Quantum Computing
Assembly Line
Meet Willow, our state-of-the-art quantum chip
Published in Nature, our results show that the more qubits we use in Willow, the more we reduce errors, and the more quantum the system becomes. We tested ever-larger arrays of physical qubits, scaling up from a grid of 3x3 encoded qubits, to a grid of 5x5, to a grid of 7x7 — and each time, using our latest advances in quantum error correction, we were able to cut the error rate in half. In other words, we achieved an exponential reduction in the error rate. This historic accomplishment is known in the field as “below threshold” — being able to drive errors down while scaling up the number of qubits. You must demonstrate being below threshold to show real progress on error correction, and this has been an outstanding challenge since quantum error correction was introduced by Peter Shor in 1995.
Quantum error correction below the surface code threshold
Quantum error correction provides a path to reach practical quantum computing by combining multiple physical qubits into a logical qubit, where the logical error rate is suppressed exponentially as more qubits are added. However, this exponential suppression only occurs if the physical error rate is below a critical threshold. In this work, we present two surface code memories operating below this threshold: a distance-7 code and a distance-5 code integrated with a real-time decoder. The logical error rate of our larger quantum memory is suppressed by a factor of Λ = 2.14 ± 0.02 when increasing the code distance by two, culminating in a 101-qubit distance-7 code with 0.143% ± 0.003% error per cycle of error correction. This logical memory is also beyond break-even, exceeding its best physical qubit’s lifetime by a factor of 2.4 ± 0.3. We maintain below-threshold performance when decoding in real time, achieving an average decoder latency of 63 μs at distance-5 up to a million cycles, with a cycle time of 1.1 μs. To probe the limits of our error-correction performance, we run repetition codes up to distance-29 and find that logical performance is limited by rare correlated error events occurring approximately once every hour, or 3 × 109 cycles. Our results present device performance that, if scaled, could realize the operational requirements of large scale fault-tolerant quantum algorithms.
Intelligent synthesis of magnetic nanographenes via chemist-intuited atomic robotic probe
Atomic-scale manufacturing of carbon-based quantum materials with single-bond precision holds immense potential in advancing tailor-made quantum materials with unconventional properties, which are crucial in developing next-generation spintronic devices and quantum information technologies. On-surface chemistry approaches, including surface-assisted synthesis and probe-assisted manipulation, are impeded by challenges in reaction selectivity control or restricted by scalability and production efficiency. Here we demonstrate the concept of the chemist-intuited atomic robotic probe by integrating probe chemistry knowledge and artificial intelligence, allowing for atomically precise single-molecule manipulation to fabricate single-molecule quantum π-magnets with single-bond precision. Our deep neural networks not only transform complex probe chemistry into machine-understandable tasks but also provide chemist intuition to elusive reaction mechanisms by extracting the critical chemical information within the data. A joint experimental and theoretical investigation demonstrates that a voltage-controlled two-electron-assisted electronic excitation enables synchronous six-bond transformations to extend the zigzag edge topology of single-molecule quantum π-magnets, triggered by phenyl C(sp2)–H bond activation, which aligns with initial conjectures given by the deep neural models. Our work represents a transition from autonomous fabrication to intelligent synthesis with levels of selectivity and precision beyond current synthetic tools for improved synthesis of organic quantum materials towards on-chip integration.
🧠🤖 Optimising Intralogistics with AI
In its production facilities in Barntrup, KEB operates the in-house transport system AGILOX, which is designed specifically for intralogistics tasks. The AGILOX system is comprised of a swarm (union) of smart automated guided vehicles (AGVs), working collaboratively to transport items throughout KEB’s warehouses.
In AutoQML – a project that develops solution approaches for linking quantum computing and machine learning – KEBs primary objective is to devise a machine learning solution capable of monitoring vehicle status and predicting potential failures. This aligns with KEBs larger objective of facilitating the broader transition to quantum computing in the future, by supporting research institutes with practical, real-world applications.
Bosch’s new partnership aims to explore quantum digital twins
Industrial giant Bosch has partnered with Multiverse Computing, a Spanish quantum software platform, to integrate quantum algorithms into digital twin simulation workflows. Bosch already has an extensive industrial simulation practice that provides insights across various business units. This new collaboration will explore ways quantum-inspired algorithms and computers could help scale these simulations more efficiently.
One of the most promising use cases for the new quantum algorithms is creating better machine learning models more quickly. Hernández Caballer said quantum computing shows tremendous promise in use cases with many combinations of parameters and materials. This early research could give Bosch a leg up in taking advantage of these new systems to improve machine learning and simulation.