Engineering light transmission through opaque media is possible thanks to the development of a classical wavefront shaping technique first reported in 2007. Researchers have now demonstrated a quantum entanglement-based method that enables selective image transmission through complex disordered materials.

“We discovered that there might be a way to use quantum properties of light to actually help or improve the problem of imaging through scattering media,” explains Hugo Defienne, a quantum optics researcher at the Paris Institute of Nanosciences (CNRS/Sorbonne University).

In two new research papers, Defienne and his colleagues show how to leverage quantum correlations to engineer incoming light to overcome the scrambling that occurs when it passes through “opaque” scattering materials. The approach could point towards alternatives to the solution pursued so far for unscrambling such light – and could even provide a route towards secure communications, by rendering channels transparent to entangled photon pairs, while remaining opaque to a classical light.

“These works offer a particularly elegant perspective, showing that for spatially entangled photons, the space of wavefront corrections that can compensate scattering is significantly larger than in the classical case,” comments Yaron Bromberg, head of the Complex Photonics Lab at the Hebrew University of Jerusalem in Israel.

Quantum opportunities

In 2007, Allard Mosk and Ivo Vellekoop, both then at the University of Twente in the Netherlands, reported how measurements of the intensity spatial distribution of light distorted by transmission through an opaque scattering material could be used to control the propagation of light and refocus it at the output, effectively turning the scattering material into a lens.

Building on this, in 2010 Sébastien Popoff and Sylvain Gigan showed that they could identify a transformation matrix between the original beams and the transmitted beams, such that applying the inverse to the initial wavefront using a spatial light modulator would allow the original beams to emerge undistorted. Later developments have applied the technique to quantum light. However, being based on intensities of the transmitted light alone, these have not actually exploited light’s quantum properties.

Defienne was working on both the quantum properties of light and the challenge of unscrambling scattered light signals when he began to mull over how to leverage quantum properties in this feedback approach. “We discovered that when you use quantum light, there are many ways of actually unscrambling the light that do not exist when you use a classical system,” he tells Physics World.

Correlation correction

To understand how solutions to the problem multiply for the quantum scenario, it helps to consider a certain type of quantum entanglement that leads to spatially correlated photons. Measure the end point of a photon that’s spatially correlated with another, and the end point of its partner photon will be dictated by the correlation. While scattering media also scramble spatial correlations, a spatial modulator can also invert the scrambling process to retrieve the original spatial correlations.

The quantum bonus comes because whereas with classical light the scattering medium only appears transparent when a one-to-one correspondence between incoming beam and output beam is achieved, there are additional solutions that return an apparently identical spatial correlation distribution between incoming and output entangled photons.

Defienne and his colleagues report the derivations for the quantum approach in Optica. They also demonstrate the approach using a single photon avalanche diode to detect the quantum correlations of light transmitted through a film of paraffin, before feeding it back into a spatial light modulator that adjusts phases to manipulate spatial correlations.

“Conceptually, it’s exactly the same idea,” says Defienne. Nonetheless, almost 20 years on from Mosk and Vellekoop reporting their approach for unscrambling light, this is the first time it has been successfully applied to the quantum properties of light. “It’s just very complex,” Defienne adds. The weak photon pair source, scattering losses in the medium and imperfect detection all pose challenges, such that it can take a long time to have enough data for the required statistics.

“In fact, this is only possible because now we have single-avalanche diode cameras,” says Defienne, noting that these became available with the required sensitivity and frame rate about five years ago. “With any previous camera technology, this is totally impossible.”

Leveraging the quantum properties in this way means that the spatial light modulator unscrambles the quantum correlations while leaving the classical beam still scrambled. The researchers suggest this could serve as a quantum filter that might be useful for blocking nefarious signals intended to muddle transmitted data – by encoding data in quantum correlations it’s possible to block fake data, so long as it is classically encoded. They demonstrate this filtering process in their Nature Physics paper.

“These results mark a significant breakthrough achieved in experimental samples,” says Sushil Mujumdar from the Tata Institute of Fundamental Research in India, who was not directly involved in the current research. Mujumdar has been working on optimizing wavefront shaping algorithms for quantum light, in particular where the incoming photon count is low. He adds: “The logical segue to this work would be the application of these techniques to thicker and realistic media, which, as acknowledged in the paper, become challenging because of drastically low signal photons, characteristic of the quantum domain.”

Indeed, Defienne and his colleagues are already looking into “some new shaping approach that could be better” for quantum correlated photons passing through, for example, a layer of paint instead of paraffin. They are also looking at the potential to leverage the optical nonlinearity of entangled photon optics for quantum reservoir computing.

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Earlier this year a group of researchers led by Sören Arlt of the University of Tübingen set out to stretch the limits of how far artificial intelligence (AI) can contribute to scientific discovery. In work published in Nature Machine Intelligence, they developed a language model capable of generating classes of blueprints for quantum optics setups that produce specific families of quantum states. Their model was able to design several experimental configurations that successfully generated desired, and in some cases previously unknown, constructions within the limits of its training.

Beyond this immediate technical achievement, the implications of this approach are striking. In principle, a researcher could ask a system like this to propose experimental setups for a desired quantum state without spending months or years exploring possible configurations. Such capabilities could accelerate research in areas like quantum computing and quantum communication, where specially engineered quantum states serve as key resources. Although the system still has clear limitations – it cannot always guarantee that the produced state perfectly matches the target and it sometimes fails to find a solution – this study demonstrates that machine learning can already contribute meaningfully to scientific discovery, even in the design of physical experiments.

Earlier attempts had already hinted that AI could assist in designing quantum experiments. In 2016, researchers from Mario Krenn‘s group (in which Arlt is a doctoral student) demonstrated that automated search methods could propose previously unknown quantum optics experiments capable of generating complex entangled states. Since then the field has grown rapidly, with tools such as PyTheus producing candidate experimental designs and revealing physical mechanisms that researchers had not previously recognised.

This time, instead of searching directly for a single experimental setup, the researchers trained a transformer-based language model on a dataset linking target quantum states to experimental blueprints. Given a desired state, the model generates Python code describing how to build a corresponding experiment. Based on the same transformer architecture used in modern language models, the system translates a quantum state into a program that constructs it experimentally. This output can be interpreted directly by researchers, allowing them to run the proposed construction and understand the design rules that the model discovered.

Using this approach, the researchers constructed 20 classes of quantum states of interest, among them well-known entangled states, such as GHZ, W and Bell states, some of which had no known experimental construction rules. Out of these, the system generated valid construction rules for six classes: four corresponded to already known solutions, while two corresponded to genuinely new construction rules for generating particular classes of entangled quantum states.

Rather than discovering entirely new states, the system identified previously unknown ways of assembling optical components that produce states with the required entanglement structure. The team verified these constructions computationally by simulating the resulting quantum states and comparing their fidelity with the target states. Although the experiments have not yet been carried out in the laboratory, the proposed setups provide experimentally testable blueprints.

The practical implications of tools like this are already prompting debate. Some see them as accelerating scientific discovery by exploring vast experimental possibilities, while others raise concerns that increasing automation could sideline experimental intuition. The key advance over previous approaches lies in generalization: rather than producing a single design, the model generates a program capable of constructing experiments for an entire class of states. “Instead of designing a single experiment for one target, this approach generates a general program that produces valid experiments for a whole class of targets,” Arlt explains.

The researchers chose to explore states that are physically relevant across different areas of quantum physics, allowing them to probe entanglement patterns relevant to quantum simulation, communication and computation. In this sense, the system expands the experimental toolbox available to physicists.

In some cases, the system uncovered patterns that the researchers had not previously identified. “We discovered two construction rules that we did not know of before,” Arlt notes. In another case, it generated a different construction rule for a class of states that had already been solved, following a completely different experimental strategy.

Rather than replacing physicists, the authors see AI changing how experiments are conceived. Instead of manually assembling setups, researchers may define the space of possible configurations and allow algorithms to explore it. As Arlt describes it: “instead of thinking about how do I put these components together so my experiment works, we think about what should the space of possible configurations look like so my computer can explore it efficiently”.

Despite the use of machine learning, the system is relatively modest in scale, with roughly 100 million parameters. While this keeps the computational cost manageable, it also constrains the range of experimental sizes and resources that the model can handle. The model also does not verify the correctness of its own outputs, requiring explicit fidelity checks of the generated states.

Looking ahead, the team hopes to extend this approach to other domains of physics and combine it with additional discovery methods.

All in all, tools like this suggest a future in which computers assist not only with simulations, but also with proposing new experiments and uncovering patterns in physical systems. Rather than replacing physicists, such systems may increasingly act as collaborators, helping researchers explore experimental designs that would otherwise remain inaccessible.

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