The Quantum Ensemble Variational Optimization Algorithm: Applications to Molecular Inverse Design

TL;DR

The paper introduces QEVO, a quantum algorithm leveraging variational methods on near-term quantum hardware to efficiently design molecules with specific properties, addressing the combinatorial complexity of molecular design.

Contribution

It presents the QEVO algorithm, a novel quantum variational approach for molecular inverse design suitable for early fault-tolerant quantum computers.

Findings

QEVO can efficiently identify drug-like molecules with anticancer properties.

The method requires only shallow quantum circuits and a modest number of qubits.

Numerical simulations demonstrate QEVO's potential in complex molecular design tasks.

Abstract

Designing molecules with optimized properties remains a fundamental challenge due to the intricate relationship between molecular structure and properties. Traditional computational approaches that address the combinatorial number of possible molecular designs become unfeasible as the molecular size increases, suffering from the so-called `curse of dimensionality' problem. Recent advances in quantum computing hardware present new opportunities to address this problem. Here, we introduce the Quantum Ensemble Variational Optimization (QEVO) method for near-term and early fault-tolerant quantum computing platforms. QEVO efficiently maps molecular structures onto an orthonormal basis of Pauli strings and samples from a superposition state generated by a variational ansatz. The ansatz is iteratively optimized to identify molecular candidates with the desired property. Our numerical simulations demonstrate the potential of QEVO in designing drug-like molecules with anticancer properties, employing a shallow quantum circuit that requires only a modest number of qubits. We envision that QEVO could be applied to a wide range of complex problems, offering practical solutions to problems with combinatorial complexity.