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Bootstrap Embedding on a Quantum Computer.

Yuan LiuOinam Romesh MeiteiZachary E ChinArkopal DuttMax TaoIsaac L ChuangTroy Van Voorhis
Published in: Journal of chemical theory and computation (2023)
We extend molecular bootstrap embedding to make it appropriate for implementation on a quantum computer. This enables solution of the electronic structure problem of a large molecule as an optimization problem for a composite Lagrangian governing fragments of the total system, in such a way that fragment solutions can harness the capabilities of quantum computers. By employing state-of-art quantum subroutines including the quantum SWAP test and quantum amplitude amplification, we show how a quadratic speedup can be obtained over the classical algorithm, in principle. Utilization of quantum computation also allows the algorithm to match─at little additional computational cost─full density matrices at fragment boundaries, instead of being limited to 1-RDMs. Current quantum computers are small, but quantum bootstrap embedding provides a potentially generalizable strategy for harnessing such small machines through quantum fragment matching.
Keyphrases
  • molecular dynamics
  • energy transfer
  • deep learning
  • monte carlo
  • healthcare
  • machine learning
  • functional connectivity
  • big data
  • resting state