A team of researchers has carried out the largest quantum computing experiment to date for image loading and classification on real-world datasets, running variational quantum circuits on utility-scale machines from IBM and Quantinuum and demonstrating that some deployed models can classify images with better than 90 percent accuracy despite operating within the noise limits of today’s hardware. The work, published in Quantum Machine Intelligence, was conducted by scientists at BlueQubit Inc. and Honda Research Institute USA and marks a significant step toward practical quantum-enhanced computer vision.
One of the central bottlenecks in quantum machine learning is deceptively simple to state: before a quantum computer can learn anything about an image, the image must be loaded into a quantum state. Classical data lives as arrays of numbers, while a quantum computer operates on qubits whose state is described by complex amplitudes. Encoding a classical image faithfully into those amplitudes is itself a computationally demanding operation. The most direct method, known as exact amplitude encoding, requires circuits whose depth grows exponentially with the number of qubits, quickly overwhelming hardware whose gate fidelities, while improving, remain finite. The new study confronts this data-loading problem head-on and shows that approximate, learned encodings can be both tractable and useful on real devices.
The researchers extended a hierarchical learning framework, previously developed for training large-scale variational quantum circuits, to the task of approximate amplitude encoding of images. Rather than demanding a perfect quantum representation of every pixel, the method trains parameterized quantum circuits to approximate the target state, accepting small infidelities in exchange for dramatically shallower circuits. The team also explored block amplitude encoding, in which different parts of an image are encoded in a tensor product of smaller quantum states, allowing images to be distributed across multiple qubit registers in a modular fashion. Both strategies were applied to digits from the MNIST dataset of handwritten numerals and to road scenes from the Honda Scenes dataset, a collection of driving imagery recorded for autonomous vehicle research.
For comparison, the team also analyzed classification performance under piecewise angle encoding, a lighter-weight encoding strategy in which pixel or feature values are baked into rotation angles of individual qubit gates. Angle encoding avoids the costly state-preparation overhead of amplitude encoding but embeds the data differently in the quantum feature space. By benchmarking classifiers built on both encoding families, the study offers one of the clearest experimental pictures yet of the trade-offs between encoding fidelity, circuit depth and classification accuracy in near-term quantum machine learning.
The experimental pipeline was substantial. The team first performed simulations and orchestrated the training workflows on the BlueQubit platform, using PennyLane for circuit construction and adjoint differentiation, and Nvidia H100 GPUs along with the cuQuantum library for high-performance simulation of circuits beyond 20 qubits. With this setup, circuits containing 720 trainable parameters on 20 qubits could be trained for 1,000 iterations in roughly 300 seconds, a pace that made systematic hyperparameter exploration feasible. Only after validating the workflows in simulation did the researchers deploy their loaders and classifiers on actual quantum processors.
The hardware deployment spanned two very different quantum computing platforms. On the superconducting side, the team used IBM’s 27-qubit Algiers, 127-qubit Brisbane and 156-qubit Fez processors, which feature median two-qubit gate fidelities above 99 percent and single-qubit fidelities above 99.9 percent. On the trapped-ion side, they employed Quantinuum’s H1 and H2 chips, whose qubits are slower to operate but offer all-to-all connectivity and exceptional gate quality, with the 56-qubit H2 matching the fidelity profile of its smaller predecessor. Across these machines, the experiments utilized up to 72 qubits and thousands of two-qubit gates to classify images from the Honda Scenes dataset, making this the largest quantum image classification experiment performed to date on a real-world dataset.
The key finding is that the variational circuits remained sufficiently shallow to operate within existing noise rates. This is no small achievement. Quantum error rates mean that every additional gate layer compounds the risk that the computation dissolves into noise before a measurement can be taken. Deep circuits, even on the best hardware, often produce output indistinguishable from random noise. By keeping circuits shallow through approximate encoding and hierarchical training, the researchers ensured that their deployed models retained genuine signal. Some of the models running on real hardware achieved above 90 percent accuracy on test images, approaching state-of-the-art classical performance while using relatively few parameters.
The hierarchical learning technique itself addresses one of the most stubborn obstacles in variational quantum algorithms: the barren plateau problem. In deep or overly expressive parameterized circuits, gradients of the cost function tend to vanish exponentially with system size, leaving optimization landscapes essentially flat and untrainable. By training circuits in stages, freezing and building upon progressively larger blocks of parameters, hierarchical learning maintains trainable gradients and allows circuit depth to grow in a controlled manner. The success of this approach at the 72-qubit scale, on hardware, suggests it is a viable recipe for scaling quantum machine learning beyond the toy demonstrations that have dominated the field.
The choice of dataset is also noteworthy. MNIST has long served as a standard benchmark, but the Honda Scenes dataset brings the experiment into territory of practical industrial relevance: dynamic traffic scene classification, a task central to autonomous driving. Original images in the dataset measured 1080 by 1920 pixels and were reshaped for the quantum workflows. Demonstrating that quantum circuits can process and classify such imagery on utility-scale processors moves the conversation from abstract benchmarks toward applications where automotive and robotics companies might plausibly care. Honda Research Institute USA co-funded the research alongside BlueQubit, underscoring this applied motivation.
The results arrive at a moment when the quantum computing community is actively debating what useful near-term applications look like. John Preskill’s influential framing of the NISQ era, the period of noisy intermediate-scale quantum devices, emphasized that machines with tens to hundreds of qubits could do interesting things, but identifying those things has proven difficult. Recent theoretical work has even suggested that certain quantum neural network architectures are effectively classically simulable, tempering expectations. Against this backdrop, an experimental demonstration of large-scale quantum image classification with accuracy approaching classical baselines provides concrete evidence that the field is not merely simulating progress but measuring it on real hardware.
Still, the authors’ claims are measured. Above 90 percent accuracy “approaching” classical state-of-the-art performance is not the same as matching or exceeding it, and no quantum speedup is claimed for the classification task itself. What the work demonstrates is feasibility: that data loading, training and inference can all be executed within the noise budget of contemporary quantum processors at a scale never before attempted for this problem class. Whether quantum circuits can eventually offer advantages in expressivity, parameter efficiency or feature-space geometry for computer vision remains an open scientific question, one that the theoretical literature on quantum embeddings and kernel methods continues to explore.
The technical infrastructure developed for the study may prove as consequential as the headline results. The combination of GPU-accelerated simulation for development, cloud orchestration across heterogeneous hardware, and careful circuit engineering for two distinct qubit modalities provides a template for future experimental quantum machine learning studies. The researchers note that data used to construct their plots and tables is available from the authors upon reasonable request, and their software stack, built on PennyLane and the BlueQubit SDK, integrates the hierarchical learning algorithm across multiple hardware connectivities and ansatz choices.
As quantum processors continue to improve in qubit count, fidelity and connectivity, experiments of this kind will serve as the yardstick against which progress is measured. For now, the message from this study is clear: loading images into quantum states and classifying them on real quantum hardware is no longer a theoretical exercise. It has been done, at scale, on two of the world’s leading quantum platforms, with accuracy figures that would have seemed implausible for noisy devices only a few years ago.
Subject of Research: Quantum image loading and image classification using approximate amplitude encoding and variational quantum circuits on utility-scale quantum computers
Subject of Research: Technology and Engineering
Article Title: Quantum image loading and classification: experiments on utility-scale quantum computers
Article References: Gharibyan, H., Karapetyan, H., Sedrakyan, T., Subasic, P., Su, V. P., Tanin, R. H., & Tepanyan, H. (2026). Quantum image loading and classification: experiments on utility-scale quantum computers. Quantum Machine Intelligence, 8(1), Article 57. https://doi.org/10.1007/s42484-026-00388-3
Image Credits: AI Generated
DOI: 10.1007/s42484-026-00388-3
Keywords: Quantum image loading, Quantum image classification, Approximate amplitude encoding, Variational quantum circuits, Hierarchical learning, Experimental quantum machine learning, MNIST, Honda Scenes dataset, Quantinuum H-2, IBM Heron, NISQ-era quantum computing
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Katie Riggs. (September 4, 2026). Quantum computers tackle image loading and classification at utility scale. Scienmag. https://scienmag.com/quantum-computers-tackle-image-loading-and-classification-at-utility-scale/
Katie Riggs. “Quantum computers tackle image loading and classification at utility scale.” Scienmag, 4 September 2026, https://scienmag.com/quantum-computers-tackle-image-loading-and-classification-at-utility-scale/. Accessed 4 September 2026.
Katie Riggs. “Quantum computers tackle image loading and classification at utility scale.” Scienmag. September 4, 2026. https://scienmag.com/quantum-computers-tackle-image-loading-and-classification-at-utility-scale/
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