  Prediction of tautomers plays an essential role in computer-aided drug
discovery. However, it remains a challenging task nowadays to accurately
predict the canonical tautomeric form of a given drug-like molecule. Lack of
extensive tautomer databases, most likely due to the difficulty in experimental
studies, hampers the development of effective empirical methods for tautomer
predictions. A more accurate estimation of the stable tautomeric form can be
achieved by quantum chemistry calculations. Yet, the computational cost
required prevents quantum chemistry calculation as a standard tool for tautomer
prediction in computer-aided drug discovery. In this paper we propose a hybrid
quantum chemistry-quantum computation workflow to efficiently predict the
dominant tautomeric form. Specifically, we select active-space molecular
orbitals based on quantum chemistry methods. Then we utilize efficient encoding
methods to map the Hamiltonian onto quantum devices to reduce the qubit
resources and circuit depth. Finally, variational quantum eigensolver (VQE)
algorithms are employed for ground state estimation where hardware-efficient
ansatz circuits are used. To demonstrate the applicability of our methodology,
we perform experiments on two tautomeric systems: acetone and Edaravone, each
having 52 and 150 spin-orbitals in the STO-3G basis set, respectively. Our
numerical results show that their tautomeric state prediction agrees with the
CCSD benchmarks. Moreover, the required quantum resources are efficient: in the
example of Edaravone, we could achieve chemical accuracy with only eight qubits
and 80 two-qubit gates.
