Company Name:


Incubation Round:

Explore, Experiment

Application Track:

Track 3

Challenge Name:

Synthetic data for proxy reconstruction

Product Description

This product benefits from cutting-edge technology (Quantum computing & ML) to generate synthetic data for assets for which regulators require longer timeframes to make investments. Institution’s internal models must conservatively assess the risk arising from less liquid positions and positions with limited price transparency under realistic market scenarios.

The procedure, called proxy reconstruction, involves estimating the behaviour of an asset using data from a similar asset under specific data standards. Proxies need to be conservative and are the key to investments when available data is insufficient or is not reflective of the true volatility of a position or portfolio.

Usage of Standards for data interoperability:

The OpenAPI Specification is a specification language for HTTP APIs that provides a standardized means to define your API to others. You can quickly discover how configure infrastructure, generate client code, and create test cases for your APIs. 

JSON Web Token is a proposed Internet standard for creating data with optional signature and/or optional encryption whose payload holds JSON that asserts some number of claims. The tokens are signed either using a private secret or a public/private key.

Structured Query Language, abbreviated as SQL, is a domain-specific language used in programming and designed for managing data held in a relational database management system, or for stream processing in a relational data stream management.

Kubernetes is an open-source container orchestration system for automating software deployment, scaling, and management. Originally designed by Google, the project is now maintained by the Cloud Native Computing Foundation.

Reach Timeline

*Expose phase is open to all Experiment phase teams


Bilbao & Madrid, Spain




Company maturity:

Pre-MVP and MVP

Investment level:

Seed, Series A

Funding raised:

< EUR 100,000

Collaboration opportunity:

Legal / Regulatory, Product development, Manufacturing, Marketing, Distribution, Service / Support, Product testing and revision, Investment

Company Description

QUANTUM MADS aims to lead the technological transition, harnessing the computation power of a new generation of quantum algorithms that can solve complex problems. The company is focused on designing new solutions to industrial problems of optimisation, simulation, and machine learning, which improve current proposals and provide new perspectives to tackle problems that have been intractable so far. 

Specifically, it develops AI, Data Analysis and Quantum Computing techniques to facilitate hybrid architectures that bring these models closer to real use cases. With these approaches, it builds solutions with clear advantages in terms of computational and energy efficiency in a wide range of multi-sector applications. To this end, it provides a QSaaS framework that enables its customer to tackle the most challenging problems.

Javier Gonzalez - CEO

Physicist and Mathematician with master’s in Machine Learning and Big Data. PhD in Financial Models with Quantum Computing at the UPV/EHU.

Giancarlo Gatti - Head of Engineering

Bsc & Msc in Physics. PhD in Quantum Computing with Superconducting Circuits at the UPV/EHU.

Daniel González - COO

BSc & MSc in Telecom Engineering. MiM in HEC Paris with focus on Digital Innovation and Entrepreneurship.