Your operational data already has value. We determine whether its hidden relationships can support a real quantum advantage, show where classical methods remain stronger, and build better features when the opportunity is there.
Private betaBring database tables, CSV exports, Excel workbooks or sensor histories. SPECTRA Lens tests the business target, the relationships inside the data and strong classical baselines. The result is a clear answer on whether to stop, investigate or advance.
Private betaRaw columns rarely expose the cycles, interactions and connected behavior a quantum model can use. SPECTRA Prism transforms existing data into features that preserve those relationships, then tests whether the new representation creates a defensible advantage.
Private betaSPECTRA Relay integrates specialist quantum and classical models into one controlled workflow. It selects the appropriate model for each case, keeps existing systems in place and uses quantum only where measured performance justifies it.
We dedicate our expertise to discovering quantum methods that deliver tangible advantages across industries, from advanced predictive classification models to complex optimization challenges.
Our approach is rooted in rigorous analysis. We carefully evaluate problem complexity and scale to determine where quantum computing can truly excel. If classical methods already perform well for your use case, we’ll be the first to tell you.
Our philosophy centers on identifying problems where quantum encoding and solving, whether for speed, performance or both, can be efficiently achieved with today’s software and hardware stack.
Graduated from Creative Destruction Lab, Quantum stream, generation 2024 to 2025.
Demonstrated benefit in training quantum machine learning models with smaller samples that generalize better.
Part of the BIQAIN (Bizkaia Quantum Advanced Industries) program in Bizkaia, Spain.
Diverse applicability cases developed through participation in projects and proofs of concept across multiple industries.
A database, CSV or spreadsheet is only a container. Advantage depends on the decision you want to improve and whether the data contains relationships that classical models do not already capture efficiently.
We define the outcome that matters, such as equipment failure, demand, credit risk, customer behavior or network congestion. Quantum value is measured against that decision, not the file format.
We benchmark strong conventional methods first. If they already capture the signal reliably and economically, there is no credible reason to add quantum complexity.
When the data has promising structure, we engineer features around time, relationships, coupled variables and operating regimes. We test each change rather than assuming it helps.
The quantum candidate and tuned classical alternatives use the same data and evaluation rules. You receive a business recommendation: stop, improve the features, run a deeper validation or prepare a controlled deployment.
A compact founding team of quantum, data and industry practitioners. Every engagement is led personally by one of us — from the first data conversation to the final go / no-go recommendation.
Quantum Computing and Machine Learning specialist with over 15+ years of experience. Ph.D. in Quantum Computing and Master in Data Management. Certified in quantum technologies by BIMTECH, MIT xPro, KAIST, IBM, and Saint Petersburg University. Co-author of “Financial Modeling using Quantum Computing” (Packt) and author of “QML Unlocked” (Amazon).
Data and AI leader with 10+ years in machine learning and quantitative risk. Tomás has built and led teams delivering credit risk, fraud, and forecasting systems for clients across multiple countries. He holds an MBA and advises companies on AI and digital transformation.
PhD candidate at the University of Oviedo holding masters in quantum computing, mathematical modeling, and data protection. Co-author and lecturer in different institutions, has held roles of responsibility for a world-wide utility and helped build the technology roadmap for many other medium and large size companies as a consultant.
SPECTRA starts with the question a manager actually needs answered: can the data we already have improve this decision? We test strong conventional models first. If they already solve the problem, we say so. If important cycles and interactions are hidden in ordinary tables, we can build and test a controlled synthetic version that makes those relationships measurable.
Synthetic data is a controlled research copy, not a replacement for operational history. We check whether it has a similar overall shape, whether its variables move together in similar ways and whether a model trained on that copy still works when tested on real future data. Only then do we use it to demonstrate what engineered quantum features might make possible.
If standard models already solve the decision, a Quantum Neural Network adds unnecessary complexity. If the data is mostly random, no model has dependable information to learn. SPECTRA looks for the practical middle: a valuable gap remains, and repeated relationships in the data can still be measured.
Quantum suitability is not limited to these four examples. It can appear wherever a business process has repeating cycles, several variables that influence one another, and a valuable decision that standard models still struggle to make. Falcondale can build time, phase, interaction and network features from ordinary operational records, then test whether those features create a credible opening for a Quantum Neural Network.
These are candidate patterns, not automatic quantum use cases. SPECTRA Lens establishes the standard-model baseline first. SPECTRA Prism builds better representations only when the evidence justifies it. SPECTRA Relay introduces a Quantum Neural Network only for the cases where it proves additional value.
Ranking score, shown technically as ROC-AUC, tells us how consistently a model puts higher-risk cases ahead of lower-risk cases. Useful-alert score, shown as PR-AUC, matters when events are rare because it reveals how many alerts are worth acting on. Separation, shown as KS, measures how clearly the model divides the two groups. A gain is called proven only when its confidence interval remains above zero.
Important: Synthetic results are controlled demonstrations inside a calibrated test environment and are never claims about the original production target. Decisions made from existing operational data and results produced with synthetic data are always shown separately. Full methodology and result files are available on request: contact@falcondale.pro.
Run SPECTRA on your dataset ↗SPECTRA is delivered as a consultancy-led engagement for operational teams. We work from the data systems you already use, connect them to a decision that matters and deepen the work only when the evidence supports it. You receive a plain-language recommendation and a practical path forward.
Request a pilot ↗Bring the decision you want to improve and the data you already have. We reply within one working day. Discovery calls are held under NDA when required, and all evaluations run against your own operational history.
Use this address for pilot requests, dataset evaluations, research inquiries and press. Please include a one-line description of the business decision you are trying to improve.