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Grid-based methods for chemistry simulations on a quantum computer.

Hans Hon Sang ChanRichard MeisterTyson JonesDavid P TewSimon C Benjamin
Published in: Science advances (2023)
First-quantized, grid-based methods for chemistry modeling are a natural and elegant fit for quantum computers. However, it is infeasible to use today's quantum prototypes to explore the power of this approach because it requires a substantial number of near-perfect qubits. Here, we use exactly emulated quantum computers with up to 36 qubits to execute deep yet resource-frugal algorithms that model 2D and 3D atoms with single and paired particles. A range of tasks is explored, from ground state preparation and energy estimation to the dynamics of scattering and ionization; we evaluate various methods within the split-operator QFT (SO-QFT) Hamiltonian simulation paradigm, including protocols previously described in theoretical papers and our own techniques. While we identify certain restrictions and caveats, generally, the grid-based method is found to perform very well; our results are consistent with the view that first-quantized paradigms will be dominant from the early fault-tolerant quantum computing era onward.
Keyphrases
  • molecular dynamics
  • monte carlo
  • energy transfer
  • deep learning
  • machine learning
  • drug discovery
  • mass spectrometry
  • high resolution
  • quantum dots