Quantum Computing
Assembly Line
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 Ο-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 Ο-magnets, triggered by phenyl C(sp2)βH 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.
π§ π€ 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 KEBβs warehouses.
In AutoQML β a project that develops solution approaches for linking quantum computing and machine learning β 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.
Boschβs 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. HernΓ‘ndez 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.