Benefit from the advantage of implementing quantum technology as an early adopter in the finance sector.

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Stafford Computing LLC


Our research and recognition

Quantum Machine Learning


Classical Machine Learning

We apply the best solutions from digital and quantum annealers to solve complex optimization problems related to portfolio optimization, credit risk analysis, and derivatives pricing.

We use cutting-edge hybrid quantum-classical algorithms to solve financial institutions' classification problems, including but not limited to: credit scoring, fraud detection, churn prediction and life-time value estimation.

In a Noisy Intermediate-Scale Quantum (NISQ) era, we still need to rely on classical machine learning, with the objective of benchmarking and ensuring the best possible performance, competing and complementing with the quantum counterparts.

Companies who trusted our experts 




United Kingdom

MDPI's Entropy Journal Paper Published

One of Stafford Computing's founders and quantum researchers, Javier Mancilla, recently co-authored a paper on the use of quantum machine learning in finance for fraud and default prediction. Accessible here.

Co-author of "Financial Modeling using Quantum Computing"  

Javier Mancilla, QML leader at Stafford Computing, also co-authored the book "Financial Modeling using Quantum Computing", available at Amazon. In this content developed for Packt, the reader can follow a journey through QML and optimization, to understand the potential impact of these technologies in their own finance-related companies. Available here.

Linkedin Quantum Top Voices 2022 

Javier Mancilla was also considered one of the top 20 Quantum Top Voices of 2022, awarded by the Spanish quantum organization, Barcelonaqbit.

Our projects and leaders have been highlighted by: 

Market trends

The early adopters of quantum technologies will capture 90% of the value 

Boston Consulting Group (BCG) presented in the Q2B 2022 that most of the early adopters of quantum solutions will capture the value of implementing such technologies. Later, when the advantage will be more broad, the pricing of vendors and services will go up, and alliances and partnerships for R&D will be less accessible.

The market is shifting to quick-wins in the quantum ecosystem 

The Quantum Insider data platform aggregates most of the investments and a lot of information about the quantum industry. One of the main discoveries between 2021 and 2022, is that the quantum software investments jumped from 7% to 38% of the total tracked annual amounts. These can interpreted as the necessity of the market to not only allocate efforts on long-term quantum hardware bets, but to extract advantage through software now.

Quantum machine learning and optimization are the most promising workloads 

Hyperion Research published a report in 2022 sharing important insights about the QC ecosystem extracted from a survey applied to 300 organizations that are already exploring quantum technologies. With regards to the most promising workloads tha could be succesfully implemented in their companies, machine learning and finance-oriented are the top 2 preferences.


Co-founder and Quantum ML Leader

PhD(c), Master in Data Management, and certified in quantum technologies by BIMTECH, MIT xPro, KAIST, IBM, and Saint Petersburg University. Specialist in quantum machine learning implementations for financial institutions.

Javier Mancilla

Tomás Tagliani

Co-founder and Machine Learning Leader

MBA(c), BSc in Actuarial Sciences. Certified Data Sciencist. ML Expert with 8+ years of experience developing projects in MercadoLibre, HSBC, Visa, Lenovo and others.

Degree in Information Technology. President of OneQuantum Africa as well as a founder member of QZimbabwe. Developer specialized in quantum SDKs for Python.

Quantum Machine Learning Developer

Lorraine Tsitsi

Iraitz Montalbán

External Senior Quantum Developer

PhD(c), Master in Mathematical Modeling, and certified in Data Protection and Quantum Technologies. Holds a Qiskit Developer Certificate and is a well-known specialist in multiple quantum SDKs and cloud services.

Anshul Saxena

Quantum Consultant for Finance

PhD(c) Financial Analytics, MBA, and certified by several institutions about quantum computing and artificial intelligence. Specialist in financial risk analytics.

Diego Tancara

Quantum Machine Learning Developer

PhD student in physics, Master's degree in theoretical physics, 5 years of experience in academic research in quantum optics, quantum computing and quantum machine learning.

Let's discover together the path towards quantum technologies adoption inside your company

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Ready to become a quantum-oriented company?

Address: 8 The Green, Suite 12267, Dover, DE 19901, USA.

Our method and technology

Quantum-classical classification models integrated in the machine learning architecture 

Stafford Computing's approach is to integrate the state-of-the-art of quantum algorithms and encoding methods into the company's machine learning architecture. Considering this path, our clients can quickly identify which technique is providing the best results, under different KPIs or objectives.

Using our structure, efforts in ML and QML will be equal and under a permanent goal: to provide the best possible outcome for key problems, such as:

  • Credit scoring
  • Fraud detection
  • Churn prediction
  • Delinquency forecast
  • Propension of acquire a new financial product

Having an incremental benefit on the model's performance can impact heavely on the unit economics that the organization is focused on.

Stafford Computing also offers state-of-the-art techniques for optimization problems, combining the strengths of both classical and quantum worlds.

Evaluating when a problem is suited for quantum optimization is as important as the selection of the algorithm and device it will run on. Some of the techniques improving out of the box solutions include:

  • Problem warm starting
  • Schedule function optimization
  • Hardware-interaction topology matching

For a sustainable solution, we also consider the access to HPC and GPU based devices. Digitization of annealing process may play a key role while maximizing approximation to a global solution of the problem, as well as decreasing the cost required for it.

Hybrid quantum-classical optimization for complex financial problems 

Niwred Ramírez

Finance and HR Manager

Experience in HR and finance management. Specialist in financial data analysis.

Our tech stack ecosystem 

Falcondale SDK