Quantum advantage for differential equation analysis
Author(s)
Kiani, Bobak Toussi; De Palma, Giacomo; Englund, Dirk; Kaminsky, William; Marvian, Milad; Lloyd, Seth; ... Show more Show less
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Quantum algorithms for differential equation solving, data processing, and machine learning potentially offer an exponential speedup over all known classical algorithms. However, there also exist obstacles to obtaining this potential speedup in useful problem instances. The essential obstacle for quantum differential equation solving is that outputting useful information may require difficult postprocessing, and the essential obstacle for quantum data processing and machine learning is that inputting the data is a difficult task just by itself. In this study, we demonstrate that, when combined, these difficulties solve one another. We show how the output of quantum differential equation solving can serve as the input for quantum data processing and machine learning, allowing dynamical analysis in terms of principal components, power spectra, and wavelet decompositions. To illustrate this, we consider continuous-time Markov processes on epidemiological and social networks. These quantum algorithms provide an exponential advantage over existing classical Monte Carlo methods.
Date issued
2022-02-14Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science; Massachusetts Institute of Technology. Research Laboratory of Electronics; Massachusetts Institute of Technology. Department of Mechanical EngineeringJournal
Physical Review A
Publisher
American Physical Society
Citation
Kiani, Bobak Toussi, De Palma, Giacomo, Englund, Dirk, Kaminsky, William, Marvian, Milad et al. 2022. "Quantum advantage for differential equation analysis." Physical Review A, 105 (2).
Version: Final published version
ISSN
2469-9926
2469-9934