Real-time viability of near-term variational quantum circuits for biomedical sensing

Ascertain whether near-term quantum processors implementing variational quantum circuits for quantum learning can meet real-time latency requirements in biomedical applications, such as intraoperative neural monitoring or closed-loop neurostimulation, while maintaining sufficient circuit expressibility and noise resilience.

Background

The paper discusses variational quantum circuits (VQCs) as a near-term approach to quantum learning for sensing, noting their reliance on iterative hybrid optimization with potentially high end-to-end latency. Biomedical use cases often require real-time feedback and control, creating a tension between latency, circuit expressibility, and noise resilience that has not yet been resolved for current hardware.

References

For biomedical applications demanding real-time feedback, such as intraoperative neural monitoring or closed-loop neurostimulation, these timescales may exceed clinically relevant thresholds. Whether near-term quantum processors can satisfy such timing constraints while maintaining sufficient circuit expressibility and noise resilience remains an open translational challenge.

— Four Generations of Quantum Biomedical Sensors  (2603.29944 - Jin et al., 31 Mar 2026) in Fourth Generation Quantum Medical Sensor: Quantum Learning (Quantum learning advantage subsection)