USC and Quantum Elements scale surface code on IBM Heron chips

Researchers at the University of Southern California and Quantum Elements have demonstrated improved error protection using the surface code on IBM Heron quantum processors, despite a mismatch between the code’s square-grid layout and the processors’ heavy-hex architecture, the company says. Published in Nature Communications, the work shows that logical qubits can achieve below-threshold performance even when mapped onto hardware not specifically designed for the surface code. This success hinged on “Orbit,” a Qiskit Function developed by Quantum Elements that suppressed idle-time noise and enabled directional subthreshold scaling, according to Quantum Elements Chief Scientific Officer Daniel Lidar: “We found that only by leveraging the dynamical decoupling techniques deployed in Quantum Elements’ Qiskit Function, Orbit, we were able to demonstrate the expected improvements.” The finding suggests greater flexibility in designing scalable, fault-tolerant quantum systems.

Surface Code Scaling Achieved on IBM Heron’s Heavy-Hex Architecture IBM Heron processors successfully ran a surface code error correction scheme despite utilizing a “heavy-hex” architecture not ideally suited for the square-grid layout the code requires, a result detailed in a recent Nature Communications paper. Researchers overcame the architectural mismatch by combining a depth-efficient error correction code with a noise suppression technique embedded within Quantum Elements’ “Orbit” Qiskit Function, achieving directional subthreshold scaling, a key indicator of improved error protection. This outcome demonstrates that surface codes can be adapted to a wider range of superconducting architectures than previously assumed, potentially easing hardware development constraints.

The team’s approach focused on mitigating idle-time noise, a significant challenge when mapping the surface code onto the heavy-hex hardware. As the error-correcting code expanded, qubits experienced periods of inactivity, increasing the likelihood of errors accumulating and undermining the correction process. Quantum Elements, founded in 2023 and headquartered in Los Angeles, has positioned itself as a key player in AI-driven quantum software development with its Constellation platform. The company’s AI-native approach to quantum simulation and software tools, backed by investors including Ground State Ventures and Firgun Ventures, is increasingly integrated with leading hardware platforms like Amazon Braket and Rigetti Computing. This partnership with Rigetti extends to collaborative research applying AI-powered digital twins to model and simulate critical qubit behaviors, including single- and two-qubit gates, readout, and idle-time noise. The development of Orbit, now a Qiskit Function, exemplifies this commitment to practical tools for quantum error mitigation. Izhar Medalsy, co-founder and CEO of Quantum Elements, emphasized the broader implications of this work for the field. “This shows the power of hybrid approaches to move us towards fault tolerant quantum computing,” he stated. Decoupling error correction code design from specific hardware layouts allows quantum processor developers to prioritize a wider range of engineering requirements without being solely constrained by code compatibility. Because a given error correcting code and hardware may not always match, we wanted to test the limits of making the surface code, designed for square lattices, work on IBM’s heavy-hex hardware. Daniel Lidar, Chief Scientific Officer at Quantum Elements and Director of the USC Center for Quantum Information Science & Technology Source: https://quantumelements.ai/ More like thisQuantum HardwareIQM engineer moves from qubits to a working quantum computerQuantum Computing Business NewsIQM sends its first quantum computer to Brazil’s Eldorado InstituteQuantum HardwareResearchers Accelerate State Transfer by 11.8 TimesQuantum Research NewsSandia Labs maps a path to faster spin qubit tuningStay 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: MLPerf Edge Agentic benchmark sees 6.4x speedup with NVIDIA September 17, 2026 Fifteen years of work yields quantum tech award for Cogito Group September 17, 2026 From Brookhaven, Kharzeev to Drive UConn Quantum Efforts September 17, 2026