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.

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.

Unscrambling Quantum: UCI Finds a Way to Recover Lost Information

I have been learning and writing quantum computing for the past several months, and scrambling is another one of those interesting concepts. Textbook explanations are technically correct: information encoded into qubits spreads across a chip, keeps spreading, and eventually becomes unrecoverable. What is not said - this is not a bug in a specific system. It is a fundamental feature of how quantum information behaves. And until recently, the answer to "can you get it back" has been no.

A team at UC Irvine published a paper this month in Physical Review Letters that changes that answer. Professor Thomas Scaffidi and graduate student Rishik Perugu found a method to reverse quantum scrambling, not just in theory but as a demonstrable property of real quantum systems.

Scrambling happens when locally encoded qubit information diffuses across a chip faster than it can be tracked. The standard framing treats this as a one-way door in practice, even though the underlying physics is technically time-symmetric. At the microscopic level, particle interactions run forward and backward with equal validity. Watching two particles collide on film; the footage makes equal sense played either direction. Scaffidi's team leaned into that symmetry and showed it holds broadly across many quantum systems, including quantum computers.

Perugu's calculations demonstrated that a precisely tuned intervention can drive a scrambled system backward, refocusing dispersed information near its origin. Scaffidi is direct about the requirements in the paper: extremely fine-tuned control at the system level. This is not a software fix. It requires hardware precision that remains an open engineering problem at scale.

The work has direct relevance to quantum error correction and quantum memory. Collaborators include Michael Flynn at BlocQ and Bryce Kobrin at Google. Scaffidi's lab is funded by a U.S. Department of Energy Early Career Research Program Award. The full UCI press release has the details.

I'm learning quantum as an engineer, not a physicist. That framing actually helps - at least for me. Reversing scrambling is not a physics curiosity anymore; it is an engineering problem. The mechanism exists, the math checks out, and the path is reversible in principle. What remains is building hardware with the precision and control to execute it at scale. That is exactly the kind of problem engineers solve. It is also, to be direct, a very hard one. But it is a different kind of hard than "this cannot be done."

Can Seagrass Health Indicate Environmental Stress?

Del Mar College’s Natural Sciences Department has made providing research opportunities to students a priority through the Minority Science and Engineering Improvement Program (MSEIP), funded by the U.S Department of Education, and has partnered with Texas A & M University-Corpus Christi (TAMU-CC) and other area institutions to give participants hands-on experiences by working with researchers and graduate students.

In this interview, done at the 2008 National Science Foundation Advanced Technological Education conference held last October in Washington, DC, Del Mar College student Nicolas Perez discusses work he did in the summer of 2008 related to research conducted by TAMU-CC associate professor of molecular and plant sciences, Dr. Kirk Cammarata. Under the supervision of Dr. Kirk Cammarata, three Del Mar College students were paired with TAMUCC- graduate student mentors and focused on separate research projects related to the study of sea grass health.




You can get more information on Delmar College, TAMU-CC and Nicolas' project here.

I enjoyed talking with Nicolas and all of the other students that attended the conference. I had the opportunity to interview a few more of them and will be posting those interviews here on occasion. If you want, you can see them all now on our Center YouTube channel linked here.