  Conformation generation, also known as molecular unfolding (MU), is a crucial
step in structure-based drug design, remaining a challenging combinatorial
optimization problem. Quantum annealing (QA) has shown great potential for
solving certain combinatorial optimization problems over traditional classical
methods such as simulated annealing (SA). However, a recent study showed that a
2000-qubit QA hardware was still unable to outperform SA for the MU problem.
Here, we propose the use of quantum-inspired algorithm to solve the MU problem,
in order to go beyond traditional SA. We introduce a highly-compact phase
encoding method which can exponentially reduce the representation space,
compared with the previous one-hot encoding method. For benchmarking, we tested
this new approach on the public QM9 dataset generated by density functional
theory (DFT). The root-mean-square deviation between the conformation
determined by our approach and DFT is negligible (less than about 0.5
Angstrom), which underpins the validity of our approach. Furthermore, the
median time-to-target metric can be reduced by a factor of five compared to SA.
Additionally, we demonstrate a simulation experiment by MindQuantum using
quantum approximate optimization algorithm (QAOA) to reach optimal results.
These results indicate that quantum-inspired algorithms can be applied to solve
practical problems even before quantum hardware become mature.
