Learn-on-the-fly: An adaptive classical/quantum hybrid molecular dynamics scheme

Bradley P. Feuston

Rocco Martinazzo, Irène Burghardt

The McLachlan "minimum-distance" principle for optimizing approximate solutions of the time-dependent Schrödinger equation is revisited, with a focus on the local-in-time error accompanying the variational solutions. Simple, exact expressions are provided for this error, which are then evaluated in illustrative cases, notably the widely used mean-field approach and the adiabatic quantum molecular dynamics. Based on these findings, we demonstrate the rigorous formulation of an adaptive scheme that resizes on the fly the underlying variational manifold and, hence, optimizes the overall computational cost of a quantum dynamical simulation. Such adaptive schemes are a crucial requirement for devising and applying direct quantum dynamical methods to molecular and condensed-phase problems.

A. Santhosh Nantha, M. Jayalakshmi, K. Maharajan +2 more

Quantum machine learning (QML) has attracted significant interest in drug discovery due to its potential to model complex molecular interactions and improve prediction accuracy for protein-ligand binding affinity. However, most existing hybrid quantum-classical approaches rely on static architectures that apply quantum computation uniformly across all samples, regardless of molecular complexity or prediction confidence. Such static execution strategies lead to inefficient quantum resource utilization and limit scalability on near-term noisy intermediatescale quantum (NISQ) devices. In this paper, we propose an uncertainty-aware adaptive hybrid quantum-classical learning framework for protein-ligand binding affinity prediction that treats quantum computation as a selectively invoked resource rather than a constant component of the learning pipeline. The proposed framework first employs a classical ensemble-based regression model to generate binding affinity predictions along with an associated uncertainty estimate derived from ensemble variance. This uncertainty information is then used by a decision mechanism to dynamically select between classical-only inference, shallow quantum circuit execution, or deep quantum circuit execution. By adapting the quantum circuit depth and invocation frequency to the predicted uncertainty, the framework enables decision-centric allocation of quantum resources. Experimental evaluation of binding affinity data demonstrates that the recommended adaptive framework utilizes around 76 % less quantum resources than static hybrid quantum learning methods that use fixed-depth quantum circuits for all samples. The framework also preserves decision-level functionality and a steady inference workflow. These findings highlight the effectiveness of uncertainty-guided adaptivity as a practical strategy and offer a scalable foundation for future quantum-enhanced molecular learning.method for incorporating QML into drug development pipelines while taking hardware constraints into consideration.

Wilke Dononelli

In this work, we present the integration of Qiskit Nature's quantum chemistry solvers into the Atomic Simulation Environment (ASE), enabling hybrid quantum-classical workflows for force-driven atomistic simulations. This coupling allows the use of the Variational Quantum Eigensolver (VQE) and its adaptive variant (ADAPT-VQE) not only for ground-state energy calculations, but also for geometry optimisation, vibrational frequency analysis, strain evaluation, and molecular dynamics, all managed through ASE's calculator interface. By applying ADAPT-VQE to multi-electron systems such as BeH2, we obtain vibrational and structural properties in close agreement with high-level classical CCSD calculations within the same minimal basis. These results demonstrate that adaptive variational quantum algorithms can deliver stable and chemically meaningful forces within an atomistic modelling workflow, enabling downstream applications such as molecular dynamics and active-learning accelerated simulations.

Randy A. Bartels