Quantum Campus shares the latest in quantum science and technology. Read by more than 1,900 researchers, we publish on Fridays and are always looking for news from across the country. Want to see your work featured? Submit your ideas to the editor.

Argonne National Lab and Intel published details of a 12-qubit system built on Intel’s Tunnel Falls chip. The silicon- and germanium-based system uses quantum dots to create electron spin qubits. Though silicon-based systems offer easier manufacturing, they typically have limited readout and control fidelity compared to other quantum computing platforms.

Argonne and Intel’s new article characterizes some of the unwanted impact of the germanium’s random distribution on the quantum dots’ lowest energy states. These insights will be crucial to improving performance and to scaling system size and manufacturing capability.

This work was published in Nature Communications.

Oxford Quantum Circuits published a review paper on erasure qubits. These qubits are considered a possible means of efficient error correction that requires fewer qubits and less computing hardware. The paper tracks recent implementations of the team’s superconducting dual-rail encoded erasure qubits, as well as nearer-term applications and open questions about their use.

“By engineering ‘erasure noise,’ we can design qubits with built-in error detection, which flags where and when an error happens. This enables much lower overheads for quantum error correction, creating a realistic avenue towards early fault-tolerant quantum computers,” OQC’s Maria Violaris said in a LinkedIn post announcing the paper.

Read the full paper on arxiv.

Engineers at Purdue developed what they call a “photonic transistor.” The team used the device to modulate a beam to a nonlinear refractive index several orders of magnitude higher than what the state of the art can currently achieve.

“Using single photons for light modulation opens the possibility of gigahertz-frequency — and potentially even faster — optical switching for on-chip photonic and quantum devices operating at room temperature,” they said in their paper.

This work was published in Nature Nanotechnology.

Using their Willow chip, Google Quantum AI released findings that quantum contextuality-based algorithms achieve quantum advantage on some magic square games often used to explore quantum theory and on a 2D hidden linear function problem. The team suggests that algorithms that rely on quantum contextuality — the principle that quantum properties aren’t pre-existing values independent of their measurement — offer a promising benchmark for comparing system performance.

In covering the paper, New Scientist talked to university-based researchers about the possible use of such benchmarks and the limitations of Google’s findings — most notably the relatively small number of qubits used in the study.

Quantum Campus is edited by Bill Bell, a science writer and marketing consultant who has covered physics and high-performance computing for more than 25 years. Disclosure statement.