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.
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