Quantum Computing and Quantum Technologies in Drug Discovery and Therapeutics: Evidence, Benchmarking, and Translational Integration.

Abstract

Quantum technologies-quantum computing, quantum sensing, and quantum-enabled materials-are increasingly proposed as tools to accelerate drug discovery. Yet "quantum advantage" is frequently asserted without standardized benchmarks, clinically meaningful endpoints, or controlled comparisons against modern classical workflows. This review separates (i) quantum computing for molecular simulation and optimization, (ii) quantum sensing for structural/biophysical characterization and diagnostics, and (iii) quantum nanotechnologies for imaging and sensing, and then extends the framework to include device-led and physical therapies that increasingly co-evolve with drug development: photobiomodulation (red/NIR), focused ultrasound for blood-brain barrier opening and delivery enhancement, noninvasive neuromodulation devices (tDCS/TMS), and optogenetic therapies. We summarize demonstrated capabilities and constraints of NISQ-era computing, outline algorithmic classes for quantum chemistry and hybrid variational methods, evaluate quantum error-mitigation strategies and their limits, and contrast claimed performance with classical baselines in computational chemistry and machine learning. We conclude that near-term translational value is most substantial for quantum sensing and for device/physical platforms with established clinical evidence. In contrast, quantum computing remains principally hypothesis-generating until fault tolerance and reproducible advantage are established. Device-based modalities-including transcranial photobiomodulation for neuropsychiatric indications, focused ultrasound enabling CNS drug delivery, and home-supervised neuromodulation-are already reshaping therapeutic landscapes and clinical trial design. For drug discovery, the central requirement is not quantum novelty but validated decision impact, demonstrated under controlled benchmarks aligned with reproducibility expectations comparable to those evolving for AI/ML-driven methods in regulated contexts.