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Tobias Hahn

Publications and source records attributed to Tobias Hahn.

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PIQC: Scalable Distributed Quantum Computing via Photonic Integration of Designed Molecular Quantum Nodes

There is a growing consensus that large-scale, fault-tolerant quantum computing (FTQC) necessitates high-fidelity photonic interconnects to overcome the scaling limits of monolithic architectures. However, most current platforms were not originally designed for native photonic connectivity and require significant engineering overhead. To overcome these fundamental hardware limitations, we recently introduced a rationally designed organic molecule that serves as an ideal quantum node, featuring a robust qubit-photon interface (QPI) and a long-lived nuclear-spin register. In this work, we present PIQC (Photonic Integrated Quantum Circuits), a distributed architecture designed to scale these molecular nodes into a functional quantum computer. The PIQC framework integrates five mutually reinforcing innovations: (i) Designer molecular qubits, i.e. carbene molecules in an isosteric host that provide millisecond-coherence electron spins with high spectral stability and spin-dependent optical emission, (ii) deterministic nuclear registers made of synthetically placed $^{13}$C or $^{14}$N labels that enable fast ($\sim 1~\mu$s), high-fidelity electron-nuclear gates, (iii) hybrid photonic integration, which allows molecular films to seamlessly integrate with existing mature fabrication technologies, e.g. thin-film lithium niobate (TFLN), (iv) heralded entanglement protocols that can tolerate up to 70% photon loss, and (v) stairway Floquetification, i.e. high-rate quantum low-density parity-check (qLDPC) codes that are converted into Floquet codes, reducing syndrome extraction to weight-two Bell-pair measurements that match PIQC's networked hardware. PIQC offers a hardware-efficient, commercially viable pathway toward a utility-scale quantum computer based on distributed FTQC.

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Raw Filtering of JSON Data on FPGAs

Many Big Data applications include the processing of data streams on semi-structured data formats such as JSON. A disadvantage of such formats is that an application may spend a significant amount of processing time just on unselectively parsing all data. To relax this issue, the concept of raw filtering is proposed with the idea to remove data from a stream prior to the costly parsing stage. However, as accurate filtering of raw data is often only possible after the data has been parsed, raw filters are designed to be approximate in the sense of allowing false-positives in order to be implemented efficiently. Contrary to previously proposed CPU-based raw filtering techniques that are restricted to string matching, we present FPGA-based primitives for filtering strings, numbers and also number ranges. In addition, a primitive respecting the basic structure of JSON data is proposed that can be used to further increase the accuracy of introduced raw filters. The proposed raw filter primitives are designed to allow for their composition according to a given filter expression of a query. Thus, complex raw filters can be created for FPGAs which enable a drastical decrease in the amount of generated false-positives, particularly for IoT workload. As there exists a trade-off between accuracy and resource consumption, we evaluate primitives as well as composed raw filters using different queries from the RiotBench benchmark. Our results show that up to 94.3% of the raw data can be filtered without producing any observed false-positives using only a few hundred LUTs.

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