Quantum advantage for differential equation analysis
| Autor: | Bobak Toussi Kiani, Giacomo De Palma, Dirk Englund, William Kaminsky, Milad Marvian, Seth Lloyd |
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| Přispěvatelé: | Kiani, Bobak Toussi, De Palma, Giacomo, Englund, Dirk, Kaminsky, William, Marvian, Milad, Lloyd, Seth |
| Rok vydání: | 2022 |
| Předmět: |
FOS: Computer and information sciences
Quantum Physics FOS: Physical sciences Numerical Analysis (math.NA) Mathematical Physics (math-ph) Computer Science - Data Structures and Algorithms Machine learning Quantum algorithm Quantum computation FOS: Mathematics Data Structures and Algorithms (cs.DS) Mathematics - Numerical Analysis Quantum Physics (quant-ph) Mathematical Physics |
| Zdroj: | Physical Review A. 105 |
| ISSN: | 2469-9934 2469-9926 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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