Quantum algorithms for finance and security.

Our core expertise is developing quantum algorithms for real industrial use cases — and the engineering judgment to tell you what they will, and won't, require from quantum hardware.

What we provide

• 01

  Training & education

  Quantum computing fundamentals and algorithm literacy for technical teams and decision-makers.

• 02

  Use case development

  Translating a business problem into a quantum algorithm — from formulation to circuits.

• 03

  Hardware requirement estimation

  Qubit counts, gate depth, and error-correction overhead for running a given algorithm at useful scale.

• 04

  IP development

  Co-creation of intellectual property, patents and proprietary models, e.g. for quantitative finance.

• 05

  Technical due diligence

  Independent technical DD of quantum computing companies for investors and strategic partners.

• —

  Something else?

  Tell us the problem. contact@jos-quantum.de →

Interactive demos

Two of our use cases, explorable right in your browser.

Research papers

• Pauli Cloners for Pauli Channels S. F. Kerstan, M. Gallezot, T. Decker, M. Braun, N. Hegemann · 2026

  Presents a quantum circuit architecture for one-to-two cloning of N-qubit registers, extending the Niu–Griffiths architecture. Explores the relationship between Pauli errors, mutually unbiased bases, and Pauli cloning.

• QKD as a Quantum Machine Learning task T. Decker, M. Gallezot, S. F. Kerstan, A. Paesano · 2025

  Proposes considering Quantum Key Distribution (QKD) protocols as a use case for Quantum Machine Learning (QML) algorithms. Investigates optimizing eavesdropping attacks on the BB84 protocol using QML.

• Quadratic Unconstrained Binary Optimization Approach for Incorporating Solvency Capital into Portfolio Optimization I. Turkalj, M. Assadsolimani, M. Braun, P. Halffmann, N. Hegemann, S. Kerstan, J. Maciejewski, S. Sharma, Y. Zhou · 2024

  Incorporates Solvency Capital Requirement (SCR) into portfolio optimization using a quadratic proxy model suitable for QUBO. Uses machine learning to approximate the SCR function for quantum computing.

• Quantum amplitude estimation with error mitigation for time-evolving probabilistic networks M. C. Braun, T. Decker, N. Hegemann, S. F. Kerstan, C. Maier, J. Ulmanis · 2023

  Presents a method to model discretized time evolution of probabilistic networks on gate-based quantum computers. Uses quantum amplitude estimation to evaluate network states and probabilities with error mitigation.

• Towards optimization under uncertainty for fundamental models in energy markets using quantum computers M. C. Braun, T. Decker, N. Hegemann, S. F. Kerstan, F. Lorenz · 2023

  Formulates the unit commitment problem in energy production as a QUBO problem, considering uncertainties in renewable supply and demand. Shows how to find cost-saving solutions using quantum computers.

• Error Resilient Quantum Amplitude Estimation from Parallel Quantum Phase Estimation M. C. Braun, T. Decker, N. Hegemann, S. F. Kerstan · 2022

  Shows how phase and amplitude estimation algorithms can be parallelized to reduce gate depth. Demonstrates that this parallelization leads to vast improvements in resilience against quantum errors.

• A Quantum Algorithm for the Sensitivity Analysis of Business Risks M. C. Braun, T. Decker, N. Hegemann, S. F. Kerstan, C. Schäfer · 2021

  Presents a quantum algorithm for the sensitivity analysis of a risk model used at Deutsche Börse Group. Shows how the risk model can be implemented as a quantum circuit with quadratic speedup.

Patents

• SECURITY PROOF OF QUANTUM COMMUNICATION PROTOCOLS DE-102023129994-A1 · Priority 2023-10-30
• ML-BASED SECURITY PROOF OF QUANTUM COMMUNICATION PROTOCOLS WITH NOISY QUANTUM CHANNELS DE-102023129996-A1 · Priority 2023-10-30
• ML-BASED SECURITY PROOF OF QUANTUM COMMUNICATION PROTOCOLS DE-102023129995-A1 · Priority 2023-10-30

Contact

Get in touch at contact@jos-quantum.de. We post research and announcements on LinkedIn.