Overview of solutions in quantum information technologies, part 2
The hardware available today does not support general-purpose quantum computation in medical settings, but specific subtasks such as sensing, photon-resolved detection, or image reconstruction can already be handled by quantum components embedded in classical systems. This article surveys recent developments across several branches of medical applications, with a complementary look at an analogous trend in machine learning. Nitrogen-vacancy centers in nanodiamonds enable nanoscale thermometry, high-resolution magnetocardiography, and real-time monitoring of free radicals inside living cells. In radiological imaging, photon-counting computed tomography is already entering routine clinical use, while QUBO-based image reconstruction on quantum annealers and gate-model processors points toward dose reduction in low-dose CT. Quantum-enhancedpositron emission tomography exploits polarization correlations of entangled annihilation photons to suppress background events and opens a path toward positronium-based biomarkers such as tissue hypoxia. Nuclear magnetic resonance ensemble computing is included to illustrate the scalability limits of fully quantum systems. The same hybrid pattern is then identified outside medicine, in efficient fine-tuning of large language models, where localized quantum modules reduce parameter counts without degrading task accuracy. The reviewed work differs in technical maturity, from systems already deployed clinically to proof-ofprinciple experiments such as X-ray spontaneous parametric down-conversion. Across this range, the quantum component handles one well-defined stage of an otherwise classical system.