Quantum4Health Tests Quantum Computing on Galicia's 061 Emergency Service and Genomic Variant Calling: What Fujitsu España and Red.es Reported on 29 September 2026
Quentir Medicine Monitor
Evidence-based insights for quantum medicine. Published by Quentir Systems LLC · September 30, 2026.
When someone in rural Galicia calls the regional emergency number, how fast the call is answered depends on how many call handlers are on shift, and how fast help arrives depends on where the nearest ambulance or helicopter is waiting. Both are planning problems that a health service solves every week with limited staff and limited vehicles. A Spanish public research project now tests whether quantum and quantum-inspired computers can help plan them, and whether other quantum computing approaches can help read DNA.
The project is Quantum4Health, funded by Red.es, the public digital-transformation agency under Spain's ministry for digital transformation. Its progress was described in a project statement published by the Spanish outlet Demócrata on 29 September 2026 and distributed the same day through Europa Press. Fujitsu España and Fsas Technologies, both part of the Fujitsu Group, lead the work. Two technical tracks run side by side: optimization on the Fujitsu Digital Annealer, a quantum-inspired machine that runs on conventional digital hardware, and quantum machine learning for variant calling, the step in genome analysis that finds mutations in a patient's DNA.
The statement describes work in progress. According to the partners, the project has so far defined its use cases, built first models and prototypes, and compared different technical approaches on real problems from the health sector. It reports no accuracy figures, no response-time savings and no patient data results. That makes it a useful view of where European health services are pointing quantum computing today, and a thin basis for any claim about benefit.
Who runs Quantum4Health: Fujitsu España, Fsas Technologies, Red.es, DigitalES, Eurecat, CESGA, Qilimanjaro and Galicia's 061
The consortium mixes a large vendor, research centers and one real health-service user. Red.es funds and drives the initiative. DigitalES, the Spanish association of digital technology companies, acts as the lead industry partner. The technology center Eurecat, the Galician Supercomputing Center (CESGA) in Santiago de Compostela and the Barcelona quantum company Qilimanjaro bring advanced computing and quantum expertise. The end user is the Public Foundation for Health Emergencies of Galicia, known as 061, which runs emergency medical calls and dispatch for the region.
That last partner matters most for a hospital or health-system reader. Adrián Picazo, public sector account manager at Fujitsu España, says in the statement that the team starts from concrete daily problems of the health services and from the technology second, so that results can be evaluated from the early phases. A project with a working emergency service at the table can test its models against real rosters, real call volumes and real geography, which a laboratory benchmark cannot.
How the project plans 061 Galicia call-handler shifts, ambulance and helicopter bases, and drone deliveries
The first track is operational. With 061 Galicia, the team is working on staff scheduling for the people who answer emergency calls. The aim, in the partners' words, is to match available staff to forecast demand and coverage needs, reduce waiting lists and balance the workload. A second model looks at where to station emergency resources such as ambulances and helicopters, to improve territorial coverage and help shorten response times. A third line studies drones that carry medicines to, and samples from, rural and sparsely populated areas, where routes, timetables and resources can be optimized.
These are classic combinatorial problems: many shifts, many bases, many possible routes, and a large number of combinations to search. The Digital Annealer is built for exactly this kind of search. It borrows the annealing idea from quantum physics, but it is a classical digital processor, so it can run on today's problems at useful scale. Alejandro Borrallo, quantum computing manager at Fsas Technologies, describes this as the part of the project that can deliver value now.
Quantum pillar: computing. Technology readiness: TRL 3 of 9. This is the Monitor's provisional assessment of the quantum machine learning track: the partners report defined use cases, first models and early prototypes tested on emulators and real quantum processors, and the 29 September statement supplies no validated results on patient data or live emergency operations.
What quantum machine learning for variant calling and tumor imaging would have to show
The second track is clinical and further from use. Quantum4Health is testing quantum and hybrid machine-learning models for variant calling, the step that compares a patient's sequenced DNA with a reference genome to find differences and mutations. The partners frame it as research into whether such models could one day complement current artificial intelligence methods on complex genetic data. The project also explores medical image analysis and tumor identification, again as a possible complement to existing AI diagnostic tools.
These models are being developed and evaluated on emulators and on real quantum processors. The statement is careful about their status: the goal is to understand their medium-term potential and the current limits of the hardware, and to prepare solutions that can grow as quantum machines improve. No comparison with established variant callers is reported. Theoretical work suggests genomic data may be one place where quantum machines eventually help; our earlier note on Zhao, Preskill and Huang's April 2026 paper on single-cell RNA data and 60 logical qubits described one such case, and no machine has yet been demonstrated running that protocol at its required scale and circuit depth.
A quieter line of work may reach clinics sooner. The project uses tensor networks, a mathematical method taken from quantum physics, to shrink the size and memory needs of AI models used in health care. Smaller models could run close to where data is produced, for example inside a hospital or an ambulance base, instead of in a distant data center. Local processing can reduce data transfers and help places with weak connectivity, although any privacy gain depends on how it is built and safeguarded, and this line does not depend on quantum hardware at all.
What the 29 September statement leaves open, and what hospital buyers can ask Fujitsu and Red.es
The statement is a company announcement, so it sits low on the evidence ladder. It shows that a public funder, a major vendor and a regional emergency service agree on a set of problems worth testing. It does not show that any quantum or quantum-inspired method beats the scheduling software, location models or genomic pipelines that health services already use.
Three questions follow for a health-system buyer or a clinical lead. First, against which classical baseline will the 061 scheduling and ambulance-location models be compared, and on how many months of real data? Second, for variant calling, which reference datasets and which established callers will serve as the benchmark, and will the results be published for outside review? Third, which parts of the work rely on quantum processors, and which run on the Digital Annealer or on classical tensor-network methods, since that split decides what could be deployed soon. Clear answers to these questions, in a public report or a peer-reviewed paper, would be the next milestone to watch.
Sources
Primary source: Quantum4Health project statement by Fujitsu España and Fsas Technologies (Fujitsu Group), with quotations from Adrián Picazo and Alejandro Borrallo, published by Demócrata and distributed by Europa Press on 29 September 2026. Also drawn on: the public websites of project partners CESGA and the Galician health emergency foundation 061; the readiness assessment is this Monitor's own.