Science-driven technology
SALGO-Q combines quantum-inspired optimization, artificial intelligence, and scientific computing to design advanced solutions for complex industrial and research challenges.
Built by scientists. Designed for real-world impact.
About SALGO-Q
SALGO-Q is a technology company created by physicists and researchers to translate advanced scientific methods into useful, deployable solutions.
We work at the intersection of physics, artificial intelligence, mathematical optimization, and high-performance computing. Our focus is on difficult problems where conventional approaches may be inefficient, expensive, or difficult to scale.
We choose methods according to the constraints, evidence, and desired outcome—not according to a technological label.
Solutions
From optimisation engines to scientific prototypes, SALGO-Q develops focused solutions that connect rigorous modelling with operational value.
01
Algorithms informed by concepts from quantum computing, designed to run on classical or hybrid infrastructure.
02
Purpose-built AI systems that support decisions, uncover structure in data, and accelerate analysis.
03
Numerical methods and simulation workflows for problems that demand physical insight and computational precision.
04
Bespoke computational components that move from proof of concept to integration with existing systems.
Technology
We select the computational approach according to the problem, rather than forcing every challenge into a single technological framework.
Alternative formulations and heuristics for hard optimisation landscapes.
Learning systems designed around the data, constraints, and decision context.
Clear formulations, objective functions, constraints, and measurable trade-offs.
Robust numerical techniques for simulation, inference, and computational experiments.
Parallel and scalable workflows for demanding computational workloads.
Domain structure and first-principles reasoning incorporated where they add value.
Team
Contact
Tell us about the challenge, the constraints, and the outcome you need. We will use that context to evaluate the most appropriate technical route.
Research & innovation
01 / 04
By developing and benchmarking quantum and hybrid quantum-classical algorithms, the project aims to deliver innovative optimization solutions for mobility, logistics, and finance, transforming cutting-edge research into commercially viable technologies.
The project is funded by the Generalitat Valenciana through the Strategic Technologies for Europe Platform (STEP), under the FEDER Comunitat Valenciana 2021–2027 programme.
02 / 04
By combining quantum algorithms, graph-based representations, and advanced numerical techniques, the project aims to improve the efficiency and accuracy of higher-order corrections in particle physics. It also explores the transfer of these methods to industrial applications, particularly through the simulation and optimization of complex processes.
The project is funded by the Generalitat Valenciana (Spain) through the DEGENT– Plan GenT2 programme (Oct. 2026 – Sep. 2030).
03 / 04
By developing quantum algorithms and testing them on both quantum simulators and real quantum hardware, the project aims to improve the efficiency of complex calculations and explore the potential advantages of quantum computing for precision predictions in particle physics.
The project was funded through the Astrophysics and High-Energy Physics (ASFAE) Complementary R&D&I Programme, supported by the Generalitat Valenciana (Spain), the Spanish Ministry of Science and Innovation, and the European Union's NextGenerationEU funds under the Recovery, Transformation and Resilience Plan (PRTR). Grant ASFAE/2022/009 (2023–2025).
04 / 04
The project focuses on developing efficient techniques for calculating higher-order scattering cross-sections, exploiting graph-based representations and Loop-Tree Duality, while applying machine-learning methods to particle collision reconstruction and the modeling of parton fragmentation functions. Its ultimate goal is to overcome computational bottlenecks and improve the accuracy and efficiency of theoretical predictions for particle collider experiments.
It was funded through USAL4EXCELLENCE, a Marie Skłodowska-Curie Actions (MSCA) COFUND fellowship programme under Horizon 2020, co-financed by the European Union, the Junta de Castilla y León, and the University of Salamanca (Spain). Grant Agreement No. 101034371; Project Ref. PROOPI-391 (October 2022 - June 2025).
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