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27% More Surgeries Per Week
Quantum computing’s most consequential open question is which real-world problems are complex enough for quantum to outperform classical approaches, and whether organizations have the data, infrastructure, and institutional knowledge ready when the hardware arrives.
Hospital del Mar in Barcelona is building that foundation now. As part of Q-CARE, a project funded by CDTI, a public business entity in Spain that promotes innovation and technological development in domestic companies, Hospital del Mar spent the past year working with Qilimanjaro to apply quantum computing to one of healthcare’s most operationally complex problems: surgical scheduling. The results from the pilot project with simulated data already show what is possible: 27% more surgeries per week, with no new rooms, staff, or investment.
Within the Q-CARE project, our collaboration with Qilimanjaro has enabled us to explore advanced optimisation techniques aligned with quantum computing and quantum-inspired approaches, applied to operating room scheduling under real clinical and operational constraints.
Rafa Luque
Nursing Coordinator of the Surgical Process and Sterilisation, Hospital del Mar
Founded in 1905, Hospital del Mar is a 471-bed tertiary care hospital in Barcelona and one of Spain’s leading biomedical research institutions. It treats medium and high-complexity conditions across multiple surgical specialties, with a strong focus on research-integrated clinical practice.
1. Surgical Scheduling Is a Quantum-Class Problem
Scheduling a surgical block looks manageable from the outside. There are a fixed number of operating rooms, a list of patients, and a set number of hours in the day. In practice, every decision interacts with every other decision in ways that scale beyond what any manual process can track.
Each surgery has an expected procedure time, a cleaning time between cases, and a recovery time in the post-anesthesia unit. That recovery unit, with 15 beds at Hospital del Mar, is shared across all 11 operating rooms. Schedule too many long-recovery cases in the morning and the recovery unit fills up by midday. Patients back up. Rooms go idle. The afternoon is lost.
The hospital already had very well-established operating procedures and a strong manual planning process, which supported the high degree of robustness required in real clinical operations. As a large general hospital, Hospital del Mar also manages a broad range of surgical specialties, each with different clinical, staffing and resource requirements. The optimisation model’s main advantage was its ability to expand the decision space by evaluating a broader set of candidate surgeries, beyond those at the top of the waiting list, while considering all relevant constraints simultaneously. This made it possible to optimise surgical planning over a wider and more diverse set of options without compromising operational robustness.
2. Step One: Prove It Classically
Before quantum optimization can be applied to a problem at scale, three things are needed:
- A precise mathematical formulation of the problem
- Data to validate it against
- Proof that the approach works
The classical phase of this project delivers all three.
Working with Hospital del Mar’s procedure data across 24 surgery types, including operation duration, recovery time, and clinical waiting list guarantees, the team built a stochastic optimization model that schedules a full week simultaneously, using estimates of variable surgery durations and recovery times. The model coordinates patient assignment, room allocation, and start times while keeping the recovery unit within capacity throughout.
The optimized schedule reached 331 surgeries per week, compared to 267 under the current process. The recovery unit stayed within capacity at every point. A sensitivity analysis confirmed that the real bottleneck was not the number of rooms or cleaning crews but the coordination of recovery bed usage, something invisible with manual planning but directly addressable through optimization.
That 27% improvement is already significant for any hospital. More importantly for the quantum roadmap, it proves the problem is well-defined, the data is usable, and the potential gains are real. The model also functions as a simulation environment for strategic planning: Hospital del Mar can use it to quantify the expected impact of resource decisions, such as whether adding operating rooms would translate into a meaningful increase in weekly procedures.
The preliminary results, obtained in simulated scenarios using the data available, indicate potential improvements of close to 30% in planning capacity compared with traditional approaches, and provide us with a much richer quantitative basis to analyse and evolve our resource-management methodology, although still within an exploratory setting.
Rafa Luque
Nursing Coordinator of the Surgical Process and Sterilisation, Hospital del Mar
3. Starting Before Quantum Is Strictly Necessary
The classical model works well at the current scale of Hospital del Mar’s scheduling problem. The case for quantum solutions lies in how the problem grows: as constraint complexity increases due to larger patient populations, additional operating rooms, or tighter clinical requirements, classical optimization reaches time limits that make it impractical for real-time planning.
Quantum processors are built for exactly this class of scaling problem. That is why the readiness gap in quantum deployment is as much institutional as it is technical. Organizations that have spent time formalizing the right problems, structuring data, and validating results will be in a fundamentally different position than those starting from scratch when viable quantum hardware arrives.
Hospital del Mar now has a working optimization framework, a validated model of their surgical block, and a clear understanding of where the constraints are. When quantum hardware reaches the scale and reliability needed for instances of this complexity, the use case is ready.
Projects like this are essential to understanding where quantum technologies can create real value in optimization tasks. Since the scale and timing of this impact are still uncertain, we believe it is crucial to start early and collaborate closely with domain experts. This allows us to develop robust algorithms that can provide practical benefits today, while progressively incorporating quantum-accelerated methods as the hardware matures.
Jordi Riu
Algorithm Portfolio PO, Qilimanjaro Quantum Tech
4. The Roadmap
Both partners are now exploring how to bring the optimization model into day-to-day clinical operations, adapting it to work within real planning workflows using live patient data. The application of quantum solvers, to tackle problem instances that exceed the practical limits of classical approaches, will progressively become more viable as quantum hardware continues to mature.
The Q-CARE project is subsidized by the Centre for the Development of Industrial Technology and Innovation (CDTI) through the Science and Innovation Missions programme, within the Transfer and Collaboration Programme of the 2024–2027 State Plan for Scientific, Technical and Innovation Research, under the framework of the Recovery, Transformation and Resilience Plan, and supported by the Ministry of Science, Innovation and Universities. It is part of Mission 6: Digital Health, promoting research and development of new digital tools that improve the efficiency of healthcare systems, optimize care processes, and accelerate the discovery of new medicines.
