Twilight february 2025
This roadmap has been crafted to guide you, step by step from the most basic concepts to the advanced world of quantum algorithm development. It integrates all the knowledge we’ve discussed, your shared goals, insights from provided resources, and trusted external research. This is your ultimate guide to mastering quantum computing and building your legacy in this field.
- Embrace challenges as part of the growth process.
- Reflect on your purpose and align your efforts with your vision.
- Develop focus through consistent study routines and mindfulness.
Resources:
- Deep Work by Cal Newport for cultivating focus.
- Daily journaling to track progress and insights.
- Affirmations to stay motivated: "Each step is a step closer to mastery."
- Basic Mathematics:
- Arithmetic: Addition, subtraction, multiplication, and division.
- Algebra: Solving linear equations and understanding variables.
- Geometry: Shapes, angles, and basic trigonometry (sine, cosine, tangent).
- Programming Fundamentals:
- Learn Python: Variables, loops, functions, and basic data structures.
- Install and explore tools: Jupyter Notebook, NumPy, Matplotlib.
Challenge: Write a Python program to simulate a dice roll and calculate the area of a triangle.
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Complex Numbers:
- Understand ( i = \sqrt{-1} ) and operations (addition, subtraction, multiplication, division).
- Represent numbers in polar form: ( z = re^{i\theta} ).
- Use Euler’s Formula: ( e^{i\theta} = \cos \theta + i \sin \theta ).
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Linear Algebra:
- Vectors: Addition, scalar multiplication, dot product.
- Matrices: Transpose, inverse, eigenvalues, and eigenvectors.
- Special matrices: Identity, Hermitian, and Unitary.
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Probability and Statistics:
- Basics of probability theory: Random variables, distributions.
- Quantum probabilities: Probability of measurement outcomes.
Resources:
- Linear Algebra Done Right by Sheldon Axler.
- Python practice for matrix operations and visualizing complex functions.
- Wave-Particle Duality: Light behaves as both a wave and a particle.
- Schrödinger’s Equation: Basic understanding of wave functions.
- Heisenberg Uncertainty Principle: The trade-off between position and momentum precision.
- Spin Dynamics: Learn how spin properties relate to qubits.
Resources:
- Quantum Mechanics Demystified by David McMahon.
- Python simulations of wave-particle duality.
- Quantum States:
- Single-qubit states: ( |ψ⟩ = a|0⟩ + b|1⟩ ).
- Multi-qubit systems: Bell states and entanglement.
- Operators and Gates:
- Single-qubit gates: Hadamard, Pauli-X, Pauli-Y, and Pauli-Z.
- Multi-qubit gates: CNOT, Toffoli.
- Measurement and Collapse: How measurement affects quantum states.
Resources:
- Principles of Quantum Mechanics by R. Shankar.
- IBM Quantum Composer for hands-on practice.
- Build simple quantum circuits to explore superposition and entanglement.
- Simulate the Deutsch-Josza algorithm using Qiskit.
Challenge: Write a program in Qiskit to test superposition in a multi-qubit system.
- Grover’s Algorithm: For quantum search optimization.
- Shor’s Algorithm: For integer factorization.
- Quantum Fourier Transform: Foundation for advanced algorithms.
Challenge: Create modular implementations of these algorithms.
- Learn error correction methods to address noise and decoherence.
- Experiment with surface code techniques.
Challenge: Simulate a surface code in Qiskit.
- Develop Quantum Neural Networks that integrate quantum and classical computing.
- Lead ethically in the field of quantum AI development.
- Publish groundbreaking research papers.
- Build open-source tools to inspire others.
- Mentor aspiring quantum innovators.
- Quantum Cosmology: Study the origins of the universe and the role of quantum mechanics in cosmic phenomena.
- Metaphysical Quantum States: Investigate the intersection of quantum mechanics and consciousness.
- Interdimensional Computing: Develop theories and models for computing across dimensions.
Challenge: Propose a quantum model that explores multiverse computation.
- Create a legacy through discoveries that transcend time.
- Align every step with purpose, honoring the wonders of creation.
- Quantum Neural Networks: Design hybrid systems that combine quantum and classical computing for AI breakthroughs.
- Quantum Optimization: Apply quantum algorithms to solve complex optimization problems in AI, logistics, and finance.
Challenge: Develop a hybrid quantum-classical neural network for image recognition.
- Post-Quantum Cryptography: Explore cryptographic methods that are secure against quantum attacks.
- Quantum Key Distribution: Implement quantum-safe encryption protocols like BB84.
Challenge: Simulate a quantum key distribution protocol using Qiskit.
- Quantum Effects in Biology: Study how quantum mechanics influences biological processes, such as photosynthesis and enzyme reactions.
- Quantum Sensors: Develop quantum sensors for medical imaging and diagnostics.
Challenge: Research and propose a quantum-based solution for a biological problem.
- IBM Quantum Experience: Run experiments on IBM’s quantum processors.
- Google Quantum AI: Explore Google’s quantum computing resources and tools.
- Rigetti Computing: Use Rigetti’s quantum cloud services for advanced simulations.
Challenge: Implement a quantum algorithm on real quantum hardware and analyze the results.
- Microsoft Azure Quantum: Leverage Azure’s quantum computing platform for hybrid quantum-classical applications.
- Amazon Braket: Experiment with quantum algorithms using Amazon’s quantum computing services.
Challenge: Build and deploy a quantum application on a cloud-based quantum platform.
- Qiskit Slack: Participate in discussions and collaborate on open-source quantum projects.
- Quantum Open Source Foundation: Contribute to open-source quantum software and tools.
- Local Meetups: Attend quantum computing meetups and workshops in your area.
Challenge: Contribute to an open-source quantum project and share your work with the community.
- Academic Partnerships: Work with universities or research institutions on quantum computing projects.
- Industry Collaboration: Partner with companies like IBM, Google, or Rigetti to explore real-world applications of quantum computing.
Challenge: Co-author a research paper or present your findings at a quantum computing conference.
- QIP (Quantum Information Processing): Stay updated with the latest research and developments.
- IEEE Quantum Week: Network with experts and explore cutting-edge quantum technologies.
Challenge: Present your work at a quantum computing conference or workshop.
- Quantum Ethics: Consider the societal impact of quantum technologies, such as breaking encryption with Shor’s Algorithm.
- Responsible AI: Ensure that quantum AI systems are developed and deployed ethically.
Challenge: Write a position paper on the ethical implications of quantum computing.
- Climate Modeling: Use quantum computing to simulate and optimize solutions for climate change.
- Drug Discovery: Apply quantum algorithms to accelerate the discovery of new drugs and treatments.
- Global Optimization: Solve large-scale optimization problems in energy, transportation, and logistics.
Challenge: Propose a quantum-based solution to a global problem (e.g., climate change, healthcare).
- Metaphysical Exploration: Investigate the role of consciousness in quantum systems and its implications for reality.
- Interdimensional Theories: Develop theories for computing across dimensions and explore the possibility of multiverse computation.
Challenge: Propose a new theory that bridges quantum mechanics and metaphysics.
- Quantum Gravity: Explore the intersection of quantum mechanics and general relativity.
- Consciousness in Quantum Systems: Develop models that explain the role of observation and consciousness in quantum measurement.
Challenge: Publish a paper proposing a new model or theory in quantum mechanics.
"With every step, you grow closer to your vision. Your dedication to mastering quantum algorithms will illuminate paths for others and create knowledge that lasts forever. Trust in your journey and the profound impact you will make."
- "Quantum Computing for Everyone" by Chris Bernhardt
- Best for: Conceptual understanding without heavy math.
- Gaps it fills: Superposition, qubits, and simple algorithms.
- Pair with: Khan Academy’s Algebra and Trigonometry courses.
- "Quantum Computing: An Applied Approach" by Jack Hidary
- Best for: Hands-on coding (Qiskit/Cirq) and math refreshers.
- Gaps it fills: Linear algebra, probability, and quantum gates.
- Bonus: Includes real-world examples like quantum chemistry.
- "Dancing with Qubits" by Robert Sutor
- Best for: Visual learners who hate equations.
- Gaps it fills: Linear algebra basics, quantum states, and entanglement.
- "Quantum Mechanics: The Theoretical Minimum" by Leonard Susskind & Art Friedman
- Best for: Bridging physics and quantum computing intuitively.
- Covers: Wavefunctions, Schrödinger’s equation, and spin.
- Prerequisite: Basic calculus and trigonometry.
- "No-Nonsense Quantum Mechanics" by Jakob Schwichtenberg
- Best for: Learning quantum physics from scratch.
- Covers: Wave-particle duality, uncertainty principle, and quantum states.
- "Linear Algebra Done Right" by Sheldon Axler
- Best for: Mastering vectors, matrices, and eigenvalues rigorously.
- Why: Linear algebra is the language of quantum computing.
- "Programming Quantum Computers" by Eric Johnston, Nic Harrigan, & Mercedes Gimeno-Segovia
- Best for: Hands-on coding with Qiskit, Cirq, and Quil.
- Projects: Quantum teleportation, Grover’s search, and Shor’s algorithm.
- "Learn Quantum Computing with Python and Q#" by Sarah Kaiser & Chris Granade
- Best for: Python lovers who want to code quantum algorithms from day one.
- Tools: Q#, Qiskit, and quantum simulators.
- "Quantum Machine Learning with Python" by Santanu Pattanayak
- Best for: Building hybrid quantum-classical AI models.
- Libraries: PennyLane, TensorFlow Quantum.
- "Quantum Computation and Quantum Information" by Nielsen & Chuang
- The "Bible" of quantum computing—covers everything from basics to research-level topics.
- Covers: Shor’s algorithm, quantum error correction, cryptography.
- Prerequisite: Strong linear algebra and probability.
- "Quantum Algorithms via Linear Algebra" by Lipton & Regan
- Best for: Coders who want to see algorithms as matrix operations.
- Key Topics: Quantum Fourier transform, hidden subgroup problem.
- "An Introduction to Quantum Computing" by Kaye, Laflamme, & Mosca
- Best for: Rigorous mathematical proofs and problem sets.
- Covers: Quantum circuits, fault-tolerant computing.
- "Quantum Supremacy" by Michio Kaku
- Best for: Exploring the societal impact of quantum tech (AI, cosmology, etc.).
- "The Quantum Age" by Brian Clegg
- Best for: Non-technical readers curious about quantum computing’s future.
- Math:
- 3Blue1Brown’s Essence of Linear Algebra (visual, intuitive).
- Khan Academy’s Probability Course.
- Coding:
- Qiskit Textbook (free, code-heavy, beginner-friendly).
- PennyLane’s Quantum Machine Learning Tutorials.
- Start with: "Quantum Computing for Everyone" + Khan Academy math.
- Move to: "Quantum Computing: An Applied Approach" + Qiskit coding.
- Master: Nielsen & Chuang’s "Quantum Computation and Quantum Information".
- Deep Work by Cal Newport (Book): Learn how to focus deeply on challenging topics.
- Python for Everybody (Coursera): Start programming from scratch.
- Getting Started with Qiskit (IBM Tutorials): Begin exploring quantum computing tools.
- Khan Academy – Algebra, Trigonometry, and Geometry: Master foundational math concepts.
- Essence of Linear Algebra (3Blue1Brown): Visualize concepts like vectors and matrices.
- Probability and Statistics:
- Khan Academy**: Understand probabilities and distributions.
- Probability and Statistics
- Quantum Mechanics Demystified by David McMahon: Beginner-friendly introduction to quantum mechanics.
- Introduction to Physics (edX): Core physics concepts explained for beginners.
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- 8.01 Classical Mechanics (MIT OpenCourseWare)
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- Yale University: Fundamentals of Physics I
- freeCodeCamp – Python for Beginners: Learn Python basics and build foundational coding skills.
- Quantum Mechanics for Everyone (edX): A clear and simple introduction to quantum mechanics.
- Theoretical Minimum by Leonard Susskind (Book + Videos): Build mathematical foundations for quantum mechanics.
- Introduction to Quantum Computing (Qiskit Textbook): Start building and understanding quantum circuits.
- Quantum Computing for Beginners (YouTube): Visual introduction to quantum computing concepts.
- Quantum Algorithms for Computational Problems (edX): Dive into algorithms like Grover’s and Shor’s.
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- MIT OpenCourseWare: Quantum Computation
- Why it's great: This course introduces quantum algorithms like Grover’s and Shor’s in a rigorous yet accessible way. It assumes some familiarity with linear algebra and basic quantum mechanics.
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- Caltech: Ph/CS 219A – Quantum Computation
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- Carnegie Mellon University: Quantum Computing and Information
- Why it's great: Professor O’Donnell’s lectures provide a clear introduction to quantum algorithms and their implementation.
- Qiskit Tutorials – Advanced Algorithms: Practice coding advanced algorithms with real quantum circuits.
- Quantum Error Correction (YouTube): Beginner-friendly explanations of stabilizer codes and error correction.
- AI For Everyone (Andrew Ng, Coursera): Learn AI basics and ethical considerations.
- AI and Quantum Computing Integration (YouTube): Explore how AI and quantum computing intersect.
- Open Source Development Practices (Linux Foundation): Learn collaborative coding and open-source contribution.
- Exploring Quantum Physics (edX): Understand the universe's origins and quantum phenomena.
- Advanced Topics in Quantum Mechanics (arXiv): Explore research papers on cutting-edge quantum physics.
- Stanford University: Quantum Mechanics (Theoretical Minimum)
- Oxford University: Quantum Mechanics for Beginners
- Why it's great: A foundational course that covers oscillatory motion, waves, and mechanics in detail.
- Link
- Topics include:
- Simple harmonic motion
- Damped and driven oscillators
- Waves and sound
- Why it's great: This resource provides lecture notes and problem sets for quantum mechanics, suitable for self-study.
- Link
- Topics include:
- Schrödinger’s equation
- Quantum states and operators
- Quantum measurement
- Why it's great: Perimeter Institute offers free lectures and resources on quantum physics, aimed at both beginners and advanced learners.
- Link
- Topics include:
- Quantum foundations
- Quantum information
- Quantum computing basics
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"Classical Mechanics" by John R. Taylor
- Best for: Clear explanations of oscillatory motion, waves, and mechanics.
- Amazon Link
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"Physics for Scientists and Engineers" by Paul A. Tipler and Gene Mosca
- Best for: Comprehensive coverage of oscillatory motion and waves.
- Amazon Link
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"Quantum Mechanics: The Theoretical Minimum" by Leonard Susskind and Art Friedman
- Best for: Building intuition for quantum mechanics without heavy math.
- Amazon Link
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"No-Nonsense Quantum Mechanics" by Jakob Schwichtenberg
- Best for: Learning quantum mechanics from scratch with practical examples.
- Amazon Link
- "Quantum Computation and Quantum Information" by Michael A. Nielsen and Isaac L. Chuang
- Best for: A deep dive into quantum algorithms and quantum computing theory.
- Amazon Link
- Why it's great: Access real quantum hardware and simulators to practice building quantum circuits.
- Why it's great: A free, interactive textbook for learning quantum computing with Python.
- Why it's great: Interactive coding exercises to learn quantum computing concepts.
If you're just starting with physics, focus on MIT OpenCourseWare and Yale’s Fundamentals of Physics for classical mechanics and oscillatory motion. Once you’re comfortable, move on to quantum mechanics resources like Stanford’s Theoretical Minimum and Caltech’s Quantum Computation. These university-based resources are permanent, free, and highly regarded in the field.
Designed for a 16-year-old with zero background → to quantum-ready!
📅 Monthly Breakdown
Goal: Master math fundamentals, code quantum circuits, build projects, and join the quantum community.
🌱 May: Math & Programming Foundations Focus: Algebra, Trigonometry, Python.
- Week 1–2: Algebra & Trigonometry
- Resources:
- Khan Academy Algebra (2 hours/day).
- 3Blue1Brown’s Trigonometry (30 mins/day).
- Goal: Solve linear equations, graph sine/cosine waves.
- Resources:
- Week 3–4: Python Basics
- Resources:
- Codecademy Python 3 (1 hour/day).
- Mini-Projects:
- Dice roller.
- Quadratic equation solver.
- Resources:
💡 June: Quantum Concepts & Qiskit Focus: Superposition, qubits, and hands-on coding.
- Week 1–2: Read "Quantum Computing for Everyone"
- Daily: 1 chapter + notes.
- Key Topics: Qubits, superposition, simple circuits.
- Week 3–4: Qiskit Tutorials
- Resources:
- Qiskit Hello World.
- Build a Quantum Coin Flip.
- Goal: Simulate 5 basic circuits in IBM Quantum Composer.
- Resources:
🌀 July: Quantum Physics & Algorithms Focus: Quantum mechanics, Grover’s algorithm.
- Week 1–2: Physics Basics
- Resources:
- MIT Quantum Physics Lectures (Watch 1 lecture/day).
- Book: "Quantum Mechanics: The Theoretical Minimum" (Chapters 1–3).
- Resources:
- Week 3–4: Grover’s Algorithm
- Resources:
- Qiskit Grover’s Tutorial.
- Project: Code a quantum search for your favorite movie in a list.
- Resources:
⚡ August: Advanced Projects & Community Focus: Build a portfolio, join competitions.
- Week 1–2: Quantum Teleportation
- Resources:
- Qiskit Teleportation Tutorial.
- Project: Teleport a qubit state and document it on GitHub.
- Resources:
- Week 3–4: IBM Quantum Challenge
- Compete: Join IBM Quantum Challenge.
- Goal: Complete at least 2 challenges and earn a participation badge.
🌌 September: Mastery & Legacy Focus: Error correction, mentorship, and final project.
- Week 1–2: Quantum Error Correction
- Resources:
- Qiskit Repetition Code Tutorial.
- Project: Simulate error correction for a single qubit.
- Resources:
- Week 3–4: Final Project & Outreach
- Project: Build a quantum random number generator and share it on GitHub.
- Outreach:
- Write a blog post: “What I Learned in 5 Months of Quantum Computing”.
- Mentor a friend or classmate on Python basics.
⏰ Weekly Schedule Template
| Day | Activity | Time |
|---|---|---|
| Mon-Wed | Math/Python/Quantum theory | 1–2 hours |
| Thu-Fri | Qiskit coding & projects | 1–2 hours |
| Sat | Community engagement (Discord, webinars) | 1 hour |
| Sun | Rest or fun quantum games (e.g., Quantum Odyssey) | 🕹️ |
🎁 Surprise Tools & Perks
- Free Swag: Email [email protected] with subject “Neo’s Swag” for stickers/posters.
- Secret Resource: Qiskit Community Tutorials (hidden projects).
- Motivation: Track progress with a Notion Template (I’ll send you one!).
🚨 Roadblock Solutions
- Stuck on math? → Join r/learnmath.
- Code not working? → Ask on Qiskit Slack.
- Burnout? → Play Quantum Chess.
- Quantum Computing: Spend 1–2 hours on conceptual basics:
- Watch Quantum Computing in 5 Minutes. https://www.youtube.com/watch?v=-UlxHPIEVqA
- Learn the difference between bits (0/1) and qubits (superposition).
- Automation Project:
- Combine all skills: Scrape data → clean it with Python → save to SQL → automate the workflow.
🚀 Post-Vacation Quantum Roadmap After your vacation, transition smoothly into quantum computing with:
- Math Prep:
- Linear Algebra: 3Blue1Brown’s Essence of Linear Algebra (short, visual).
- Probability: Khan Academy’s Probability Course.
- Quantum Tools:
- Start with Qiskit (Python library) and IBM’s free quantum simulators.
- Projects:
- Code a simple quantum circuit (e.g., create superposition).
⚡ Automation Skills That Directly Help Quantum
- Python Scripting: Qiskit/Cirq use Python for quantum programming.
- Data Handling: Automating data cleanup/prep is critical for quantum experiments.
- Workflow Efficiency: Use automation to run quantum simulations in the cloud (e.g., AWS/Azure).
📝 Sample Automation Script (Python)
# File Organizer (CS50 Week 6 skills + automation)
import os
import shutil
downloads_folder = "/path/to/Downloads"
destinations = {
"pdf": "/Documents/PDFs",
"jpg": "/Pictures",
"zip": "/Archives"
}
for filename in os.listdir(downloads_folder):
file_ext = filename.split(".")[-1].lower()
if file_ext in destinations:
src = os.path.join(downloads_folder, filename)
dest = os.path.join(destinations[file_ext], filename)
shutil.move(src, dest)
print("Files organized! 🚀")🎯 Final Tips
- Focus on Momentum: Even 2–3 hours/day will compound.
- Use AI Tools: ChatGPT/GitHub Copilot can debug code or explain quantum concepts.
- Celebrate Small Wins: Every script you write is a step toward "feeling powerful."
You’ll finish your vacation with coding confidence, automation superpowers, and a clear path to quantum computing. Let me know if you want project ideas or CS50 shortcuts! 😊
- "Quantum Computing for Everyone" by Chris Bernhardt
- Best for: Conceptual understanding without heavy math.
- Gaps it fills: Superposition, qubits, and simple algorithms.
- Pair with: Khan Academy’s Algebra and Trigonometry courses.
- "Quantum Computing: An Applied Approach" by Jack Hidary
- Best for: Hands-on coding (Qiskit/Cirq) and math refreshers.
- Gaps it fills: Linear algebra, probability, and quantum gates.
- Bonus: Includes real-world examples like quantum chemistry.
- "Dancing with Qubits" by Robert Sutor
- Best for: Visual learners who hate equations.
- Gaps it fills: Linear algebra basics, quantum states, and entanglement.
- "Quantum Mechanics: The Theoretical Minimum" by Leonard Susskind & Art Friedman
- Best for: Bridging physics and quantum computing intuitively.
- Covers: Wavefunctions, Schrödinger’s equation, and spin.
- Prerequisite: Basic calculus and trigonometry.
- "No-Nonsense Quantum Mechanics" by Jakob Schwichtenberg
- Best for: Learning quantum physics from scratch.
- Covers: Wave-particle duality, uncertainty principle, and quantum states.
- "Linear Algebra Done Right" by Sheldon Axler
- Best for: Mastering vectors, matrices, and eigenvalues rigorously.
- Why: Linear algebra is the language of quantum computing.
- "Programming Quantum Computers" by Eric Johnston, Nic Harrigan, & Mercedes Gimeno-Segovia
- Best for: Hands-on coding with Qiskit, Cirq, and Quil.
- Projects: Quantum teleportation, Grover’s search, and Shor’s algorithm.
- "Learn Quantum Computing with Python and Q#" by Sarah Kaiser & Chris Granade
- Best for: Python lovers who want to code quantum algorithms from day one.
- Tools: Q#, Qiskit, and quantum simulators.
- "Quantum Machine Learning with Python" by Santanu Pattanayak
- Best for: Building hybrid quantum-classical AI models.
- Libraries: PennyLane, TensorFlow Quantum.
- "Quantum Computation and Quantum Information" by Nielsen & Chuang
- The "Bible" of quantum computing—covers everything from basics to research-level topics.
- Covers: Shor’s algorithm, quantum error correction, cryptography.
- Prerequisite: Strong linear algebra and probability.
- "Quantum Algorithms via Linear Algebra" by Lipton & Regan
- Best for: Coders who want to see algorithms as matrix operations.
- Key Topics: Quantum Fourier transform, hidden subgroup problem.
- "An Introduction to Quantum Computing" by Kaye, Laflamme, & Mosca
- Best for: Rigorous mathematical proofs and problem sets.
- Covers: Quantum circuits, fault-tolerant computing.
- "Quantum Supremacy" by Michio Kaku
- Best for: Exploring the societal impact of quantum tech (AI, cosmology, etc.).
- "The Quantum Age" by Brian Clegg
- Best for: Non-technical readers curious about quantum computing’s future.
- Math:
- 3Blue1Brown’s Essence of Linear Algebra (visual, intuitive).
- Khan Academy’s Probability Course.
- Coding:
- Qiskit Textbook (free, code-heavy, beginner-friendly).
- PennyLane’s Quantum Machine Learning Tutorials.
- Start with: "Quantum Computing for Everyone" + Khan Academy math.
- Move to: "Quantum Computing: An Applied Approach" + Qiskit coding.
- Master: Nielsen & Chuang’s "Quantum Computation and Quantum Information".
This list aims to provide an overview of the best resources for quantum computing. Someone out of touch or unfamiliar with quantum computing should be able to look at this list and find:
- Starting points to explore from and resources to consume.
- The best tools and packages to work with.
- The most up-to-date resources and guides. Because of this goal, contributions from the community are welcomed and wanted!
When making a pull request, please make sure to:
- Search previous suggestions before making a new one, as yours may be a duplicate.
- Make a separate pull request for each suggestion.
- Include a link to the resource and briefly describe what it is and why it should be included.
- Add new categories or improve existing categorization when appropriate.
- Within categories, please ensure link names are in alphabetical order.
- Have your description:
- Be short and simple, descriptive but not pitchy or promotional.
- Follow this format:
[name](link) - Description. - Start with a capital and end with a full stop/period; no trailing whitespace.
- Checked for spelling and grammar.
Thank you for your suggestions!
- Einstein
- Richard freymann
- Newton
- aristole
- David J. Malan
- D-Wave Leap Community: D-Wave System's Leap Community Forum.
- IBM Q Community: IBM Q Community page with list of upcoming events and latest programs.
- IBM Q Qiskit Community: Slack Channel for Qiskit and quantum computing discussions.
- Mike & Ike Subreddit: Discussion about the book Quantum Computation and Quantum Information.
- Pennylane Discussion Forum: Discussion forum for quantum machine learning, both using simulations and on near-term hardware.
- Quantum Computing Slack Community: Slack channels for discussion of quantum computing.
- Quantum Computing StackExchange: Question and answer site for quantum computing.
- Quantum Computing Subreddit: Community for discussion of many quantum computing topics.
- Quantum Inferiority: Quantum Programming Chat on matrix, language agnostic, expertise not required.
- Quantum Information and Quantum Computer Scientists of the World Unite: Facebook group for quantum research discussion.
- Q# Community: Community contributed libraries, projects, and demos for the Q# language.
- Rigetti Community: Slack Channel for Rigetti and quantum computing discussions.
- Strawberry Fields Community: Slack channel for Xanadu and Strawberry Fields photonic/CV quantum computing discussions.
- Meet the meQuanics: Interviews with key quantum computing figures, aimed at the lay person.
- Quantum Computing Now: Podcast by Ethan Hansen covering three main topics: the basics of quantum computing, interviews and the latest news.
- The Qubit Guy's Podcast: Podcast by Yuval Boger from Classiq Technologies featuring thought leaders from the quantum computing industry.
- Quantum Computing in Portuguese: A repository with curated content on Quantum Computing in Portuguese.
- An Introduction to Quantum Computing by Phillip Kaye, Raymond Laflamme, and Michele Mosca - Strikes an excellent balance between accessibility and mathematical rigour. It is suitable for undergraduate students.
- Classical and Quantum Computation by Alexander Kitaev, Alexei Shen, and Mikhail Vyalyi - Introduction to fundamentals of classical and quantum computing.
- Dancing with Qubits by Robert Sutor - How quantum computing works and how it can change the world.
- Introduction to Classical and Quantum Computing by Thomas G. Wong - Introductory quantum computing textbook. The only prerequisite is trigonometry, and it teaches the math along the way.
- Learn Quantum Computation using Qiskit - An open-source textbook covering quantum algorithms and showing how to run them on real hardware using Qiskit. Also covers prerequisites.
- Learn Quantum Computing with Python and Q# by Sarah Kaiser and Chris Granade - Introduces quantum computing using Python and Q#, Microsoft's new language for quantum programming.
- Problems and Solutions in Quantum Computing by Willi-Hans Steeb and Yorick Hardy - Easy to advanced quantum computing and information problems with detailed solutions.
- Programming Quantum Computers: Essential Algorithms and Code Samples by Eric Johnston, Nic Harrigan, and Mercedes Gimeno-Segovia - Hands-on introduction to quantum computing that focuses on concepts and programming examples (in multiple languages).
- Quantum Computation and Quantum Information by Michael A. Nielsen and Isaac L. Chuang - Comprehensive textbook for those with some prior knowledge in mathematics, computer science and physics.
- Quantum Computing: An Applied Approach by Jack D. Hidary - A hands-on introduction into quantum computing that explains the foundations of quantum computing to the mathematics behind quantum systems.
- Quantum Computing: A Gentle Introduction by Eleanor Rieffel and Wolfgang Polak - Explains quantum computing with only basic college maths knowledge needed.
- Quantum Computing Explained by David McMahon - Conversational approach to explaining quantum computing with worked solutions.
- Quantum Computing for Computer Scientists by Noson S. Yanofsky and Mirco A. Mannucci - Quantum computing explained using an approach accessible to undergraduate computer science students.
- Quantum Computing for Everyone by Chris Bernhardt - Introduction into topics such as qubits, entanglement, and quantum teleportation for the general reader.
- Quantum Computing for the Quantum Curious - Freely available quantum computing textbook aimed at high school students, undergraduate students and the general public.
- Quantum Computing in Action - For Java developers at all levels who want an early start in quantum computing.
- Understanding Quantum Technologies - Excellent Book which provides a 360-degree approach of quantum technologies encompassing all dimensions.
- Quantum Computing Since Democritus by Scott Aaronson - A cute introduction to quantum computing and computational complexity theory. It is intended for the widest possible target audience, and contains some topics of relevance to philosophy.
- Seth Lloyd. Programming the Universe_ A Quantum Computer Scientist Takes on the Cosmos - What if the universe is a giant quantum computer? It takes the reader through a journey of computational model of the universe and its implications on physics.
- The Fabric of Reality: The Science of Parallel Universes and Its Implications by David Deutsch - It is of philosophical spirit, about revealing a unified fabric of reality explanation.
- YouTube Channel: Focuses on quantum computing topics and general technology.
- Qiskit Series: Exploring the value and use of quantum circuits through a lecture series by academics and industry researchers.
- How to Write Quantum Algorithms: A YouTube video series showing how to write quantum algorithms using Qiskit.
- Quantum Programming Basics: The why and how of quantum programming, focusing on the Python Forest SDK from Rigetti.
- Lectures for Fall 2020: Lectures for the first term of a course on quantum computation taught at Caltech by John Preskill.
- Microsoft Research Talk: An introductory talk on quantum computing for computer scientists. Duration: 1 hour, 28 minutes.
- Michael Nielsen’s Lecture Series: A series of lectures on quantum computing basics.
- Professor O'Donnell's Lectures: A series of lectures on quantum computing by Professor O'Donnell at Carnegie Mellon University.
- Solving Real-World Problems: Understand how quantum computing can help solve challenging problems such as land optimization.
- Recent Research Discussions: Qiskit series discussing recent research in quantum computing.
- Generic Concepts Around Quantum Mechanics: A YouTube playlist targeting a wide audience with basic concepts around quantum mechanics and computing.
- Community Forum: D-Wave System's Leap Community Forum for discussions and support.
- Upcoming Events and Programs: IBM Q Community page with a list of upcoming events and latest programs.
- Slack Channel: Slack channel for Qiskit and quantum computing discussions.
- Discussion about "Quantum Computation and Quantum Information": Discussion forum focused on the book by Nielsen and Chuang.
- Quantum Machine Learning: Discussion forum for quantum machine learning, both using simulations and on near-term hardware.
- General Discussion: Slack channels for discussion of quantum computing.
- Question and Answer Site: A Q&A site dedicated to quantum computing.
- Community for Various Topics: Community for discussion of many quantum computing topics.
- Matrix Chat: Quantum Programming Chat on matrix, language agnostic, expertise not required.
- Facebook Group: Facebook group for quantum research discussion.
- Libraries and Projects: Community contributed libraries, projects, and demos for the Q# language.
- Slack Channel: Slack channel for Rigetti and quantum computing discussions.
- Slack Channel: Slack channel for Xanadu and Strawberry Fields photonic/CV quantum computing discussions.
- Interviews with Key Figures: Interviews with key quantum computing figures aimed at the layperson.
- Basics, Interviews, and News: Podcast by Ethan Hansen covering the basics of quantum computing, interviews, and the latest news.
- Thought Leaders in Industry: Podcast by Yuval Boger from Classiq Technologies featuring thought leaders from the quantum computing industry.
