Quantum Hardware, Quantum Simulation

Quantum 3D ion trap combines chip & blade designs

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October 14, 2026 by Ivy Delaney
Image: Rice University/Jorge Vidal · news.rice.edu

Rice University researchers have developed a new monolithic segmented 3D ion trap that combines the strengths of both blade-style and microfabricated chip designs, overcoming previous limitations in the field of quantum simulation. The trap enabled the team to study complex systems using heavy ytterbium ions, a capability beyond that of lighter ion types. “Trapped ion simulators can help us understand complex problems, like charge transfer in photosynthesis,” said Guido Pagano, an associate professor of physics and astronomy and senior author on this study.

Monolithic 3D Trap Design Combines Blade & Chip Features

Blade-style traps, traditionally favored for their high performance, are limited by their manual assembly and resulting lack of geometric precision; conversely, microfabricated chip traps offer superior precision but often fall short in overall performance. This new design successfully combines the strengths of both approaches within a single, compact architecture, a feat previously limited to lighter ions. The key to this integration lies in the trap’s fabrication process, beginning with a block of fusilica glass etched with lasers and chemicals to create channels for ions and trenches for electrodes.

These trenches are not merely structural. They actively isolate the electrodes, preventing short circuits during gold sputtering and dielectric breakdown under high vacuum conditions. “Our first generation failed, although in a promising and informative way: It had a significant thermal and electrical management problem. The trap began to glow visibly when the desired high radio-frequency voltage was applied in a vacuum,” explained Abhishek Menon, a graduate student and the corresponding author on this study.

Subsequent iterations refined the design, culminating in a viable trap capable of handling heavier ions like ytterbium. The ability to trap heavier ions is particularly significant, expanding the range of complex systems that can be modeled using trapped ion simulators.

Roman Zhuravel, an assistant research professor, emphasized the broader impact of this accessibility, stating, “The monolithic trap makes trapped-ion simulators more accessible to academic labs.” The team’s design allows researchers to simplify challenging quantum problems into manageable components, then map those solutions onto larger, more complex scenarios. The researchers confirmed through testing and computer simulations that the trap functions as intended.

Trapped ion simulators can help us understand complex problems, like charge transfer in photosynthesis.

Guido Pagano, associate professor of physics and astronomy and senior author on this study

Ytterbium Ions Enable Accessible Quantum Simulations

The ability to manipulate heavier ions, specifically ytterbium, expands the range of complex systems amenable to simulation, a capability previously limited by trap design. This refinement was needed to achieve a functional and versatile ion trap capable of handling heavier ionic species while also simplifying the trapping of lighter ions for diverse quantum technologies.

In our monolithic ion trap, we wanted to combine the high-performance features and accessibility of the blade trap with the scalability and modularity of the chip trap in a way that was compatible with the heavier ions.

Abhishek Menon, a graduate student and the corresponding author on this study
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Ivy Delaney

Ivy Delaney has been working with neural networks and machine learning since the mid-nineties, back when a couple of hidden layers and a long afternoon of training counted as ambitious. She has watched the field go from academic curiosity to the thing quietly running underneath everything, and she brings that long view to quantum computing. For Quantum Zeitgeist she covers the ground where the two fields meet. That means quantum machine learning and the variational algorithms it leans on, and it also means the less glamorous but more interesting story of classical machine learning already doing real work inside quantum machines, decoding error-correcting codes, calibrating noisy hardware and learning the error models that simulators depend on. She writes about the hardware those algorithms have to run on too, and about the post-quantum cryptography scramble that the same hardware has set off. Her stories typically start with the paper, whether that is peer-reviewed work, conference proceedings or an arXiv preprint, with the source linked so you can hold a claim up against the research it came from. She is unimpressed by benchmarks that will not say what they beat, and by demonstrations that only work in the press release.

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