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Efficient molecular conformation generation with quantum-inspired algorithm.

Yunting Li1,2, Xiaopeng Cui2, Zhaoping Xiong3

  • 1Institute for Nanoelectronic Devices and Quantum Computing, Fudan University, Shanghai, 200433, China.

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|June 25, 2024
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Summary
This summary is machine-generated.

A new quantum-inspired algorithm significantly speeds up molecular unfolding for drug design, outperforming traditional methods. This approach offers a viable solution for complex optimization problems even before mature quantum hardware is available.

Keywords:
Drug designMolecular conformation generationMolecular dockingMolecular unfoldingQuantum annealingQuantum-inspired

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Area of Science:

  • Computational Chemistry
  • Quantum Computing
  • Drug Design

Background:

  • Molecular unfolding (MU) is vital for structure-based drug design but remains a complex combinatorial optimization challenge.
  • Quantum annealing (QA) shows promise for optimization, yet current hardware struggles to outperform classical simulated annealing (SA) for MU.
  • A novel quantum-inspired algorithm is proposed to address limitations of SA and current QA hardware for MU.

Purpose of the Study:

  • To introduce a quantum-inspired algorithm for molecular unfolding (MU) that surpasses traditional simulated annealing (SA).
  • To develop a compact phase encoding method for efficient representation space reduction.
  • To validate the approach using the QM9 dataset and compare performance against SA and quantum approximate optimization algorithm (QAOA).

Main Methods:

  • The molecular unfolding (MU) objective function, a high-order unconstrained binary optimization (HUBO) problem, was defined.
  • A compact phase encoding method discretized and encoded variables into binary representations.
  • The quantum-inspired simulated bifurcation algorithm was employed for optimization, with validation using density functional theory (DFT) data and QAOA simulations.

Main Results:

  • The quantum-inspired approach achieved negligible root-mean-square deviation (<0.5Å) compared to DFT-generated conformations, confirming its validity.
  • The median time-to-target was reduced by a factor of five compared to simulated annealing (SA).
  • Quantum approximate optimization algorithm (QAOA) simulations demonstrated the potential for achieving optimal results.

Conclusions:

  • Quantum-inspired algorithms, such as the simulated bifurcation algorithm, offer a powerful alternative for solving molecular unfolding problems.
  • The proposed phase encoding method significantly enhances efficiency by reducing the representation space.
  • These findings highlight the practical applicability of quantum-inspired methods for complex optimization tasks, even in the current era of developing quantum hardware.