Martin F. X. Mauser

The past decades have seen the development of quantum machine learning, stemming from the intersection of quantum computing and machine learning. This field is particularly promising for the design of alternative quantum (or quantum inspired) computation paradigms that could require fewer resources with respect to standard ones, e.g., in terms of energy consumption. In this context, we present the implementation of a data reuploading scheme on a photonic integrated processor, achieving high accuracies in several image classification tasks. We thoroughly investigate the capabilities of this apparently simple model, which relies on the evolution of one-qubit states, by providing an analytical proof that our implementation is a universal classifier and an effective learner, capable of generalizing to new, unknown data. Hence, our results not only demonstrate data reuploading in a potentially resource-efficient optical implementation but also provide theoretical insight into this algorithm, its trainability, and generalizability properties. This lays the groundwork for developing more resource-efficient machine learning algorithms, leveraging our scheme as a subroutine.

The analysis of proteins in the gas phase benefits from detectors that exhibit high efficiency and precise spatial resolution. Although modern secondary electron multipliers already address numerous analytical requirements, additional methods are desired for macromolecules at energies lower than currently used in post-acceleration detection. Previous studies have proven the sensitivity of superconducting detectors to high-energy particles in time-of-flight mass spectrometry. Here, we demonstrate that superconducting nanowire detectors are exceptionally well suited for quadrupole mass spectrometry and exhibit an outstanding quantum yield at low-impact energies. At energies as low as 100 eV, the sensitivity of these detectors surpasses conventional ion detectors by three orders of magnitude, and they offer the possibility to discriminate molecules by their impact energy and charge. We demonstrate three developments with these compact and sensitive devices, the recording of 2D ion beam profiles, photochemistry experiments in the gas phase, and advanced cryogenic electronics to pave the way toward highly integrated detectors.

The isolation of biomolecules in a high vacuum enables experiments on fragile species in the absence of a perturbing environment. Since many molecular properties are influenced by local electric fields, here we seek to gain control over the number of charges on a biopolymer by photochemical uncaging. We present the design, modeling, and synthesis of photoactive molecular tags, their labeling to peptides and proteins as well as their photochemical validation in solution and in the gas phase. The tailored tags can be selectively cleaved off at a well-defined time and without the need for any external charge-transferring agents. The energy of a single or two green photons can already trigger the process, and it is soft enough to ensure the integrity of the released biomolecular cargo. We exploit differences in the cleavage pathways in solution and in vacuum and observe a surprising robustness in upscaling the approach from a model system to genuine proteins. The interaction wavelength of 532 nm is compatible with various biomolecular entities, such as oligonucleotides or oligosaccharides.