Where the Jobs Actually Are

Some graduates spent the 2026 commencement season blaming AI for a job market that shut them out, loud enough that tech executives got booed at graduation ceremonies over it. Recruiters tell a different story.

Matt Walsh, CEO of the Phoenix search firm Blue Signal, works semiconductor hiring daily and says the problem isn't automation. "There aren't enough people," he says. The United States is heading toward what labor economists call the largest workforce shortage in its history, and it shows up hardest in the fields that build things.

The semiconductor industry expects to add close to 115,000 jobs by 2030. The Semiconductor Industry Association projects a shortfall of 67,000 technicians and engineers to fill them. That gap sits squarely in associate degree and bachelor's degree technical programs, not in the AI research labs getting most of the headlines.

Construction and the skilled trades show the same pattern. Branka Minic, CEO of the Building Talent Foundation, says fewer than half the workers needed in construction are entering the field, even with starting wages hitting $50 an hour in some markets. College graduates aren't matching that pay in comparable years of training.

Cybersecurity tells a similar story. CyberSeek, the workforce tracker built by CompTIA and NIST, counts hundreds of thousands of open cybersecurity positions in the U.S. against a supply of qualified workers that consistently falls short. The roles span network defense, security operations, and incident response, and they don't require a computer science PhD. A two-year degree with the right certifications gets a candidate into the field.

This is good news if you're building a technical career instead of chasing a headline. Employers in semiconductors, cybersecurity, advanced manufacturing, robotics, and skilled trades are competing for candidates, not filtering through thousands of applicants for one opening. Two-year technical programs, apprenticeships, internships and engineering degrees put graduates directly into that competition.

States have noticed too: several are merging workforce and higher education agencies or offering loan payoff incentives to pull people into these pipelines.

The AI panic makes for a cleaner headline than a demographic and skills pipeline problem. But the demand for people who can build, install, test, and maintain physical systems is not shrinking. It's the part of the labor market with the fewest applicants and the most openings.

ETH Zurich Builds Quantum RAM Out of Vibrations

Chapter 5 of Quantum From The Ground Up covers superconducting qubits by way of Josephson junction fabrication and IBM's 1,121-qubit Condor chip. It never had to answer a basic architecture question: where a superconducting qubit puts its data when it isn't actively working on it. Researchers at ETH Zurich just built an answer, and it doesn't look anything like a normal memory chip.

A team led by physicist Yiwen Chu, head of ETH Zurich's Hybrid Quantum Systems Group, split a quantum computer into the same two roles a laptop uses: a processor and a separate working memory. The design uses a superconducting transmon qubit as the processor and a mechanical resonator as memory, on a chip package 7.5 millimeters long. Instead of storing a qubit's state electromagnetically, the resonator holds it as a mechanical vibration, the way a guitar string holds a note, except this vibration follows quantum rules rather than classical ones.

Each resonator supports several distinct vibrational modes, and each mode works as its own memory slot. To run a computation, the qubit reaches into the resonator, pulls out a stored vibration, modifies it, and writes it back. Doctoral students Yu Yang and Igor Kladarić built the hybrid chip alongside Chu. The team validated the architecture by running a Quantum Fourier Transform and a period-finding algorithm on the hybrid chip, published in Science. That marks the first demonstration of mechanical resonators executing real quantum algorithms rather than just holding a state.

Superconducting qubits pack in tightly, but that density crowds out room for data. Electromagnetic memory schemes have historically traded a smaller footprint against coherence time. Mechanical resonators split that trade differently, offering higher storage density and longer coherence in less physical space. the approach still has to prove it scales beyond a single test chip, and Chu's group is continuing the work with that goal in mind.

What This Changes in the Book

Chapter 5's numbers don't move. IBM's Condor still holds at 1,121 qubits and 99.0 to 99.5 percent two-qubit fidelity, and nothing here challenges either figure. What changes is the chapter's scope. Chu's result adds a memory subsystem to the superconducting platform, a second engineering problem the chapter didn't previously address. It's a proof of principle, not a shipped component. A chip built for one qubit and one resonator still has to prove itself when both categories multiply.

This post will fold into the next edition of Quantum from the Ground Up, due September 1. The current edition is free to read at gordostuff.com/p/quantum-from-ground-up-hardware.html, and if it's useful to you, a coffee at ko-fi.com/gordostuff keeps it updated.

Dad and Doug

Lost my father and a close friend, Doug, within months of each other last year. Dad was fine right up until the last few weeks. Doug had been fighting for about five years. I wrote about each of them here already (for Dad, and for Doug), but never posted the obituaries themselves.

This blog has been mostly tech since 2005. But some things need to get written down while I still can, and this is one of them. Miss them both.

Write things down while people are still around to read them. The obituary is the last thing anyone writes about most of us. Everything before it is still yours to say.

The Free Path Into Quantum Work

The entire IBM Quantum Learning catalog is now open to the public, no partner account or IBM Quantum Network membership required. The catalog runs past ten courses, from the basics of qubits and circuits through algorithms for factoring and search, up to a course built around running experiments on processors with 100 or more qubits, all hosted free on the IBM Quantum Platform.

Path from free IBM Quantum Learning courses to certification to a quantum workforce role

The IBM Certified Quantum Computation using Qiskit v2.X Developer - Associate is a single exam built on the same Qiskit SDK and Qiskit Runtime the free courses teach. Since the program launched in 2021, more than 1,300 people across 71 countries have passed it. The exam runs up to $200 depending on location, with an optional $30 practice test through Pearson VUE. The preparation runs $0.

This matters most for programs without a quantum lab or a research grant behind them, which describes most community colleges and a fair number of universities. Qiskit needs Python and a working knowledge of linear algebra. Nothing else.

Every one of my students has a laptop and an internet connection. That's all you need to get started today.

This post rolls into the next edition of Quantum from the Ground Up, due September 1.

The Other Half of the Emerging Quantum Workforce

Chapter 2 of Quantum from the Ground Up covers the Massachusetts side of quantum
workforce building: over fifty million dollars in state investment, a quantum supply chain accelerator complex in Springfield, Massachusetts and a three tier pyramid running from technicians to PhDs. That chapter answers one question. How do you train people for jobs that barely exist yet? It does not answer a second question. Once someone is trained, where do they get time on a real quantum computer?

The federal government's answer is a program most people outside national labs have never heard of. The Quantum Computing User Program, or QCUP, runs out of the Oak Ridge Leadership Computing Facility. It owns none of the hardware it gives access to. Instead it brokers competitive, merit reviewed time on commercial quantum processors from vendors including IBM, Quantinuum, and Rigetti, for researchers doing open, fundamental science.

The mechanism is simple. A researcher submits a project proposal explaining what they want to run and why it needs quantum hardware. The Quantum Resource Utilization Council and independent referees review it for merit. Once approved, every user on that project applies for an account and gets assigned a Scientific Liaison, someone who understands both the science domain and the hardware, to help them get past the parts of quantum programming that have nothing to do with their actual research question.

The growth numbers are accelerating. A 2024 survey of the program found it had grown from 52 projects and 117 users in 2020 to 80 projects and 271 users by the end of 2023. Users range from national lab veterans to graduate students running their first circuit. Most projects are proof of principle work, not production science, which is exactly what you would expect from a field still figuring out what its hardware is good for.

Where the Access Turns Into Results

The hadronization work covered in my previous post, Anthony Ciavarella's simulation of quark binding on an IBM Heron processor, ran entirely through QCUP access. That is not a coincidence. It is the point of the program. Give enough researchers enough time on real hardware and some of them will publish in Physical Review D.

Demand is outrunning the obvious ways to measure it. The 2026 Quantum Computing User Forum at Oak Ridge, running July 20 through 24, closed registration before the event even started. A Fall 2026 hackathon open now for proposals gives existing users priority and asks new applicants to already hold an allocation before an August 7 deadline. This is not a program short on interested researchers.

What This Changes in the Book

Chapter 2 currently frames quantum workforce building as a state level, training focused story: Massachusetts building capacity so people are ready when jobs appear. QCUP is the federal, research access side of the same pipeline, and the book does not mention it yet. The two are not competing models. A pipeline that only trains people without giving anyone hands on time on real hardware produces graduates who have never run a circuit outside a simulator. QCUP is where some of that hands on time actually happens, for the research end of the workforce rather than the classroom end.

The next edition will add QCUP to Chapter 2 as the research access counterpart to the state workforce investment already covered there, with the growth numbers above and a note that at least one book worthy physics result, the Ciavarella hadronization paper, came directly out of that access.

This post will be folded into the next edition of Quantum from the Ground Up, targeted for September 1. The current edition is available at gordostuff.com/p/quantum-from-ground-up-hardware.html

A Quantum Computer Watches Matter Form From Scratch

Chapter 11 of Quantum from the Ground Up covers an IBM quantum chip that helped simulate a large protein molecule in May 2026. A new result uses a chip from the same family for something further from everyday life: watching the pieces of an atom's nucleus form.

Here’s some background - protons and neutrons, the particles that make up the nucleus of an atom, are not the smallest things out there. Each one is built from smaller particles called quarks, held together by a force called the strong force. Physicists call any particle built from quarks a hadron. The process of quarks locking together into a hadron is called hadronization. It happens constantly inside stars, inside particle colliders, and, billions of years ago, in the early universe. Nobody has ever watched it happen step by step, because it unfolds too fast and too small to see, even with instruments as powerful as CERN's Large Hadron Collider.

Anthony Ciavarella, a scientist at Lawrence Berkeley National Laboratory, used 104 qubits on an IBM quantum processor to simulate a piece of this process instead of observing it directly. He reached the machine remotely through a Department of Energy cloud access program called QCUP, rather than owning the hardware himself. We’ve described qubits in past posts - the basic unit of information in a quantum computer, similar to a bit in a regular computer, but able to hold more complex states. More qubits generally means a computer can model a bigger or more detailed problem.

The number 104 is an interesting choice and not a hardware limit. IBM's chip has 156 qubits, and Ciavarella used only some of them. In his setup, each qubit stands in for one point on a one-dimensional line of quark positions, so the qubit count is really a choice about how long a line to simulate. He picked a line of 104 points, long enough to fit a stretched gluon string spanning most of it, so something called the snap could happen out in the middle where it is easy to see clearly, rather than run into the edge of the simulation before it finished stretching.

The specific piece he simulated is called string breaking. Quarks are linked by something physicists describe as a string made of particles called gluons. Pull two quarks apart and that string stretches, the way a rubber band stretches, until it holds so much energy that it snaps. When it snaps, the energy does not just disappear. It turns into a brand new pair of quarks, which is how new hadrons get built. Ciavarella simplified the problem by using heavier quarks, which move around less and are easier to track, and he set up his simulation using a method he helped develop for preparing a quantum computer's starting state cleanly.

The simulation's answer matched what earlier work on ordinary supercomputers had already found. That match is the actual achievement here. Nobody is claiming the quantum computer beat a classical one. The result, published in Physical Review D, shows that a real quantum computer can reproduce a calculation even though the model was simplified down to one dimension. The data also hinted that the gluon string might briefly act like a heated gas right before it snaps, a detail worth checking again once the simulations get less simplified.

Why This Matters

Quantum computers are noisy. Every operation carries a small error, and those errors add up as a circuit grows. Nobody trusts a quantum result on a problem nobody can already solve until the machine first proves it gets the right answer on a problem people already know. Matching the classical calculation is that proof. It confirms the way Ciavarella mapped quark positions onto qubits, prepared the starting state, and corrected for hardware noise all worked correctly on real hardware, not only on paper.

Ciavarella's model used heavy quarks in one dimension because that version is still solvable classically. Light quarks, three dimensions, and watching a collision unfold in real time rather than checking a single snapshot are where classical computers run out of room, since those calculations carry exactly the kind of entanglement a regular computer cannot represent efficiently. That regime is also the one closest to what actually happens inside a real collision at a facility like the Large Hadron Collider. As Ciavarella put it, physicists already have the theory for hadronization; they have lacked a way to calculate predictions from it. This result is the first step toward a machine that can.

This was not the first attempt at something like this. In 2024, some of the same researchers, working with the University of Washington and Berkeley Lab, used 112 qubits to build and track a hadron directly, watching it move over time in an earlier, related model on an earlier version of this same IBM chip. The 2026 result trades that moving picture for a sharper look at the single moment when a hadron forms. 

What This Changes in the Book

Chapter 5, which covers this family of IBM chips, gets a new data point: the same chip line now has a published result in particle physics, using 104 qubits, on top of the protein simulation already covered in Chapter 11. Chapter 11's claim that this hardware has uses beyond biology gets a second example, and a more direct one, since this simulation ran entirely on the quantum processor rather than splitting the work between a quantum computer and a classical one.

This post will fold into the next edition of Quantum from the Ground Up, due September 1. The current edition is free to download at gordostuff.com/p/quantum-from-ground-up-hardware.html.

The Quantum Chandelier

Photo MIT Technology Review

You've probably seen a photo like this - a tower of gold-plated discs wrapped in loops of wire, narrowing toward a point at the bottom. Google, IBM, and most quantum computing press releases use some version of this image. People call the assembly the chandelier, and it looks like the computer. It isn't. It's the life support system for a chip you can't even see in the photo.

Start with what's actually inside a quantum computer. A qubit is a physical device, usually a tiny loop of superconducting metal, that can hold a mix of two states at once instead of a single 0 or 1. That mixed state is fragile. A stray photon, a vibration, or a few millikelvin of extra heat can collapse it before you get a useful calculation out of it. Physicists call that collapse decoherence, and it's the central engineering problem in the entire field. Keeping decoherence at bay is the whole reason the chandelier exists.

Why It Has to Be That Cold

At room temperature, everything around a chip is radiating heat as stray photons, trillions of them, bouncing around and hitting anything nearby. For a normal computer chip that's irrelevant. For a superconducting qubit it's fatal, since a single one of those stray photons carries enough energy to flip the qubit's state. Cooling the chip down to the mixing chamber stage, near 10 millikelvin starves the environment of those stray photons and also lets the qubit's own wiring become superconducting, meaning it carries current with zero electrical resistance. Both effects are required. Without one or the other, the qubit decoheres in nanoseconds instead of the few hundred microseconds researchers need.

The Five Stages

The gold structure is a dilution refrigerator, built from stacked stages that grow colder toward the bottom. A pulse tube cryocooler, essentially a specialized mechanical compressor, does the first heavy lifting, dropping the system from room temperature to about 40 Kelvin and then 4 Kelvin using compressed helium gas. Below that, the fridge switches to a different method. A chamber called the still boils off helium-3 to reach roughly 0.7 Kelvin, and a series of heat exchangers pushes the temperature down further, through about 0.1 Kelvin, to the mixing chamber at the very bottom. A mixture of helium-3 and helium-4 drives that last stage. The full cooldown from room temperature to base takes 24 to 48 hours, and it has to happen every time the system needs to be opened for maintenance.

Each gold disc in the photo is one of these stages, plated in gold because gold conducts heat well, resists corrosion, and doesn't interfere magnetically with the qubits. Each stage nests inside the next, shielding the colder one below it from the warmer one above.

Figure: the chandelier's cooling stages, colored from gold at the warmest to blue at the coldest, with the qubit chip mounted at the base.

The Wiring Problem

Every wire running through those stages carries control signals down to the qubits and readout signals back up to room-temperature electronics. A signal heading down gets attenuated at each stage on the way, stripping out electrical noise picked up from the warmer stages above. A signal heading up gets amplified, since the qubit's own readout signal is too faint to detect at room temperature. Every one of those wires is also a heat leak. Heat travels down a wire just as easily as a signal does, and the cooling power at the base stage is measured in microwatts, barely enough to warm a fraction of a grain of rice. A single wire that's improperly thermalized can add more heat than the entire fridge can remove.

That tradeoff is why wiring, not the qubits themselves, has become one of the field's biggest scaling obstacles. Each qubit needs its own set of control and readout lines, so the number of cables required grows directly with qubit count, eventually exceeding what a single cryostat can physically hold. Labs are now racing to move some control electronics inside the fridge itself, onto chips that can survive the cold, so fewer wires have to cross from room temperature down to the millikelvin stage. A single dilution refrigerator system runs one to five million dollars, and most of that cost is solving this wiring problem.

What's Actually Quantum

The qubit chip itself sits at the very bottom, bolted to the mixing chamber, a few millimeters across. It's small enough to miss in most photos of the chandelier, which is the point. Everything above it, every gold disc, every coil of coax, every attenuator and amplifier, exists for one reason: to keep that small chip cold and quiet enough to hold a quantum state long enough to be useful. Chapter 3 of Quantum from the Ground Up makes this argument directly: physics works, engineering makes it work. The chandelier is that argument built out of gold-plated copper and helium-3.

Next time a chandelier photo turns up in your feed, look past the wiring for the small chip at the bottom. That chip is the entire quantum computer. Everything else in the picture is plumbing.

This post will fold into the next edition of Quantum from the Ground Up, out September 1.