Quantum neural networks get their first hardware test
Neural networks have transformed how machines find patterns in data, from recognizing faces in photos to predicting the shapes of proteins. So far, all of this progress has been made on ordinary classical computers, but with quantum computers now edging into practical use, there is a real possibility that neural networks could tap into distinctly quantum effects and operate in ways that classical machines never could. So far, however, neural networks have proven far more difficult to run on quantum hardware.
Through new research published in Physical Review Letters, Djamil Lakhdar-Hamina and colleagues at the University of Maryland, College Park, have built a neural network that runs on two different types of quantum computer, allowing them to test directly whether these systems can live up to their theoretical promise.
Elusive quantum advantage
A neural network is built from layers of simple units, each taking in signals and passing on an output depending on what it receives. To train a network, the connections between these units are adjusted until the network reliably produces the right answer for a given task.
In the quantum world, a similar structure can be built using qubits: the basic unit of quantum information, whose measurement outcomes stand in for the signals passed between layers. Researchers have long suspected that quantum versions of these networks could offer genuine advantages over classical ones, perhaps by exploiting quantum uncertainty. However, very few of these ideas have actually been tested on physical devices.
Sweet spot in noise
Lakhdar-Hamina's team addressed this by training a network in the usual classical way, then running the finished network's decision-making step on quantum hardware. This network can also be dialed between two extremes: a purely classical mode and a fully quantum one, where measurement uncertainty plays a larger role.
Crucially, they ran the same network on two very different quantum platforms: one storing information in trapped ions, the other in superconducting circuits.
The team tested the network on the classic task of recognizing handwritten digits. By introducing a moderate amount of quantum uncertainty, they found that its accuracy actually improved compared with the fully classical mode—creating a sweet spot in reliability before further added noise made the results more unreliable.
Benchmark for performance
More strikingly, for images the classical network identified incorrectly, the quantum version sometimes got them right, seemingly nudged toward the correct answer by the hardware's own physical noise. By deliberately adding extra operations that should cancel out in a perfect circuit, the team could compare how the two types of quantum hardware responded differently to noise.
The results suggest noise isn't simply something to be eliminated in quantum machine learning. Handled well, it can sometimes push a network's decisions in a useful direction.
As quantum computers grow larger and better controlled, this kind of benchmarking will matter for determining which architectures are worth developing further and whether quantum neural networks might one day tackle problems beyond the reach of classical computers altogether.
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Publication details
Djamil Lakhdar-Hamina et al, Benchmarking a Tunable Quantum Neural Network on Trapped-Ion and Superconducting Hardware, Physical Review Letters (2026). DOI: 10.1103/9bp2-42v3. On arXiv: DOI: 10.48550/arxiv.2507.21222
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Citation: Quantum neural networks get their first hardware test (2026, July 27) retrieved 27 July 2026 from https://phys.org/news/2026-07-quantum-neural-networks-hardware.html
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