HRL Ribbon Cable Preserves Qubit States in Self-Controlling Processor

HRL Laboratories reports demonstrating a self-operating quantum processor, a critical step toward scalable quantum computing.

The team overcame a major hurdle by replacing racks of external control electronics with a custom CMOS controller operating at, 450°F, enabling autonomous error correction for its 18-qubit device.A new high-density superconducting ribbon cable preserves fragile qubit states by delivering hundreds of control signals without transferring heat. “The technologies that enabled conventional computing weren’t just the highest-performing — they were the ones that could be manufactured cheaply and at scale,” said Rob Vasquez, CEO, HRL Laboratories, outlining the team’s focus on manufacturability.A silicon quantum processor capable of self-operation represents a significant advance in the pursuit of scalable quantum computing, as HRL Laboratories demonstrated autonomous error correction on an 18-qubit device. Unlike conventional systems reliant on external racks of electronics for control, HRL’s prototype integrates a custom CMOS controller directly within the cryostat, operating at, 450°F, eliminating the need for real-time input from room-temperature components, a first for this architecture.This achievement addresses a fundamental challenge in quantum computing: managing the escalating complexity of controlling a large number of qubits without introducing excessive wiring and heat. Central to this innovation is a newly developed high-density superconducting ribbon cable, designed to deliver hundreds of control signals to the qubits while minimizing heat transfer. Maintaining the qubits’ fragile quantum states requires extreme isolation, and this cable preserves that isolation, a critical factor previously hindering scaling efforts.The system’s performance is markedly improved; control errors are ten times lower than in previous demonstrations using similar qubits, a result of a refined fabrication process that reduces device noise and enhances reliability. Each quantum operation is completed in under a microsecond, demonstrating the speed and precision of the integrated control system.

The team observed a roughly fivefold reduction in errors as they increased the number of qubits used in their error-correcting repetition code, validating the expected error suppression properties essential for practical quantum computation.This outcome aligns with the team’s theoretical models, suggesting the approach will remain effective as the system scales to larger qubit counts. This marks the first instance of complete error correction being executed by a cryogenic controller, operating independently of external, room-temperature electronics.HRL views this as a complete prototype for a future quantum computer architecture, one built on standard microchip production lines and designed to fit within a single refrigerator, lowering production costs and broadening potential applications. Rob Vasquez, President and Chief Executive Officer, HRL Laboratories, added, “We think quantum computing will follow a similar path. Our goal is to build these powerful computers using standard microchip production lines and fit each one inside a single refrigerator.” The findings, published in Nature as “A digitally controlled silicon quantum processing unit,” signal a move toward more practical and manufacturable quantum systems.The technologies that enabled conventional computing weren’t just the highest-performing – they were the ones that could be manufactured cheaply and at scale. Source: https://www.businesswire.com/news/home/20260728330204/en/HRL-Demonstrates-a-Silicon-Quantum-Processor-That-Runs-Itself See today’s quantum computing news on Quantum Zeitgeist for the latest breakthroughs in qubits, hardware, algorithms, and industry deals.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.