This guide helps you build the essential knowledge and skills needed to understand and use Cirq, Googleโs quantum programming framework.
It assumes minimal background and focuses on core concepts, math foundations, and practical resources.
- Stage 1: Core Prerequisites
- Stage 2: Learn Quantum Computing Fundamentals
- Stage 3: Mathematical Foundations
- Stage 4: Programming for Quantum Systems
- Stage 5: Hands-On Quantum Simulation
- Stage 6: Transition to Cirq
- Bonus: Books and YouTube Lectures
Before touching quantum code, make sure youโre comfortable with:
- Basic Python programming (variables, loops, functions, classes)
- Linear Algebra (vectors, matrices, complex numbers)
- Introductory Probability
- Python for Everybody (Coursera)
- Khan Academy: Linear Algebra
- 3Blue1Brown: Essence of Linear Algebra (YouTube)
- Khan Academy: Probability & Statistics
Here youโll understand qubits, superposition, entanglement, and measurement โ the core physics behind Cirq.
- IBM Quantum Learning Platform
- UChicagoX: Introduction to Quantum Computing (edX)
- Brilliant.org โ Quantum Computing Course
- Quantum Country (Free Online Textbook)
๐งฉ Focus Concepts
- Qubits and the Bloch Sphere
- Quantum Gates (X, Y, Z, H, CNOT, etc.)
- Measurement and Collapse
- Quantum Circuits
To understand Cirqโs gate operations and simulations, youโll need strong math intuition in complex numbers, vector spaces, and tensor products.
- HarvardX: Quantum Computing Fundamentals (edX)
- The Math of Quantum Computing (YouTube by Michael Nielsen)
- Linear Algebra Refresher for Quantum Computing (Qiskit Textbook)
๐ง Key Topics
- Matrix multiplication and unitary operations
- Eigenvalues/eigenvectors (important for quantum observables)
- Tensor products (multi-qubit systems)
- Dirac notation (bras โจฯ| and kets |ฯโฉ)
Here youโll practice implementing basic quantum concepts using Python.
๐งฉ Hands-on Goals
- Simulate qubit states using NumPy arrays
- Apply quantum gates (as matrices)
- Measure probabilities by squaring amplitudes
Before Cirq, experiment with other beginner-friendly frameworks to build intuition.
| Platform | Description | Link |
|---|---|---|
| IBM Quantum Composer | Visual drag-and-drop circuit builder | https://quantum-computing.ibm.com/composer |
| Qiskit SDK | Python library to simulate circuits | https://qiskit.org |
| Microsoft Q# / QDK | Code-first approach to quantum logic | https://learn.microsoft.com/en-us/quantum/overview |
๐งฉ Try building:
- A single qubit simulator
- The Bell State (entanglement)
- A Quantum Teleportation Circuit
Once you understand how quantum circuits and gates work, youโre ready for Cirq.
- Official Cirq Documentation
- Cirq GitHub Repository
- Quantum Computing with Cirq (Qiskit vs Cirq Comparison)
- Googleโs Quantum AI Tutorials
๐งฉ First Cirq Projects
- Build and simulate basic circuits
- Implement quantum gates (H, CNOT, Z, etc.)
- Measure qubits and visualize results
- Explore noise models and simulators
- Quantum Computation and Quantum Information โ Nielsen & Chuang (classic)
- Quantum Computing for Everyone โ Chris Bernhardt
- Dancing with Qubits โ Robert Sutor
| Stage | Focus | Example Tools |
|---|---|---|
| 1 | Python, Math Basics | Python, Khan Academy |
| 2 | Quantum Concepts | IBM Quantum, Brilliant |
| 3 | Linear Algebra, Complex Numbers | Qiskit Textbook |
| 4 | Simulation in Python | NumPy, Qiskit |
| 5 | Hands-on Circuits | IBM Quantum Lab |
| 6 | Dive into Cirq | Cirq Docs, Google AI |
โ
End Goal:
By following this path, youโll:
- Understand how qubits and gates work
- Be comfortable coding and simulating quantum systems
- Be fully prepared to explore Cirq for real quantum algorithm design
This is a list of beginner-friendly resources to help you gain the necessary foundational knowledge for learning the Cirq framework.
This guide assumes you are starting with minimal background in quantum computing and aims to equip you with the essential concepts and mathematical understanding needed before diving into the Cirq tutorials.
- Online Courses
- Interactive and Hands-on Platforms
- Programming-focused Resources
- Learning Path Recommendation
Description:
A free, comprehensive platform that guides you from quantum information basics to running code on real quantum hardware using Qiskit (a Python SDK similar to Cirq).
The conceptual knowledge gained here is highly transferable.
Description:
Designed for complete beginners and requires only a basic understanding of algebra.
Uses an intuitive approach to explain quantum phenomena and covers the essential components of quantum circuits and algorithms.
Description:
A beginner-friendly course from Fractal Analytics that makes quantum computing concepts accessible without heavy mathematics.
Covers core quantum mechanics, algorithms, and practical applications.
Description:
A more mathematically rigorous course that provides a solid foundation in vector spaces, qubits, and basic quantum algorithms.
Ideal for learners with a background in computer science or engineering.
Description:
For those who prefer visual, interactive learning, this course offers a hands-on approach to superposition, entanglement, and qubit circuits.
Description:
Provides access to real quantum hardware and interactive labs through the cloud.
Experimenting with simulators and small quantum circuits is an excellent way to build practical understanding of quantum gates and measurements.
Description:
A free toolkit that includes the Q# programming language, simulators, and extensive documentation with step-by-step tutorials.
A great way to learn quantum concepts through coding.
Description:
A self-paced, gamified series of programming exercises that help you learn quantum algorithms by writing code.
The Katas use Q#, but are excellent for reinforcing computational thinking and circuit logic.
Description:
A book tailored for developers that explains complex topics using Python and C++.
Includes simulations for over 25 algorithms โ bridging the gap between theoretical knowledge and hands-on practice.
Link: Check major booksellers such as Amazon or OโReilly.
-
Begin with the basics:
Start with a beginner-friendly course like UChicagoX or Brilliant.org to build an intuitive grasp of concepts such as superposition and entanglement. -
Learn the math:
As your comfort grows, reinforce your understanding of linear algebra and complex numbers.
Courses like HarvardX Quantum Computing Fundamentals or Courseraโs math primers are perfect for this. -
Get hands-on experience:
Use simulators and real quantum devices via the IBM Quantum Platform.
Practice building and visualizing quantum circuits to develop intuition for gate operations and measurements. -
Dive into Cirq:
With your foundational knowledge in place, youโll be ready to tackle Cirqโs official tutorials and documentation โ applying what youโve learned to simulate and run real quantum algorithms.
๐ก Tip:
Quantum computing is a deep but rewarding field โ focus on conceptual understanding first, then explore mathematical details and practical tools as you go.
Patience and curiosity are your best allies!
- Google DeepMind Official Site
- Google DeepMind ยท GitHub
- GitHub - google-deepmind/educational
- Using JAX to accelerate our research - Google DeepMind
- Google DeepMind (@googledeepmind) โข Instagram photos and videos
- Google DeepMind + Gemini for Developers | I/O 2025 Keynote
- New โblueprintโ for advancing practical, trustworthy AI | The University of Sheffield
- JAX / Colab:
- Neel Nanda (DeepMind):
- Projects: #Project The Bible LLM, #Project Rich supports poor, #Project Fluid Dynamics (waste and air pollution)
- Monetization/Credits: How to mine BITCOIN with your Home PC or Laptop!, Google cloud AI Startup credits
- Firebase Studio
- RMIT Hackathon 2025 (Kaggle)
- Substack (Cameron R Wolfe)
- Dive into Deep Learning
- Distill.pub
- Train an LLM From Scratch On NVIDIA Jetson Nano (Step-by-Step Guide)
- Tiktokenizer
- Sign in to Google AI Studio
- Domain names idea
- My name is Rajesh kumar Karra... (Google Doc)
- Google Quantum AI
- The Qubit Game
- The Quantum Atlas | Get Started
- Meet Willow, our state-of-the-art quantum chip
- The Theoretical Minimum |
- Quantum mechanics the theoretical minimum (PDF)
- Videos on Quantum Tech:
- Tom Rocks Maths:
- AP/College Math:
- 3Blue1Brown: 3Blue1Brown Home Page
- Math for Quantum Computing:
This section details the resources for the self-taught Physics goal.
- Key Strategies:
- Physics Institutions & Research:
- Textbooks & Courseware:
- Websites/Tools:
- Platforms & Courses:
- IBM Quantum Learning
- Qiskit | IBM Quantum Computing
- Register for the new Qiskit v2.X developer certification
- Qiskit Summer school
- Cirq | Google Quantum AI
- Cirq basics | Google Quantum AI
- Learn quantum programming โ PennyLane
- Azure Quantum
- Learn with Azure Quantum katas
- Installing Silq (Chrome Tab)
- Roadmaps & Business:
- 6-month Roadmap to learn about Quantum Computing for free | by Naem Azam
- RoadMap โ Quantum Computing - by Arnaldo Gunzi
- A guide to online resources for learning quantum computing
- 20 Profitable Quantum Computing Business Opportunities
- Make Money Out Of Quantum Computing | by Yung Lin Ma | Storytellings | Medium
- The WIRED Guide to Quantum Computing
- Five Things You Should Be Doing Now to Prepare for Quantum Computing (WP)
- Quantum Machine Learning Explained
- Quantum Machine Learning | PennyLane
- Quantum Machine Learning: A Roadmap for Technologists
- Physics-Informed Neural Networks (PINNs):
- Swift/iOS Development:
- General Programming Institutes:
- TensorFlow & Google:
- Tutorials | TensorFlow Core
- TensorFlow - YouTube
- Machine learning education | TensorFlow
- Seedbank โ discover machine learning examples โ The TensorFlow Blog
- Machine Learning Crash Course | Google Developers
- Learn AI & machine learning - Grow with Google
- Machine Learning Engineer Learning Path | Google Cloud Skills Boost
- Google for Developers Playlists
- LLMs & Neural Networks:
- Kaggle & Open Source:
- Google Cyber Security Certificate
- Gemini Share on Cyber Security
- Installing NetHunter on the Xiaomi Mi A3 | Kali Linux Documentation
- Kali Linux NetHunter install in 8 minutes (rootless) and includes Android 15
- Root Android (Kali Linux NetHunter install)
- Android Root PDF (Dropbox)
- fsociety GitHub Repo
- fsociety.dev
- Google Careers:
- Professional Work:
- Examinations & Hackathons:
- Google Research Playlists (YouTube)
- Google Labs
- Go behind the browser with Chromeโs new AI features
- Learn Your Way: Reimagining textbooks with generative AI
- Learn Your Way (Google)
- Computational Thinking & Scratch - Intro to Computer Science - Harvard's CS50 (2018)
- IELTS Status: In progress
- English Learning:
- Working Papers: (Check Google Classroom and fill the form for access.)
- Academic Files:
Ielts_information_for_candidates_us_version.pdfelectronics important questions .pdfBasic Electronics.pdfNumerical Analysis_material.pdfNumerical Analysis Imp Questions.pdfVector Calculus.pdfENGLISH 6th Semester.pdfVI-SEM grammer.pdf6th sem Telugu.pdftelugu 2nd sem important questions.pdf
- IBM Quantum Challenge 2024
- https://www.instagram.com/p/C7kKh4dNi18/
- Google Classroom Link
- IELTS IDP India Login
- IELTS Writing Task 1 Tips, Model Answers & More
- Quantum computing applications and simulations (Fermilab)
- How to prepare for IELTS Reading | How to succeed at IELTS (YouTube)
- Outlook Mail
- IELTS Online Course Checkout
- Partnership Accreditation for Landlords Property Search
- Cunningham Avenue, Hatfield, AL10 (PAL)
- Honeysuckle Gardens, Hatfield, AL10 (PAL)
Academic Information
This roadmap organizes your resources into a progression, starting with foundational AI/ML concepts and moving towards specialized, cutting-edge topics like JAX and LLMs.
This phase covers the core concepts of Machine Learning, starting with widely adopted Google frameworks and foundational coding skills.
- Core Concepts & Theory:
- Complete the Machine Learning Crash Course | Google Developers.
- Review the Most Important Algorithm in Machine Learning (from Karpathy's video).
- Work through the Dive into Deep Learning resource.
- TensorFlow Mastery:
- Follow the Machine Learning Engineer Learning Path | Google Cloud Skills Boost.
- Practice with tutorials on TensorFlow Core and explore examples on Seedbank โ discover machine learning examples โ The TensorFlow Blog.
- Watch the TensorFlow - YouTube channel for new techniques and tutorials.
- Data Structures & Google Dev Resources:
- Explore general learning resources from Grow with Google and Google for Developers Playlists.
This phase moves into the architecture and development of modern large language models, drawing heavily on Andrej Karpathy's content.
- Neural Network Fundamentals:
- Start the Neural Networks: Zero to Hero - YouTube playlist.
- Work on The spelled-out intro to neural networks and backpropagation: building micrograd (Andrej Karpathy) to understand core mechanics.
- Study resources from Andrej Karpathy (GitHub) & Andrej Karpathy (Website).
- LLM Development:
- Understand tokenization using Tiktokenizer.
- Follow the guide for Train an LLM From Scratch On NVIDIA Jetson Nano (Step-by-Step Guide).
- Look into the #Project The Bible LLM to apply your learning to a specific project.
- Review publications from Distill.pub for advanced visualization and understanding of models.
- Experimentation & Tools:
- Use Sign in to Google AI Studio for prompt engineering and model experimentation.
This phase focuses on JAX, Google DeepMind's preferred framework for high-performance ML research, building on the knowledge from the previous phases.
- JAX Introduction:
- Review the DeepMind blog post: Using JAX to accelerate our research - Google DeepMind.
- Start with the educational resources: GitHub - google-deepmind/educational.
- Follow the structured tutorials: GitHub - gordicaleksa/get-started-with-JAX (Tutorials).
- Utilize the community guides: JAX Guide | Kaggle.
- DeepMind Context & Research:
- Keep up with current research via the Google DeepMind Official Site and Google DeepMind ยท GitHub.
- Explore DeepMind's structure and personnel (e.g., Neel Nanda resources).
- Engage with competition platforms: RMIT Hackathon 2025 (Kaggle).
This plan sequences your existing resources, guiding you from foundational physics to advanced theoretical and quantum computing topics.
This phase establishes the bedrock knowledge needed for advanced study, primarily using MIT and general university resources.
- Core Physics & Mechanics:
- Start with introductory materials like the MIT OpenCourseWare (OCW) sequence and APยฎ๏ธ/College Calculus AB - Khan Academy.
- Utilize open-access textbooks like OpenStax.
- Reference foundational guides like Physics โ Susan Rigetti and the "Self-teach Physics" materials.
- Calculus & Algebra Refresher:
- Master the required mathematics using full-length YouTube courses:
- Deepen conceptual understanding with 3Blue1Brown Home Page and MIT OCW's Calculus Online Textbook.
- Programming for Physics:
- Begin using Python for computational tasks (e.g., in the algebra/precalculus courses) to prepare for modeling.
This phase moves into modern physics and the specialized mathematics for quantum information.
- Quantum Theory:
- Focus on the principles of Quantum Mechanics using The Theoretical Minimum lectures/book.
- Explore popular science articles and books like those recommended by Evening Standard to build intuition.
- Check research updates from Google Research - Physics and Physics | CERN.
- Quantum Math & Logic:
- Review Linear Algebra Crash Course for Machine Learning and Generative AI [Full 7h], as it is crucial for quantum computing.
- Study advanced math concepts with Tom Rocks Maths.
- Computational Physics:
- Explore root.cern for data analysis tools used in high-energy physics.
This final phase focuses on practical quantum programming and the intersection of physics and AI.
- Quantum Programming:
- Select your primary framework: Qiskit (IBM), Cirq (Google), or PennyLane.
- Complete the introductory material for the chosen platform, such as the IBM Quantum Learning path or Cirq basics.
- Follow suggested learning paths like the 6-month Roadmap to learn about Quantum Computing for free.
- Quantum Machine Learning (QML) & PINNs:
- Study the intersection of these fields with Quantum Machine Learning resources from PennyLane.
- Learn how to use Physics-Informed Neural Networks (PINNs) for solving differential equations, utilizing resources like the Introduction to Physics-informed Machine Learning with Modulus (NVIDIA) documentation.
- Swift/iOS Development:
- General Programming Institutes:
- Cyber Security:
- Career & Testing:
- IELTS (In progress):
- General Academic:
- Academic Files: (Check Google Classroom and fill the form for access.)
Ielts_information_for_candidates_us_version.pdfNumerical Analysis_material.pdf
- Chrome Tabs (Unsorted):