Light Cages Could Solve Quantum Computing’s Memory Problem

The Bottleneck: Why Quantum Computing Needs New Memory

For a quantum computer to start being used, if not routinely, at least reliably, it will need to replicate with quantum-compatible components most of the functions performed by silicon semiconductors: not just calculation (processor/chips), but also networking and memory.

Networking is progressing. We have seen the release of QNodeOS, an operating system dedicated to quantum networking, alongside mass-producible photonic chips, erbium nanophotonic amplifiers, and quantum teleportation using traditional optical fiber networks.

But memory has been more elusive, although sound waves might provide a sort of hybrid solution to the issue of stability.

This difficulty arises because qubits are extremely unstable, requiring superconducting materials, total isolation from environmental interference, and ultra-cold temperatures.

Networking can partially help alleviate the lack of memory by forwarding information to other physical qubits in a cluster, but this option can only go so far. At some point, complex calculations will require a long-lasting (by quantum standards) memory system able to retain quantum data reliably.

This is exactly what researchers in Germany at the Humboldt-Universität zu Berlin, University of Stuttgart, and Leibniz Institute of Photonic Technology appear to have achieved.

They created a nanoscopic “light cage” able to retain quantum data for an unprecedented length of time. They published their findings in the scientific journal Light: Science & Applications¹, under the title “Light storage in light cages: a scalable platform for multiplexed quantum memories”.

What Are Nanoscopic “Light Cages”?

Quantum memory refers to components able to store and preserve intact quantum information (qubits).

In practice, this functions like RAM: not for long-term data storage, but for keeping data accessible for the next step in a calculation process.

This requires three successive steps:

1. Capturing the quantum state.
2. Storing this state in a format more stable than volatile qubits.
3. Retrieving the data for further processing.

How 3D-Printed Light Cages Work

The foundation of the German researchers’ work is the “light cage.” These nanoscopic structures are designed to hold onto light without it losing its quantum characteristics.

Source: Light

In this specific case, they used hollow-core waveguides filled with an atomic vapor of cesium atoms.

The structures themselves were built using nanoprinting technology, specifically two-photon polymerization lithography with commercial 3D printing systems.

To ensure long-term stability in the reactive cesium environment, the structures are coated with a protective layer, demonstrating remarkable durability with no degradation observed even after five years of operation.

Source: Light

Advantages Over Traditional Quantum Memory

This design offers unique advantages compared to previous attempts.

First, these nanoprinted structures allow for the rapid diffusion of cesium atoms. This reduces the time required to fill the core with atomic vapor from months to just days, all while maintaining excellent optical field confinement.

Second, the design allows for unique side-wise access to the core regions, facilitating the retrieval of quantum data when needed.

“We created a guiding structure that allows quick diffusion of gases and fluids inside its core, with the versatility and reproducibility provided by the 3D-nanoprinting process.

This enables true scalability of this platform, not only for intra-chip fabrication of the waveguides but also inter-chip, for producing multiple chips with the same performance.”

This scalability makes it far easier to reach an industrial commercial stage. It allows for multiple light cages on the same chip, increasing the potential total memory of a quantum processor. Variations within a single chip were kept below 2 nanometers, while differences between chips remained under 15 nanometers.

Because the storage performance between different light cages is minimal and consistent, the design produces reliable expectations for engineers.

Swipe to scroll →

Another massive shift compared to most quantum computing technology is that light cage memory operates slightly above room temperature and does not require cryogenic cooling. This makes it not only more reliable, but also significantly more economical.

How Long Can Light Cages Store Data?

The light cages enable highly efficient conversion of guided light pulses into collective atomic excitations. An optical control laser can then release the light on demand, retrieving the data for further quantum calculations.

The research team successfully stored attenuated light pulses containing only a few photons for durations of several hundred nanoseconds.

Source: Light

While this timescale may seem short, in quantum networking and photonic-memory terms, it represents an unusually long and stable storage duration, especially for room-temperature-compatible systems.

Scaling Quantum Networks with Optical Memory

While networks have so far helped compensate for the lack of memory, reliable memory could conversely help create more complex networks.

By creating reliable storage, quantum memory can serve as repeater nodes, boosting the reliability and range of the quantum network significantly. This is a major step toward networking together several quantum chips in one supercomputer, as well as connecting physically distant quantum computers.

Conclusion

Quantum computing has made massive progress in the past few years, with networking and larger, scalable quantum chips being developed. The missing link for a full-blown quantum computer or large-scale quantum network was reliable memory components.

The utilization of these improved light cages might be exactly the key to accelerating the development of quantum computing, thanks to its cheap and reliable manufacturing process.

The next step will likely be practical testing with existing quantum chips and optimization of the manufacturing process to integrate into the standard practices of a semiconductor foundry.

Investing in Quantum Computing

Honeywell / Quantinuum (HON)

Quantinuum is the result of the merger of Honeywell Quantum Solutions and Cambridge Quantum.

Honeywell remains the company’s majority shareholder (likely 52% ownership) after a fundraising round valuing it at $10B. Founder Ilyas Khan is reported to own approximately 20% of the company. Other shareholders include JSR Corporation, Mitsui, Amgen, IBM, and JP Morgan.

A potential IPO of Quantinuum, potentially as a part of a larger corporate restructuring, is estimated by analysts to be worth as much as $20B and might occur between 2026 and 2027.

Quantum computing is not the central part of Honeywell’s business, which is more centered around products in aerospace, automation, and specialty chemicals & materials.

Each of these domains might, however, benefit from quantum computing, especially computational chemistry and quantum cybersecurity, potentially giving Honeywell an advantage against its competitors.

The company’s main model for now is Helios, the successor to H2, and the “most accurate quantum computer in the world”. It has a record-breaking 98 fully connected physical qubits with single-qubit gate fidelity of 99.9975% and two-qubit gate fidelity of 99.921% across all qubit pairs.

We also leveraged Helios to perform large-scale simulations in high-temperature superconductivity and quantum magnetism—both with clear pathways to real-world industry applications.

The company has pursued high-quality computing with very little error, rather than simply adding as many qubits as possible, creating so-called “fault-tolerant quantum computing”.

This approach is labeled by the company “Better qubits, better results”, with a similar amount of qubits achieving 100-1,000 fold more reliable results.

Source: Quantinuum

This could make a notable difference in urgently needed quantum-resistant cryptography. Defense company Thales (HO.PA -0.96%) is already collaborating with Quantinuum, as are international banks like HSBC and JP Morgan.

Quantinuum also offers its proprietary quantum computational chemistry InQuanto, usable for pharmaceuticals, material sciences, chemicals, energy, and aerospace applications.

Like many other quantum computing companies, Quantinuum offers Helios as “hardware-as-a-service”, allowing users to benefit from quantum computing without having to deal with the complexity of operating the system themselves.

Quantinuum signed in November 2024 a partnership with German Infineon, Europe’s largest semiconductor manufacturer. Infineon will bring its integrated photonics and control electronics technology to help create the next generation of trapped-ion quantum computers.

As integrated photonics move closer to practical use cases, it is now clear how important this partnership might be for the future of Quantinuum. At this point, it seems that the next step for the company will be to release the world’s first AI-focused photonics-quantum chip.

In the coming months, Quantinuum will share results from ongoing collaborations, showcasing the groundbreaking potential of quantum-driven advancements in Generative AI.

The innovative Gen QAI capability will enhance and accelerate the use of Metallic Organic Frameworks for drug delivery, paving the way for more efficient and personalized treatment options, with details to be unveiled at the launch of Helios.

Quantinuum Announces Generative Quantum AI Breakthrough with Massive Commercial Potential

More ongoing use cases could strongly boost the future value of the company, and therefore, Honeywell’s stake in it.

Generative Quantum AI: Unlocking AI’s Full Potential

(You can read more about the rest of Honeywell’s industrial activities in automation, aerospace, and advanced materials in the report dedicated to the company.)

Latest Honeywell (HON) Stock News and Developments

Study Referenced

^{1. Gómez-López, E., Ritter, D., Kim, J. et al. Light storage in light cages: a scalable platform for multiplexed quantum memories. Light Sci Appl 15, 13 (2026). https://doi.org/10.1038/s41377-025-02085-5}