Quantum advantage for differential equation analysis
[1];
[2];
[5];
- Massachusetts Inst. of Technology (MIT), Cambridge, MA (United States); Massachusetts Inst. of Technology (MIT), Cambridge, MA (United States). Research Lab. of Electronics
- Massachusetts Inst. of Technology (MIT), Cambridge, MA (United States). Research Lab. of Electronics; Scuola Normale Superiore, Pisa (Italy)
- Massachusetts Inst. of Technology (MIT), Cambridge, MA (United States); Massachusetts Inst. of Technology (MIT), Cambridge, MA (United States). Research Lab. of Electronics; QuEra Computing, Boston, MA (United States)
- Independent Contractor, Cambridge, MA (United States)
- Massachusetts Inst. of Technology (MIT), Cambridge, MA (United States). Research Lab. of Electronics; Univ. of New Mexico, Albuquerque, NM (United States). Center for Quantum Information and Control (CQuIC)
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.
- Research Organization:
- Massachusetts Inst. of Technology (MIT), Cambridge, MA (United States)
- Sponsoring Organization:
- USDOE; National Science Foundation (NSF)
- OSTI ID:
- 1982747
- Journal Information:
- Physical Review A, Journal Name: Physical Review A Journal Issue: 2 Vol. 105; ISSN 2469-9926
- Publisher:
- American Physical Society (APS)Copyright Statement
- Country of Publication:
- United States
- Language:
- English
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