Quantum Hardware, Quantum Sensors

Titanium nitride inductor could shrink quantum sensors and circuits

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November 4, 2026 by Ivy Delaney

Researchers from nanoGUNE, Quantum Motion, and Imperial College London have fabricated a new superinductor within a silicon integrated circuit using a standard commercial complementary metal-oxide-semiconductor (CMOS) process. The device uses titanium nitride (TiN) for its high kinetic inductance, enabling the creation of compact, high-inductance circuit elements for superconducting qubits and quantum circuits, nanoGUNE says.

This technology aims to create a radiofrequency single-electron transistor capable of spin readout speeds below the microsecond timescale; the research team states that the applications of the TiN-based CMOS inductors could expand well beyond quantum sensing and computing for spin qubits, impacting areas such as radio-astronomy, low-power cryoelectronics and quantum sensing and simulations.

Titanium Nitride Superinductor Fabrication in CMOS Silicon Circuits

This approach allows for compact, high-inductance circuit elements while preserving superconducting properties, a critical feature for manipulating quantum behavior within circuits. Researchers successfully demonstrated a non-linear superinductor integrated directly into silicon. The resulting system is designed to create a radiofrequency single-electron transistor, a sensor commonly used for semiconductor spin qubits, with a significantly reduced physical size and improved sensitivity.

the applications of the TiN-based CMOS inductors could expand well beyond quantum sensing and computing for spin qubits, impacting areas such as radio-astronomy, low-power cryoelectronics and quantum sensing and simulations.

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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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