Argonne Lab maps atomic flaws that cause silicon qubit errors

Argonne National Laboratory researchers have mapped atomic-level flaws that directly impact the performance of silicon spin qubits, a promising platform for scalable quantum computing.

The team used the Chicago Quantum Computing Testbed, the first full-stack, solid-state qubit testbed at a U.S. research institution, to analyze industrial-grade silicon wafers and pinpoint the origin of qubit failure. Their study revealed that random atomic-scale fluctuations within the silicon quantum well layers are the primary cause of variability in valley splitting, a critical energy difference affecting electron stability. This work, the researchers state, “transforms valley splitting from an unexplained obstacle into a materials engineering challenge with clear paths toward improved silicon qubits.” Atomic-Scale Disorder Correlates with Valley Splitting Variability Silicon spin qubits offer a compelling route to scalable quantum computing because they leverage established semiconductor manufacturing techniques. The research institution has pinpointed a critical factor limiting their performance: atomic-scale disorder within the silicon quantum well layers. Researchers demonstrated a direct correlation between these material imperfections and the variability of valley splitting, a quantum property impacting electron stability and qubit fidelity.

The team employed a sensitive electrical spectroscopy method to map valley splitting across individual quantum dots positioned within the silicon quantum well. By shifting the quantum dot’s location and measuring the resulting changes in valley splitting, they generated a nanoscale map revealing random atomic-scale fluctuations as the dominant source of variability. These fluctuations, occurring within the alloyed quantum well, directly influence the energy difference between electron valley states; a smaller split increases the risk of electrons leaking into unwanted states, introducing errors into calculations. This detailed mapping was made possible through a collaboration between Argonne and Intel, combining the laboratory’s measurement expertise with Intel’s industrial fabrication capabilities on a 12-qubit class silicon quantum dot processor. James Clarke, Director of Quantum Hardware at Intel Corporation, emphasizes the significance of this shift in understanding, as explained in a recent publication by Marcks, J.C., et al. in Nature Communications. The ability to directly link atomic-level flaws to qubit performance provides a tangible target for material refinement and optimization, potentially leading to more reliable and higher-fidelity silicon qubits and, ultimately, more powerful quantum computers. Source: https://www.energy.gov/science/bes/articles/uncovering-hidden-disorder-silicon-quantum-computers Stay currentSee today’s quantum computing news on Quantum Zeitgeist for the latest breakthroughs in qubits, hardware, algorithms, and industry deals. Tags: 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. Latest Posts by Ivy Delaney: QuiX Quantum joins Netherlands Top 250 quantum computing companies August 12, 2026 New perovskite cells hit 33.64% efficiency with a molecular tweak August 12, 2026 Alice & Bob joins Europe’s first quantum error correction network August 12, 2026