Quantum Computing in Medicine

Abstract

Quantum computing (QC) represents a paradigm shift in computational power, offering unique capabilities for addressing complex problems that are infeasible for classical computers. This review paper provides a detailed account of the current state of QC, with a particular focus on its applications within medicine. It explores fundamental concepts such as qubits, superposition, and entanglement, as well as the evolution of QC from theoretical foundations to practical advancements. The paper covers significant milestones where QC has intersected with medical research, including breakthroughs in drug discovery, molecular modeling, genomics, and medical diagnostics. Additionally, key quantum techniques such as quantum algorithms, quantum machine learning (QML), and quantum-enhanced imaging are explained, highlighting their relevance in healthcare. The paper also addresses challenges in the field, including hardware limitations, scalability, and integration within clinical environments. Looking forward, the paper discusses the potential for quantum-classical hybrid systems and emerging innovations in quantum hardware, suggesting how these advancements may accelerate the adoption of QC in medical research and clinical practice. By synthesizing reliable knowledge and presenting it through a comprehensive lens, this paper serves as a valuable reference for researchers interested in the transformative potential of QC in medicine.

Keywords: Monte Carlo simulation; drug discovery; healthcare; medical diagnostics; medicine; personalized medicine; quantum algorithms; quantum computing; quantum machine learning; radiotherapy optimization.

Figure 1

Timeline of key milestones in quantum computing’s evolution in medicine, from Feynman’s 1981 proposal to anticipated advancements in personalized medicine by 2024. Notable events include Shor’s algorithm in 1994, the first drug discovery algorithms in 2001, and IBM and Google’s demonstration of quantum supremacy in 2019, highlighting the transformative potential of quantum computing in healthcare.

Figure 2

Schematic diagram showing the key applications of QC in medicine, including drug design and molecular simulation, genomics and personalized medicine, medical diagnostics, AI-enhanced healthcare, and Monte Carlo simulations in radiotherapy. QC’s advanced computational capabilities offer transformative potential in improving accuracy, speed, and efficiency across these critical healthcare domains.

Figure 3

A schematic comparison between near-time hybrid programs (A) and the real-time hybrid quantum programs considered and implemented in this work (B) is shown. Notably, in the more advanced form discussed here, hybrid quantum–classical programs can make classical decisions based on the outcomes of quantum measurements. These decisions are then used to condition and control future quantum operations, all within the coherence times of quantum registers. Reproduced from reference [85] under the Creative Commons Attribution 4.0 International License (https://creativecommons.org/licenses/by/4.0/ (accessed on 2 October 2024)).