A Real Physics Research Question, Answered on a Quantum Computer
- Aug 4
- 4 min read
What the IBM–Qedma paper means for investors: not commercial advantage, but early evidence that quantum computers can become useful research tools
The quantum-computing industry has spent years looking for quantum advantage: the point at which a quantum computer can perform a calculation beyond what the best known classical methods can reliably do. For investors, it is often treated as a turning point for usefulness, and eventually commercial viability.
One of the most prominent places to look for it is physics simulation. Quantum systems become extremely difficult for classical computers to represent as they grow, while quantum computers operate according to the same underlying rules.
That is what makes the IBM–Qedma result interesting. Qedma used error-mitigation software on IBM hardware to test whether an unexpected oscillation in a driven quantum magnet survives as the system grows. The experiment had to overcome two limits at once: classical simulations stopped producing reliable results at the relevant scale, while noise quickly degraded the quantum computer’s output. The result does not establish durable quantum advantage, but it shows a quantum computer contributing useful evidence to an open physics question.
What IBM and Qedma found
Last Thursday, IBM announced three quantum papers – one with Qedma, one with Algorithmiq and one with a team from the University of Chicago – framing them in the press as demonstrations of quantum advantage.
One of the three papers reports research was led by Qedma, an Israeli startup that develops software for mitigating errors in quantum computers. The paper makes a narrower claim than IBM’s announcement: an error-mitigated quantum computer simulated a driven quantum magnet at sizes and times where the classical methods tested no longer produced reliable results. The larger-system data provided evidence that an unexpected oscillation may persist as the system scales.
The experiment simulates a model of a quantum magnet driven by a repeating pulse. Systems of this kind absorb energy and approach a featureless thermal state. Over the measured window, however, this magnet’s magnetization kept oscillating in a regular pattern through thirty cycles, at a rhythm roughly four to five times slower than the drive.
The open question was whether that oscillation is real physics or an artifact of a small system. For this finite-size analysis, exact classical simulation reached 35 qubits, and across those sizes the oscillation shrank – but not in a way that revealed whether it was heading toward zero or toward a fixed nonzero value. The 51- and 74-qubit hardware runs supplied the missing points and, within the paper’s finite-size model, the trend then favoured a nonzero limit. The oscillation appears to survive at large system size. Two distinct mitigation estimators agreed over their shared range, and selected points produced consistent results on Quantinuum’s trapped-ion machines.
Why this is a different kind of claim
What makes this interesting is that the problem was worth solving on its own. The oscillation question was open, and the answer is a contribution to physics whether or not a quantum computer produced it (unlike a sampling benchmark, whose primary value lies in the comparison being run). Here the machine was pointed at a real question and returned something usable.
That this works in physics is not surprising. Researching quantum systems was one of the original proposed uses for quantum computers, because the machine and the problem share the same structure. If quantum computing becomes useful before fault tolerance, this is where many people expected it to happen first, which makes this less a benchmark result than an early instance of the technology being used for something meaningful.
What that does and does not imply
It is not a claim of exponential speedup. The paper concludes with the following:
“The above comparison suggests that error mitigation enables quantum computing to access a regime that is not reliably reproduced classically for the problem at hand. This observation should be distinguished from an asymptotic, complexity-theoretic separation […] Better classical algorithms or problem-specific strategies may nevertheless reproduce these results …”
The paper is clear that error mitigation cannot deliver a lasting separation from classical computing: its cost grows exponentially with the noise accumulated by the circuit. The original method became prohibitively expensive beyond cycle 16, so a cheaper heuristic method carried the experiment through cycle 30.
Nor does the result imply that no classical algorithm will catch up. Four leading classical methods were tried, and each broke in an identifiable way, but that is a statement about four methods at a given compute budget, not about what is possible. The instance has been published with an open invitation to challenge it. The boundaries are narrow in other ways too: one family of lattice geometries, intermediate timescales and no theoretical account yet of why the oscillations occur.
What follows commercially
If scientific research proves to be the near-term wedge for quantum computing, potential customers include national labs, university groups and corporate R&D rather than mainstream enterprise users.
An interesting commercial signal might lie in the error-mitigation part. Without it, this experiment loses accuracy by the fourth of thirty cycles; everything beyond that point depends on software correcting for hardware noise. A simplified version also produced consistent results on Quantinuum’s hardware, providing an early signal of portability.
To sum it all up, a quantum computer supplied the larger-system evidence behind an observation that now requires a theoretical explanation. While this does not establish durable quantum advantage or commercial ROI, it is an important signal that quantum computers are beginning to produce useful research results, and that value may accrue to the software layer that makes those results reliable.
Itamar Fink is a Research Intern at Qbeat Ventures and a Master’s student in Computer Science at LMU Munich, focusing on quantum technologies and their emerging applications.
