Linkedin

Google AI Quantum Computing & Machine Learning

  • 24/7 Support
  • 6 Months
  • 240 Sessions

Course Description

Google AI Quantum Computing & Machine Learning is an advanced program designed to introduce learners to the powerful intersection of quantum computing and machine learning. This course covers quantum fundamentals, quantum algorithms, and how machine learning models can be enhanced using quantum principles. Learners gain exposure to Google’s Quantum AI tools and real-world use cases, understanding how quantum systems can solve complex problems in optimization, data analysis, and AI research. The course focuses on conceptual clarity, future applications, and industry relevance, preparing learners for the next wave of intelligent computing technologies.

One to One personalized training Schedule for Google AI Quantum Computing & Machine Learning

EkasCloud provides flexible training to all it's student. Here is our training schedule. Incase you find these timings difficult, please let us know. We will try to arrange appropriate timings based on your Convenience.

09-02-2026 Monday (Monday - Friday) Weekdays Regular 08:00 AM (IST) (Class 1Hr - 1:30Hrs) / Per Session
11-02-2026 Wednesday (Monday - Friday) Weekdays Regular 08:00 AM (IST) (Class 1Hr - 1:30Hrs) / Per Session
13-02-2026 Friday (Monday - Friday) Weekdays Regular 08:00 AM (IST) (Class 1Hr - 1:30Hrs) / Per Session
14-02-2026 Saturday (Monday - Friday) Weekdays Regular 08:00 AM (IST) (Class 1Hr - 1:30Hrs) / Per Session

Course Detail

Google AI Quantum Computing & Machine Learning is an advanced program designed to introduce learners to the powerful intersection of quantum computing and machine learning. This course covers quantum fundamentals, quantum algorithms, and how machine learning models can be enhanced using quantum principles. Learners gain exposure to Google’s Quantum AI tools and real-world use cases, understanding how quantum systems can solve complex problems in optimization, data analysis, and AI research. The course focuses on conceptual clarity, future applications, and industry relevance, preparing learners for the next wave of intelligent computing technologies.

Quantum computing combined with machine learning represents the future of intelligent systems. Learning this course helps you understand how quantum algorithms can accelerate machine learning models and solve problems that classical computers cannot. It prepares you for emerging technologies that are gaining strong interest from global tech leaders, research institutions, and enterprises. By learning now, you position yourself ahead of the curve in a high-impact, future-ready domain with long-term career potential.


Benefits of This Course
  • Understand the integration of Quantum Computing with Machine Learning

  • Learn quantum algorithms used in AI and data optimization

  • Gain exposure to Google Quantum AI tools and frameworks

  • Develop advanced analytical and problem-solving skills

  • Build a future-focused skill set in a rapidly growing technology area

  • Strengthen your resume with next-generation AI expertise

  • Prepare for careers in Quantum AI research, advanced AI, and innovation-driven roles

 
 
 

 We check your knowledge before we start the session.

 We build foundational topics first and core topics next.

 Theory classes with a real-time case study.

 Demo on every topic.

 You will learn how to design architecture diagrams for each service.

 Mock exam on every topic you understand.

 Exam Preparation

 Interview Preparation

 
 
 

Google AI Quantum Computing & Machine Learning Syllabus


6 Months Course 50% Theory 50% Lab Daily Home work 10 Real time Projects Unlimited Mock Exams Unlimited Mock Interview

Topics Covered

  • Advanced Quantum State Formalism

  • Dirac (Bra–Ket) Notation and Hilbert Spaces

  • Multi-Qubit Systems and Entanglement Theory

  • Quantum Measurement, Noise, and Decoherence

  • Advanced Quantum Gates and Circuit Design

  • Unitary Evolution and Circuit Expressivity

  • Foundational Quantum Algorithms

  • Grover’s and Deutsch–Jozsa Algorithms

  • Variational and Optimization Quantum Algorithms

  • Mathematical Foundations for Quantum Machine Learning

  • Optimization, Gradients, and Barren Plateaus

  • Advanced Google Cirq Programming

  • Quantum Machine Learning Models and Data Encoding

  • Hybrid Quantum–Classical Architectures

  • Quantum AI Use Cases, Ethics, and Capstone Project

 
 
 

Frequently asked question

Q: What if I miss a class?
A: We will stop the course for you because it is one-to-one or one-to-two students only.

Q: Will I get placement assistance?
A: Yes. You will get it once you are Certified.

Q: What if I have queries after I complete this course?
A: You can check our blog or send your queries from our website, social media like Facebook, Linkedin, Instagram, Twitter, and Youtube.

Q: How soon after signing up will I get access to the course?
A: Once you join the course, our Counselor Manager book a slot with our Trainer based on your and Trainer's available time.

Q: Is the course material accessible to the students even after the course training is over?
A: Yes, you can access our course material. Not only material, but you can also watch short and sweet videos from our Youtube Channel.

Q: What is the average salary ?   
A: $1,40,868

Q: How will this course help me pass the certification?
A: We provide separate Training for Exam and Interviews. In this Training will get more experience to pass Your Exam.

 
 
 

Admission Process

If a student want to take admission in any course he has to go with the following steps

Step 1
1 Hour Interview
  • Discuss Learning Goals: Understand the candidate’s career objectives, learning expectations, and prior experience (if any).
  • Personalized Course Recommendation: Based on the discussion, recommend the most suitable course.
  • Course Customization: Tailor the course plan to fit the candidate’s needs, including scheduling flexibility.
Step 2
3 Hour Assessment Session
  • Technical Skills Evaluation: Hands-on tasks or exercises to evaluate the candidate’s current technical understanding (for advanced courses).
  • Cloud Fundamentals Check: For entry-level courses, a basic assessment of cloud knowledge and IT skills.
  • Feedback & Results: Provide instant feedback and suggest an appropriate course path based on assessment performance.
Step 3
Final Enrollment

Upon successful completion of the assessment, candidates receive a customized learning path, course schedule, and payment options. Candidates can finalize their enrollment by agreeing to the course structure and payment plan.


Google AI Quantum Computing & Machine Learning Fees
£ 7000