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HybridQuantum.AI

Hybrid Quantum AI Training & Certification Program

🚀 Hybrid Quantum AI Certification Program

Master the future of AI through a self-paced, hands-on certification program focused on neural networks, quantum machine learning, and hybrid orchestration. This course is designed for technically literate learners aiming to apply Hybrid Quantum AI in real-world scenarios — complete with labs, quizzes, and a full-code capstone project. See the code page for examples of code to be covered during the training.

🔒 Lock in Today’s Pricing

Register now to secure early access pricing for the HQAI certification tracks.

📩 Reserve HQAI™ Engineer Spot 📩 Reserve HQAI™ Architect Spot 📩 Reserve Both (Bundle)

🎯 Course Prerequisites

It is best if you have some experience programming in Python, and know a little about matrix math. Knowing some linear algebra would enable you to dig deeper to understand the theory. But this is not a quantum mechanics course.

If you can pick up Python programming, you’re in good shape—we’ll provide a primer on Python and other essentials you need to know before you dive into the good stuff!

🧱 Certification Module Structure

Each track is structured into purpose-built modules that build technical fluency, design capabilities, and hands-on experience — culminating in a capstone that demonstrates real-world HQAI™ proficiency.

🧠 HQAI™ Engineer Modules

  • Introduction to Hybrid Quantum AI Systems
  • Classical Foundations for Hybrid ML
  • Quantum Programming with PennyLane and Qiskit
  • Quantum Circuits and Information Encoding
  • Entanglement, Measurement, and PQCs
  • Variational Algorithms and Hybrid Optimization
  • Hybrid Quantum-Classical Neural Networks
  • Quantum Data Encodings and Feature Maps
  • Quantum Reservoir Computing and Dynamical Models
  • Designing Explainable Hybrid AI Systems
  • Hybrid AI Capstone Design and Implementation

🧭 HQAI™ Architect Modules

  • Strategic Framing of Hybrid Quantum AI Architectures
  • Business Objectives, Use Cases, and Hybrid Design Thinking
  • Algorithm Selection and Explainability Trade-Offs
  • Compliance, Traceability, and Human Oversight in HQAI Systems
  • System Architecture for Enterprise Deployment
  • Governance and Operational Maturity for Hybrid AI
  • Critical Evaluation of Models and Claims
  • Architect Capstone: Scalable Hybrid Quantum AI Design Proposal

🎓 Representative Capstone Projects

Certified HQAI Engineerâ„¢

  • Multimodal Fraud Detection: Combine CNN and quantum circuits with justification tracing in a hybrid AI system.
  • Quantum-Classical Sentiment Classifier: Integrate BERT-style embeddings with parameterized quantum layers and explainability tags.
  • Hybrid Time Series Forecasting: Use quantum reservoir computing and classical post-processing to model market or climate dynamics.
  • Explainable Hybrid Anomaly Detector: Fuse angle-encoded quantum states with classical rules-based layers for security event monitoring.

Certified HQAI Architectâ„¢

  • Healthcare Governance Blueprint: Design an HQAI architecture supporting AI-augmented diagnostics in regulated environments.
  • Explainability Framework for Financial AI: Map hybrid model decisions to audit and compliance standards in capital markets.
  • AI Governance Plan for Smart Infrastructure: Architect a scalable HQAI system for urban systems with human-in-the-loop oversight.
  • Quantum-Aware AI Deployment Model: Propose enterprise workflows for R&D teams transitioning to hybrid quantum solutions.

💵 Pricing & Access Terms

Transparent pricing with limited-time discounts, 12-month access, and lifetime certification for early adopters.
Best Value: Early Access Tier 1 (first 15 learners)
Next Best Value: Early Access Tier 2 (next 15 learners)
Early Access Rates Available While Seats Last!

Certified HQAI Engineerâ„¢

  • Early Access Tier 1 (first 15 learners): $749
  • Early Access Tier 2 (next 15 learners): $999
  • Full Price: $1,450
  • Access: 12 months from first login
  • Includes: Full training, labs, quizzes, capstone, certification review, email + Q&A support

Certified HQAI Architectâ„¢

  • Early Access Tier 1 (first 15 learners):Currently only available in bundle. See bundle price below.
  • Early Access Tier 2 (next 15 learners):Currently only available in bundle. See bundle price below.
  • Full Price: $1,250
  • Access: 12 months from first login (extends Engineer access)
  • Includes: Strategy modules, case design labs, capstone, certification review, email + Q&A support

Engineer + Architect Bundle

  • Early Access Tier 1 Bundle (first 15 learners): $1,250
  • Early Access Tier 2 Bundle (next 15 learners): $1,750
  • Full Price: $2,400 (save $300)
  • Access: 12 months for each track, staggered from first login
  • Includes: All modules, both certifications, all capstones, bundled PDF guides, Q&A support

🔒 Lock in Today’s Pricing

Register now to secure early access pricing for the HQAI certification tracks.

📩 Reserve HQAI™ Engineer Spot 📩 Reserve HQAI™ Architect Spot 📩 Reserve Both (Bundle)

📌 Certification Objectives

🎓 HQAI™ Engineer Certification Objectives

  1. Explain hybrid quantum AI at a conceptual and applied systems level.
  2. Demonstrate fluency in classical machine learning foundations and architectures.
  3. Implement quantum circuits using high-level APIs (PennyLane, Qiskit, Cirq).
  4. Formulate and solve optimization problems using quantum annealing platforms (e.g., D-Wave via AWS Braket).
  5. Distinguish between quantum gates, entanglement, and circuit-level concepts with practical application.
  6. Apply parameterized quantum circuits (PQCs) in hybrid settings.
  7. Use variational algorithms (VQE, QAOA) for hybrid optimization workflows.
  8. Compare annealing-based and gate-based approaches for solving QUBO problems.
  9. Build hybrid neural network models using quantum-classical interfaces.
  10. Interpret simulator/backend/classical interactions in code.
  11. Apply explainability to hybrid models (structure, interpretability, justification tags).
  12. Use data encodings (amplitude, angle, basis) in QML workflows.
  13. Simulate quantum behavior in code (e.g., entanglement, measurement).
  14. Implement quantum reservoir computing and contrast with classical RNNs.
  15. Design and validate a hybrid AI system in a capstone project.
  16. Document, justify, and trace quantum–classical decisions.

🧠 HQAI™ Architect Certification Objectives

  1. Synthesize hybrid quantum AI architectures for operational needs.
  2. Map business/scientific goals to hybrid algorithm strategies.
  3. Evaluate explainability, governance, and compliance implications.
  4. Design hybrid pipelines with feedback loops.
  5. Assess hybrid vs classical DL vs simulation-based trade-offs.
  6. Lead design reviews in regulated or high-risk environments.
  7. Define training/testing/production pipelines for enterprise hybrid quantum AI.
  8. Plan human-in-the-loop interpretability & decision auditing.
  9. Leverage variational models and encoding schemes.
  10. Advise on teams, toolchains, and operational maturity.
  11. Evaluate quantum annealing–based systems (e.g., D-Wave) for suitability in optimization-heavy domains.
  12. Integrate annealing platforms into hybrid cloud architectures (e.g., via AWS Braket or hybrid solver orchestration).
  13. Critically evaluate public hybrid quantum AI models/claims.
  14. Plan lifecycle for retraining, certification, and updates.

Note: The module structure and certification objectives presented here are in final draft form. Minor adjustments may be made prior to program launch to reflect refinements in curriculum, pedagogy, or platform implementation. All enrolled learners will receive final, up-to-date materials upon their first login.