Quantum computing is poised to transform modern technology by solving computational tasks that cannot be resolved by classical computing. Surprisingly, one of the central challenges for quantum technologies, is the search for useful applications of (near-term) quantum machines. Combining methods from classical machine learning and quantum computing, the Demler lab has recently shown it is possible to achieve super-polynomial speedup in solving inference problems related to Nuclear Magnetic Resonance (NMR) spectroscopy.

NMR is one of the most powerful analytical techniques available to biology, however, it presents challenges in data interpretation. NMR spectra only directly observes the magnetic spectrum of a biological sample, whereas the ultimate goal is to identify and quantify chemical compounds. This is manageable for small molecules but becomes problematic when the complexity of the molecules increases, or when many spectra overlap. The resulting spectral profiling is often best done by a trained expert, which makes analysis slow and leads to inconsistent results, thus hindering clinical and industrial adoption of NMR. This work enables improved speed and consistency of NMR analysis for complex molecules, greatly enhancing the power of this analysis tool for the biopharma industry.

This work has been published in Nature Machine Intelligence. An article also appeared in the Harvard Gazette.