Quantum Computing for XRF fitting; towards practical applications

Quantum computing represents a paradigm shift in scientific computing, transcending mere acceleration to enable a reevaluation and restructuring of problem-solving methodologies. Although practical applications are very limited due to ongoing challenges, progress is made daily. Our research extends previous work on quantum optimization in battery research [1] to demonstrate early yet promising applications in XRF fitting, a technique vital across various disciplines and wide-ranging applications from cultural heritage preservation to environmental monitoring. Although mature XRF fitting software exists (ie PyMCA), crucial for accurate elemental analysis, it often grapples with complexities, especially in soft X-ray applications (<2keV) or when analyzing extensive XRF maps (>10³ points).

Emphasizing the technique's breadth, from portable devices to modern synchrotron beamlines, XRF analysis demands nuanced spectral fitting, often challenging due to manual adjustments, the intricacies of soft X-rays, multi-element detectors or the necessity for rapid analysis in extensive XRF maps. Certain conditions like overlapping peaks, background noise, trace elements detection and high accuracy requirements add to the challenge. Our hypothesis posits that quantum superposition could substantially advance spectrum processing, enhancing speed and reducing the computational load for complex samples.

Eventually this approach could lead to new quantum algorithms for XRF fitting, exploiting quantum entanglement and interference to explore solution spaces more comprehensively than classical algorithms. Initial findings suggest the potential for quantum computing to refine XRF fitting accuracy, adeptly navigating the multi-variable optimizations required to untangle overlapping spectral peaks but also touches arguments such as matrix effects correction and quantitative analysis in trace element detection. This work also foresees applications that go beyond speed improvements such as simulating atomic interactions and taking advantage of entanglement and interference in the near future. Our preliminary work introduces a practical proof-of-concept (and a blueprint) based on PennyLane, Cirq, QisKit, PyQuil and QuQuantum; critically examining them albeit not in all detail. There we prototyped solutions tailored to align with real LEXRF data from the TwinMic beamline at Italian synchrotron Elettra Sincrotrone Trieste. Among the results employ suitable cost functions to bridge measured and predicted LEXRF data, subsequently optimizing quantum circuit parameters through classical methods. As an initial foray, this presentation marks a significant step toward practical quantum applications in XRF analysis, inviting interest and collaboration from peers in this research.