Benchmarking the Impact of Active Space Selection on the VQE Pipeline for Quantum Drug Discovery

TL;DR

This paper presents a systematic benchmark assessing how active space selection influences the performance of VQE algorithms in quantum drug discovery, using chemically grounded criteria and both simulation and real quantum hardware evaluations.

Contribution

It introduces the first comprehensive benchmark for active space choices in VQE, integrating chemical and hardware metrics for quantum drug discovery applications.

Findings

Active space selection significantly impacts VQE accuracy.

Chemically motivated criteria effectively classify molecule suitability.

Benchmark results guide future hardware-algorithm co-design in quantum chemistry.

Abstract

Quantum computers promise scalable treatments of electronic structure, yet applying variational quantum eigensolvers (VQE) on realistic drug-like molecules remains constrained by the performance limitations of near-term quantum hardwares. A key strategy for addressing this challenge which effectively leverages current Noisy Intermediate-Scale Quantum (NISQ) hardwares yet remains under-benchmarked is active space selection. We introduce a benchmark that heuristically proposes criteria based on chemically grounded metrics to classify the suitability of a molecule for using quantum computing and then quantifies the impact of active space choices across the VQE pipeline for quantum drug discovery. The suite covers several representative drug-like molecules (e.g., lovastatin, oseltamivir, morphine) and uses chemically motivated active spaces. Our VQE evaluations employ both simulation and quantum processing unit (QPU) execution using unitary coupled-cluster with singles and doubles (UCCSD) and hardware-efficient ansatz (HEA). We adopt a more comprehensive evaluation, including chemistry metrics and architecture-centric metrics. For accuracy, we compare them with classical quantum chemistry methods. This work establishes the first systematic benchmark for active space driven VQE and lays the groundwork for future hardware-algorithm co-design studies in quantum drug discovery.