4.6 Article

Quantum algorithm for solving linear differential equations: Theory and experiment

Journal

PHYSICAL REVIEW A
Volume 101, Issue 3, Pages -

Publisher

AMER PHYSICAL SOC
DOI: 10.1103/PhysRevA.101.032307

Keywords

-

Funding

  1. National Basic Research Program of China
  2. National Natural Science Foundation of China [11974205, 11774197, 11905099, 11605005, 11875159, U1801661]
  3. National Key Research and Development Program of China [2017YFA0303700]
  4. Key Research and Development Program of Guangdong province [2018B030325002]
  5. Beijing Advanced Innovation Center for Future Chip (ICFC)
  6. Science, Technology and Innovation Commission of Shenzhen Municipality [ZDSYS20170303165926217, JCYJ20170412152620376]
  7. Guangdong Innovative and Entrepreneurial Research Team Program [2016ZT06D348]
  8. Guangdong Basic and Applied Basic Research Foundation [2019A1515011383]
  9. Spanish Government [PGC2018-095113-B-I00]
  10. EU FET Open Grant Quromorphic [828826]
  11. Basque Government [IT986-16, PRE-2015-1-0394]
  12. project QMiCS of the EU Flagship on Quantum Technologies [820505]
  13. project OpenSuperQ of the EU Flagship on Quantum Technologies [820363]

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Solving linear differential equations (LDEs) is a hard problem for classical computers, while quantum algorithms have been proposed to be capable of speeding up the calculation. However, they are yet to be realized in experiment as it cannot be easily converted into an implementable quantum circuit. Here, we present and experimentally realize an implementable gate-based quantum algorithm for efficiently solving the LDE problem: given an N x N matrix M, an N-dimensional vector b, and an initial vector x(0), we obtain a target vector x(t ) as a function of time t according to the constraint dx(t)Idt = Mx(t) + b. We show that our algorithm exhibits an exponential speedup over its classical counterpart in certain circumstances, and a gate-based quantum circuit is produced which is friendly to the experimentalists and implementable in current quantum techniques. In addition, we experimentally solve a 4 x 4 linear differential equation using our quantum algorithm in a four-qubit nuclear magnetic resonance quantum information processor. Our algorithm provides a key technique for solving many important problems which rely on the solutions to linear differential equations.

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