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AI+ Quantum Practitioner (AT-410)

Quantum computing is opening new possibilities for solving problems that challenge classical systems, especially when combined with artificial intelligence. The AI+ Quantum Practitioner certification builds practical understanding of this intersection through quantum algorithms, machine learning, and deep learning.

Ethics, real-world case studies, and hands-on work connect theory with practical quantum applications. It prepares professionals to explore emerging quantum-AI opportunities confidently.

Overview

Overview

AI and quantum computing are beginning to work together on problems that traditional systems struggle to solve efficiently. This course explains that connection in practical terms. You’ll learn how quantum computing supports AI, where it can improve problem-solving, and how these technologies are being applied.

The course also covers responsible use, current industry trends, real-world examples, and practical activities using IBM quantum tools. A final workshop helps turn the theory into hands-on experience.

Prerequisites
  • Foundational understanding of AI concepts, programming languages, mathematics, and physics

  • Willingness to explore unconventional problem-solving approaches within AI and quantum computing

  • Openness to critically examine ethical dilemmas involving AI and quantum technologies

Target Audience
  • AI and quantum computing professionals deepening their technical expertise

  • Technology innovators and researchers exploring advanced AI and quantum applications

  • Data scientists and engineers expanding their QML and QDL skills

Exam Blueprint
  • Overview of Artificial Intelligence (AI) and Quantum Computing – 5%
  • Quantum Computing Gates, Circuits, and Algorithms – 11%
  • Quantum Algorithms for AI – 12%
  • Quantum Machine Learning – 12%
  • Quantum Deep Learning – 12%
  • Ethical Considerations – 12%
  • Trends and Outlook – 12%
  • Use Cases & Case Studies – 12%
  • Workshop – 12%
FAQs

1. Does the course include practical quantum computing experience?
Yes. Learners work with IBM Qiskit and complete workshop projects using QSVM, VQC, QNNs, and the Iris dataset.

2. Can the course be taken online?
Yes. The certification is available through live virtual instructor-led training or a self-paced online learning option.

3. Is in-person training available?
Yes. Classroom instructor-led training is available through AI CERTs Authorized Training Partners.

Course Outline

Module 1: Overview of AI and Quantum Computing
  1. Artificial intelligence fundamentals
  2. Machine learning fundamentals
  3. Deep learning fundamentals
  4. Ethical considerations in AI
  5. Quantum computing fundamentals
  6. Core quantum concepts
  7. Relationship between AI and quantum computing
  8. Introduction to IBM Qiskit SDK
  9. Practical quantum computing exercises
  10. Real-world case studies
  11. Quantum computing workshop activities
  12. Applications of AI within quantum environments

 

Module 2: Quantum Computing Gates, Circuits, and Algorithms
  1. Quantum gates and their representations
  2. Single-qubit gates
  3. Pauli-X gate
  4. Pauli-Y gate
  5. Pauli-Z gate
  6. Hadamard gate
  7. Qubit measurement
  8. Interpreting quantum computations
  9. Multi-qubit systems
  10. Quantum superposition
  11. Quantum entanglement
  12. Multi-qubit gates
  13. CNOT gate
  14. Building blocks of quantum algorithms

 

Module 3: Quantum Algorithms for AI
  1. Core quantum algorithms
  2. Deutsch-Jozsa Algorithm
  3. Bernstein-Vazirani Algorithm
  4. Grover’s Algorithm
  5. Faster database searching
  6. Quantum Fourier Transform
  7. Processing periodicity
  8. Variational quantum methods
  9. Quantum optimization
  10. Quantum Approximate Optimization Algorithm
  11. Applying quantum algorithms to AI
  12. Solving complex computational problems
  13. Quantum advantages over classical approaches

 

Module 4: Quantum Machine Learning
  1. Foundations of Quantum Machine Learning
  2. Quantum algorithms for machine learning
  3. Deutsch-Jozsa Algorithm
  4. Bernstein-Vazirani Algorithm
  5. Harrow-Hassidim-Lloyd algorithm
  6. Grover’s Algorithm
  7. Quantum classifier algorithms
  8. Quantum Fourier Transform
  9. Variational methods
  10. Quantum Approximate Optimization Algorithm
  11. Quantum Support Vector Machines
  12. Quantum k-Nearest Neighbors
  13. Quantum classification
  14. Quantum clustering
  15. High-dimensional data processing

 

Module 5: Quantum Deep Learning
  1. Foundations of Quantum Deep Learning
  2. Quantum Neural Networks
  3. Quantum Convolutional Neural Networks
  4. Quantum image processing
  5. Quantum Generative Adversarial Networks
  6. Quantum generative models
  7. Quantum Recurrent Neural Networks
  8. Sequential quantum data processing
  9. Quantum Variational Autoencoders
  10. Quantum data encoding
  11. Combining quantum computing with deep learning
  12. Solving computationally intensive problems
  13. Ethical quantum development
  14. Secure quantum practices

 

Module 6: Ethical Considerations
  1. Ethics in quantum computing
  2. Data privacy
  3. Quantum security
  4. Potential technology misuse
  5. Equitable access to quantum technologies
  6. Societal impacts
  7. Responsible quantum development
  8. Responsible technology deployment
  9. Ethical frameworks
  10. Secure development guidelines
  11. Technical considerations
  12. Regulatory considerations
  13. Current quantum computing developments
  14. Real-world applications
  15. Responsible usage standards

 

Module 7: Trends and Outlook
  1. Current quantum computing trends
  2. Recent quantum advancements
  3. Emerging quantum applications
  4. Research focus areas
  5. Quantum computing tools
  6. Quantum software
  7. Quantum algorithm development
  8. Future quantum predictions
  9. Potential technological breakthroughs
  10. Industry impact
  11. Quantum investment trends
  12. Funding landscape
  13. Key industry players
  14. Economic implications
  15. Future opportunities in quantum computing

 

Module 8: Use Cases & Case Studies
  1. Quantum computing use cases
  2. Quantum cryptography
  3. Drug discovery applications
  4. Optimization use cases
  5. Machine learning applications
  6. Quantum Machine Learning case studies
  7. IBM quantum initiatives
  8. Qiskit applications
  9. Quantum cloud services
  10. Quantum software advancements
  11. Industry partnerships
  12. Real-world quantum implementations
  13. Connecting theory with practical applications

 

Module 9: Workshop
  1. Practical quantum computing projects
  2. Applying theoretical quantum concepts
  3. QSVM for Iris dataset classification
  4. Quantum Machine Learning for pattern recognition
  5. VQC applications
  6. QNN applications
  7. Iris dataset experimentation
  8. Quantum variational circuits
  9. Quantum neural networks
  10. IBM Quantum Computers
  11. Quantum computer architecture
  12. Quantum computer capabilities
  13. Quantum development opportunities
  14. Practical algorithm experimentation
  15. Hands-on quantum computing experience
Note : A representative from Datacipher will contact you with further details
Payment Methods

At DataCipher, we offer a variety of payment options for our Fortinet courses. Here are the methods available:

Purchase Order (PO) – If your organization prefers using a purchase order, begin the registration process by clicking the Register button. At the conclusion of the registration form, choose the option “My company will pay for it, please send an invoice with the payment details.” Our training team will then provide an official quote and any necessary additional information that your accounts department might need to issue the PO.

Bank Transfer – DataCipher maintains bank accounts in both the US and Europe, accommodating all standard bank transfer methods such as IBAN/BIC, Swift, ACH, or wire transfer. To make a payment via bank transfer, simply use the Register button to sign up for your selected course.

Credit Card Payments – We accept payments from all major credit cards, including Mastercard, VISA, American Express, Discover & Diners, and Cartes Bancaires. Payments can be made directly through the registration link or by requesting an invoice that includes a web link for online payment. All transactions are secure, and DataCipher does not store any credit card information.

These options are designed to make the registration process as smooth and flexible as possible for all participants.

Status

Guaranteed to Run – DataCipher is committed to running this class unless unforeseen events such as an instructor’s accident or illness occur.

Guaranteed on Next Booking – The course will proceed once an additional student registers.

Scheduled Class – We have scheduled this course and rarely cancel due to low enrollment. We offer a “Cancel No More Than Once” guarantee, ensuring that if a class is canceled due to insufficient enrollment, the next session will run regardless of the number of attendees.

Sold Out – If the class is fully booked, please use our contact form to join the waiting list or to inquire about additional sessions. We’re here to accommodate your training needs and keep you informed of new opportunities.

Half and Full-Day Training

At DataCipher, we offer our training courses in both traditional full-day and convenient half-day formats. Our half-day classes are specifically designed for IT professionals who cannot be away from their workplaces for consecutive full days. This flexible schedule allows participants to dedicate a few hours to learning and then return to their regular work responsibilities.

The curriculum for both the full-day and half-day formats is identical. The primary difference is that the half-day classes spread the coursework over a more extended period, providing a balanced approach to professional education. DataCipher has been successfully running these half-day training sessions for several years, receiving consistently positive feedback from our customers. They appreciate the flexibility and report that the extended timeframe facilitates a deeper understanding of the material, as it gives them more time to absorb and reflect on the information learned.

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