Quantum Algorithms for Eigenvalue Problems - presented by Lin Lin

Quantum Algorithms for Eigenvalue Problems

Lin Lin

Lin Lin
Quantum Algorithms for Eigenvalue Problems
Lin Lin
Lin Lin
University of California, Berkeley

The problem of finding the smallest eigenvalue of a Hermitian matrix (also called the ground state energy) has wide applications in quantum physics. In this talk, I will first briefly introduce the mathematical setup of quantum algorithms, and discuss how to use textbook quantum algorithms to tackle this problem. I will then introduce a new quantum algorithm that can significantly and provably reduce the circuit depth for solving this problem (the reduction can be around two orders of magnitude). This algorithm reduces the requirement on the maximal coherent time for the quantum computer, and can therefore be suitable for early fault-tolerant quantum devices. No prior knowledge on quantum algorithms is necessary for understanding most parts of the talk. (Joint work with Zhiyan Ding).

References
  • 1.
    L. Lin and Y. Tong (2022) Heisenberg-Limited Ground-State Energy Estimation for Early Fault-Tolerant Quantum Computers. PRX Quantum
  • 2.
    L. Lin and Y. Tong (2020) Near-optimal ground state preparation. Quantum
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Journal of Computational Physics Seminar Series
Journal of Computational Physics
Cite as
L. Lin (2023, March 13), Quantum Algorithms for Eigenvalue Problems
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Listed seminar This seminar is open to all
Recorded Available to all
Video length 1:06:52
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Disclaimer The views expressed in this seminar are those of the speaker and not necessarily those of the journal