Quantum-assisted NMR data processing: enhancing sensitivity and resolution with quantum computing algorithms - Vaara Bugay Russell; Outeiral Strahm Sushkov; Koch Bohnet Böhm (Institute of Computer Science (IOCS), 2022)
Quantum computing is used to improve NMR spectroscopy sensitivity and resolution. Many scientific fields employ NMR to analyze molecular structures and interactions. Typical NMR data processing algorithms have limitations, especially in recognizing low-abundance compounds and resolving overlapping signals. To solve NMR data processing problems, the proposed research uses quantum algorithms, simulations, and a hybrid quantum-classical approach. Quantum Fourier transform (QFT) enhances sensitivity and quantum phase estimation (QPE) improves resolution. The QFT accelerates data analysis using quantum parallelism to detect low-concentration chemicals. QPEs accurately estimate phases, resolve overlapping peaks, and improve peak assignments. Quantum-assisted NMR data processing improvements are shown numerically. Quantum algorithms improve sensitivity and resolution, allowing delicate signals and correct structural assessments. This study also addresses quantum hardware restrictions, noise, and efficient quantum algorithm design. Quantum-assisted NMR data processing has the potential to transform NMR spectroscopy. Researchers can acquire new accuracy and sensitivity into molecule structures, interactions, and dynamics by linking quantum computers and NMR data analysis. This research advances quantum computing and NMR spectroscopy and lays the groundwork for future studies on quantum-assisted approaches in real-world NMR applications. Quantum-assisted data processing will enable novel molecular characterisation methods and groundbreaking scientific discoveries as quantum technologies advance.