Q-CTRL announces a successful practical quantum advantage experiment
The company shows a 3,000x time savings in a materials science calculation compared to the best-in-class classical alternative.
On 6 May 2026, Q-CTRL, the Australian quantum control software (and quantum sensing) vendor, announced [1] they had achieved “practical quantum advantage” for a materials science experiment. The company defines practical quantum advantage precisely in their announcement. My take is that Q-CTRL has achieved a degree of advantage that is qualitatively different (and better) than other “commercial quantum advantage” announcements to date.
Simulating highly correlated electrons is useful in understanding how materials behave
Q-CTRL’s experiment [2] was run on the IBM Quantum Platform, a quantum computing (QC) cloud service, using Q-CTRL’s Fire Opal quantum control software to optimize the QC’s performance beyond what IBM’s native Qiskit toolset can do. The experiment performed a “1D Fermi Hubbard” calculation both on the QC and on the best-in-class classical computing alternative. This classical alternative was the ITensor software package of a TDVP (time-dependent variational principle) solver run on a 32vCPU instance with 64GB of RAM in the AWS cloud.
The Fermi Hubbard model provides a key means of simplifying and simulating the behavior of highly correlated electrons in a material. Electron behavior, in terms of characteristics like how much energy they have as they move around and how strongly they repel each other, are central to understanding how a material behaves. Many of the interesting questions we have in materials science involve highly correlated electrons. This is the case, for example, in the creation of high temperature superconductors or improving battery chemistry. However, the high degree of correlation makes simulation exponentially more resource intensive as simulation size grows. This resource requirement growth is challenging for classical computers but theoretically well-suited for quantum computers, which compute in a way that naturally models the exponential complexity growth of quantum mechanical systems.
Q-CTRL showed impressive results versus the best-in-class classical alternative
The headline result Q-CTRL achieved was to reduce the time needed to perform the calculation by 3,000x compared to the classical alternative. Specifically, Q-CTRL needed only about two minutes to run a QC simulation compared to over 100 hours in the AWS cloud. When you consider that a real-world experiment would likely involve many simulation runs it’s clear that the time-savings quickly multiply. This could move the time domain advantage needle from “helpful” to “essential” in performing an experiment.
Q-CTRL achieved this result in two main ways:
First, the company customized the human-written software code to run precisely on the exact hardware used in the experiment. This was done automatically with the Fire Opal software, not fine-tuned by hand as is usually the case in QC.
Second, Q-CTRL was able to use error suppression techniques to reduce errors in real time. This approach contrasts with the error mitigation approach, in which errors are post-selected out of the results after the simulation run. The challenge with post-selection error mitigation is that it doesn’t operate in real time and the time needed can grow exponentially, which negates the time advantage that Q-CTRL’s approach delivered.
By customizing the code so precisely and performing error suppression, Q-CTRL was able to reduce the number of two-qubit gates (a unit of operation) by 60% compared to the native IBM Qiskit implementation. In effect, Q-CTRL increased the “resolution” performance of the IBM QC to the point that it was able to successfully run the Fermi Hubbard calculation at a size the best-in-class classical computer couldn’t match.
What really caught my eye about the announcement is how Q-CTRL benchmarked its results. They specifically measured against an objective best-in-class solution and derived a result that is not just slightly better but orders of magnitude better than best-in-class classical solution.
The results fall under what I think of as “commercial advantage” in contrast to “computational advantage.” As I mentioned in a previous article, “commercial advantage” is when the advantage is subjective: the customer adopting the solution decides if it offers an advantage over what the customer would otherwise use. “Computational advantage” is the case where the QC has a mathematically provable advantage against any possible classical alternative, now or in the future.
The challenge with commercial advantage is how subjective it is. For example, as with the case of the SQC Watermelon offering that I wrote about previously, the customer may feel they are benefitting from an advantage, but compared to what? Maybe the customer is comparing to a classical solution that could easily be beaten by another, superior, classical solution. Maybe the advantage only shows up in highly specific circumstances that wouldn’t apply to most other potential adopters.
In the case of Q-CTRL’s announcement, the results are unambiguously better (by orders of magnitude) than the best-in-class classical alternative. The company carefully acknowledges that future classical computing innovation could change this comparison but based on the current technology available to anyone wanting to perform this calculation, the Q-CTRL approach is, on a time basis, the best you can possibly achieve. Of course, customers don’t base purchase decisions on time savings alone, but still, Q-CTRL’s results are wholly different in their degree of objective advantage compared to other current examples of commercial advantage.
Going forward: next steps
Q-CTRL’s results were announced in a pre-print paper and still need to be peer-reviewed. One issue that faces QC in general is verification of the QC’s results: after all, the whole point of QC long term is to perform calculations not possible on a classical computer. How do you double-check that the QC in your experiment is giving correct answers if the classical computer can’t replicate the results as the problem size grows larger? Q-CTRL acknowledges this issue, and very importantly, the time advantage mentioned above is specifically noted for the last point at which the classical computer can “keep up” with the QC; the last point of agreement. Longer term, specifically for quantum mechanical simulation use cases, a useful check will be “proof by physics” when, for example, materials are created based on QC calculations. If they work as expected, the QC’s “correctness” is “proved.”
A second issue that is more specific to Q-CTRL’s experiment is whether, how well, and how quickly the company’s approach in this experiment will scale up for real world use by material science researchers. What I mean is, the company’s experiment was conducted in one dimension, basically along a string, in effect. But materials interesting to researchers are more often in two dimensions (sheets) or three dimensions (cubes, etc.). Of course, it’s very exciting just to show a real example of QC practical advantage, even at a starting scale. It may or may not be a smooth linear line of progress from here to solving room temperature superconductivity.
Citations
1. Q-CTRL, “Practical quantum advantage signals a new commercial era for quantum computing,” https://q-ctrl.com/blog/practical-quantum-advantage-signals-a-new-commercial-era-for-quantum-computing, May 6, 2026.
2. Gavin S. Hartnett, et al., “Fast, accurate, high-resolution simulation of large-scale Fermi-Hubbard models on a digital quantum processor,” https://arxiv.org/abs/2605.04025, May 5, 2026.