Quantum readiness & use-case screening
Identify quantum-suitable problems, define success metrics, and design defendable pilots that align with business objectives.
Building quantum systems for
the post-classical era
Strategy, execution, and capability development for quantum computing, AI/ML, and post-quantum cryptography
Mission
To lead the global transformation into the quantum era by building practical quantum and quantum-AI solutions and expanding access through education and partnerships.
Quantum Playground
Run on real QPUs in minutes
Most teams never run on quantum hardware because setup is complex. We've removed the barrier. Just bring an API key and run your quantum experiments on real QPUs through our HuggingFace projects—no infrastructure setup required.
Explore on HuggingFace →API key ready? Start in < 5 min
Featured Projects & Resources
- Live Demo
Hybrid quantum-classical link prediction
Knowledge graph analysis over Hetionet using quantum embedding techniques.
- Repository
Quantum cryptography RNG
Post-quantum secure random number generation implementations.
- Project
Materials science simulation
Quantum algorithms for molecular modeling and materials discovery.
- Framework
Hybrid QML toolkit
Production-ready quantum machine learning integration patterns.
- Research
Quantum portfolio optimization
Financial modeling using quantum annealing approaches.
- Tutorial
Getting started with quantum
Foundational concepts and practical first steps for technical teams.
Services
Strategy Execution Research to production
Build reproducible experiments, benchmark against classical methods, and deliver hybrid quantum-classical implementations.
Capability Workforce development
Practical learning programs and project-based training that move teams from theory to delivery.
Process
- 01
Define the outcome
Align on the decision or workflow, success metrics, and what "better" means.
- 02
Fit check
Determine whether the problem is quantum-suitable now, later, or best served classically.
- 03
Pilot design
Define datasets, constraints, baselines, and acceptance criteria for a defendable pilot.
- 04
Build and benchmark
Implement reproducible experiments and compare against classical baselines.
- 05
Report and roadmap
Share results, tradeoffs, and next steps for scaling, iteration, or pivot.
- 06
Enable the team
Build capability with workforce tracks, tooling, and knowledge transfer.
Leadership
Kevin Robinson
Chief Executive OfficerPhysicist and emerging-technology strategist focused on partnerships, workforce development, and equitable access to quantum computing.
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Jonathan Beale
Chief Technology OfficerSoftware and machine-learning engineer specializing in data architecture, experimentation frameworks, and production ML systems.
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Dr. Mark Jack
Quantum Science AdvisorScientific oversight and research mentorship. Focused on translating quantum theory into practical, scalable solutions.
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Get in Touch
Ready to explore quantum solutions for your organization? Let's start a conversation.