversal is permanent. Euro NCAP is moving toward requiring physical controls on five core functions to keep a five-star rating, which means the regulators are about to do what the marketing departments would not.

The buyer is in the showroom for an afternoon. The best interface is one you do not have to think about, and you should not need a manual to signal a left turn.

DC, AC and GROUND Coupling Settings on the Oscilloscope

A student stopped me in lab last week. She was staring at a trace that kept drifting off screen. She had the coupling set to DC and a large offset was pushing her signal out of range. The question she asked was a good one: what is the difference between AC, DC, and ground coupling, and when do you use each?

The short answer is that coupling controls what the oscilloscope passes to its vertical amplifier before it draws anything on screen. With newer scopes from different manufacturers it is sometimes difficult to find where to set coupling so you can either hunt around (usually the way I do it!) or consult the user manuals.The choice changes what you see, and getting it wrong means you either miss something or start chasing a problem that is not there. Here's a pic with short definitions for each following.

DC Coupling

DC coupling passes everything through: the full signal including any DC offset. If you have a 10 mV sine wave sitting on top of a 12V supply, you see both. This is the correct default for most measurements. Power supply work, logic signals, anything where the offset carries information requires DC coupling. You want the complete picture. That student had the scope set to DC Coupling (often the default.)

AC Coupling

AC coupling inserts a series capacitor that blocks DC and attenuates frequencies below roughly 10 to 20 Hz, depending on the scope. That same sine wave now appears centered on zero, and you can use full vertical scale to examine it. This is useful for noise on a supply output, audio signals, or serial data eye diagrams where the DC level is irrelevant. The tradeoff is real: the capacitor charges and discharges, so the trace drifts for a few seconds after you switch modes. Square waves also suffer at low frequencies because the flat tops droop as the capacitor charges through the input impedance.

Ground Coupling

Ground coupling disconnects the input entirely and connects the vertical amplifier to ground. The signal disappears. You get a flat line at 0V. This is a calibration step, not a measurement mode. Use it to find your ground reference position before you connect a live signal, especially when you need to compare multiple channels on the same scale.

The student switched to AC coupling, centered the trace, and saw exactly what she wanted to see: about 80 mV of sine wave.

A Different Path to Quantum Computing Hits 99% Accuracy

Two research teams, one in Germany and one in Switzerland, published results in Nature on the same day showing quantum logic operations with better than 99% accuracy. The technique they used has been worked on for almost thirty years.

To get a quantum computer to do anything, you have to make individual atoms interact in a controlled way. Most neutral-atom companies do this by hitting atoms with lasers to push them into a high-energy state. The atoms interact, then the state decays quickly. The operation has to finish before the decay.

These two teams used a different method. They cooled lithium atoms to near absolute zero, trapped them in a grid made of laser light, and let neighboring atoms overlap slightly. No high-energy state needed. The atoms touch in a controlled, predictable way, and that touch becomes the logic operation. The ETH Zurich group and the Max Planck group used different control schemes and landed at similar fidelities.

The teams also reported Bell-state lifetimes over 10 seconds. A Bell state is a pair of qubits locked together so that measuring one determines the other, no matter how far apart they sit. The lifetime is how long that link survives before noise breaks it. Many quantum platforms measure this in microseconds or milliseconds. Ten seconds is a long window, and it matters because every gate operation has to finish before the link decays.

Lithium matters here for a specific reason. Its electrons follow a rule that no two of them can occupy the same state at the same time. That rule, built into the physics, prevents a whole category of errors automatically. The hardware handles some of the error checking that software would otherwise have to do.

The likely first use for machines like this is quantum chemistry. Simulating a drug molecule or a battery material means simulating how electrons behave. Lithium atoms in this setup follow the same rules as those electrons, so the hardware and the problem use the same physics. The phys.org writeup has details.