Free Quantum Computing Courses for 2026-27

🌈 You do not need to spend a single dollar to learn #quantum computing in 2026-27. Here are 9 free courses worth your time 👇 🟢 Beginner 🔹IBM Quantum Learning - build real circuits on IBM hardware from day 🔹one #Xanadu Codebook - interactive exercises right in your browser, no setup needed 🔹QWorld QBronze - #Python notebooks covering gates, states and Grover's algorithm 🔵 Intermediate 🔸MIT OpenCourseWare - full MIT lecture series with problem sets, completely free 🔸#Qiskit Textbook - quantum algorithms in real runnable code 🔸University of St Andrews on Coursera - audit free, covers hardware and circuit design 🟣 Advanced Google Cirq tutorials - NISQ algorithms and noise models from the Google Quantum AI team 🔹Caltech Quantum #Cryptography on edX - BB84, post-quantum standards, audit free 🔹Scott Aaronson lectures on #YouTube - the deepest free dive into why #quantum computers are powerful Total cost? Zero. The #quantum talent shortage is real. There are 3x more open roles than qualified candidates right now. These courses are the fastest free path to closing that gap. -------------------- 👉 Quantum Jobs List (global): https://lnkd.in/dYSJ3XBm WhatsApp channel for job alerts: https://lnkd.in/dxZ_umhR 👉 Quantum Jobs USA: https://lnkd.in/dN2UW5BR WhatsApp channel for US quantum Jobs: https://lnkd.in/dej6ZzQv #QuantumComputing #QuantumJobs #CareerChange #TechCareers #QuantumTech #FutureOfWork #JobSearch #Hiring2026 #QuantumJobsUSA #QuantumJobsList 📌 Content Usage #Policy: This infographic may be reused for educational or editorial purposes. Kindly provide proper attribution to ‘Quantum Jobs List ( www.quantumjobslist.com )’ with a link to the original source. #QuantumJobs #NoDegreeNoProblem #FutureTech #QuantumComputing #CareerChange #LearnQuantum #TechCareers #Upskilling

If you are looking to break into the quantum space or deepen your foundational knowledge, this curated list from Quantum Jobs List is an incredible starting point. The roadmap effectively breaks down the journey from zero prior experience to advanced topics like quantum cryptography and error correction, utilizing high-quality, free resources from industry leaders like IBM, Xanadu, Google, and top-tier universities. For anyone looking to understand the fundamentals of qubits, circuit building, or framework programming like Qiskit and Cirq, the barrier to entry has never been lower. It’s a great reminder that the most valuable investment you can make in this field right now is simply your time. Which of these platforms have you found most effective for hands-on learning? #QuantumComputing #QuantumPhysics #DeepTech #ContinuousLearning #Semiconductors

🚀 We are happy to announce that the third edition of the BasQ-IBM Road to Practitioner (R2P) program will be launched in September! Over the course of three months, participants will have an excellent opportunity to strengthen their understanding of Quantum Computing and develop their skills in Qiskit programming. 🎓 Who can apply? University students and professors, researchers, and industry professionals. Please, note that only applicants affiliated with universities, research centers or companies that are part of the BasQ ecosystem are eligible to participate in this program. 🗓️ Deadline Applications will remain open until Tuesday 30 June, 2026. Participation in the program is fully funded by BasQ. In return, selected participants are expected to show strong commitment, active participation, and engagement throughout the program. 🔗 More information available in the following link: https://lnkd.in/eiuBDDUA Eusko Jaurlaritza - Gobierno Vasco Zientzia, Unibertsitate eta Berrikuntza Saila Diputación Foral de Alava - Arabako Foru Aldundia Bizkaiko Foru Aldundia / Diputación Foral de Bizkaia Gipuzkoako Foru Aldundia Ikerbasque IBM IBM Quantum

Thrilled to share that my team secured 3rd place at the IBM SkillsBuild AI Hackathon, hosted by IBM and the University at Buffalo School of Engineering and Applied Sciences! 🏆 It was a privilege working alongside brilliant teammates Zainul Abideen Sayed, Dharshini Venkataramani, and Kyle Shi — couldn't have done this without them. A huge thank you to Kasey Zhang for her invaluable guidance and support throughout. Our project: an Agentic AI Research Assistant for UB CCR — built on IBM watsonx Orchestrate. The Problem: UB's Center for Computational Research (CCR) houses powerful HPC infrastructure — GPUs, CPUs, and research cloud resources. Yet many students, especially those new to research, struggle to access and utilize these resources effectively. That's wasted compute, wasted time, and missed research opportunities. Our Solution: We built a multi-agent AI system with two specialized sub-agents orchestrated by a supervisor: CCR Helper Agent — scrapes and indexes UB CCR's official documentation, answers HPC questions with cited sources, and is embedded directly into a CCR documentation prototype. Ask it "How do I submit a SLURM job?" and it tells you exactly how — with a source link. Professor Discovery Agent — takes your research background or bio as a prompt and surfaces UB faculty from Google Scholar whose work aligns with your interests and expertise. Perfect for students trying to identify the right professor to approach for RAship opportunities. The supervisor agent intelligently routes each query to the right sub-agent. Tech Stack IBM watsonx Orchestrate · Victor AI (data use case) · Python · CLIP Embeddings · Redis · Semantic Search · RAG Architecture Why it matters For a student trying to break into research at UB — knowing how to use the CCR cluster AND knowing which professors to reach out to can make all the difference. We built both into one system. Grateful to IBM and UB CSE for this opportunity, and proud of what my team built in such a short time. #IBM #Hackathon #AIAgents #watsonx #MachineLearning #UniversityAtBuffalo #HPC #RAG #ResearchAI #StudentInnovation

Researchers working with #MATLAB can now explore how to scale their workflows for high-performance computing and AI applications. From May 27–29, the NHR@TUD support team at our Department #ZIH offers a three-day workshop series on “MATLAB in HPC” — available both on-site and online. The workshop introduces participants to using MATLAB and the MATLAB Parallel Server efficiently on HPC clusters. Topics range from batch jobs and parallel computing to scalable AI systems, machine learning, deep learning with GPUs, and software development workflows with MATLAB and Simulink. Designed for researchers with basic MATLAB experience, the training provides practical insights into how computationally intensive workloads can be accelerated in cluster environments while staying within the familiar MATLAB ecosystem. 🗓️ May 27–29, 2026 (Registration until May 20) 📍 Hybrid format: online or on-site at TU Dresden Course language: English | Participation is free of charge 👉 Further information: https://lnkd.in/d3yuWw3a We are looking forward to welcoming interested researchers and MATLAB users! #HPC #ScientificComputing #CIDS #NHR

Quantum Computing Bootcamp | Day 04 | Session 07 Prof Jaskaran Singh's delivery was flawless. He communicated all the technical concepts related to Quantum Computing in an easy to understand language. After the session, I was trying to use the vocabulary and concepts from Quantum Computing to understand the issues related to Education Policy specially in the context of NEP 2020 --> NEP 2030 transition. What if the biggest failure in education policy is not policy design—but communication failure? Today’s session by Prof. Jaskaran Singh on Quantum Communications triggered a question that felt highly relevant to education policy. Most discussions around education reform focus on: Curriculum, Infrastructure, Funding, Technology But one critical variable is often ignored: Communication architecture. And that may explain why many education reforms struggle during implementation. Think about previous education policies: NEP 1968, NEP 1986, NEP 2020. A policy gets drafted at the national level. Ministry of Education creates vision. Then it flows through: State Governments → Regulators → Universities → Schools → Teachers → Parents → Students At every stage: (1) signal weakens, (2) interpretation changes, (3) incentives distort meaning, (4) vested interests intervene, (5) implementation quality declines, By the time policy reaches the classroom, the original intent is often diluted. This reminded me of communication challenges in quantum systems: (1) Noise → signal corruption (2) Decoherence → loss of original state (3) Fidelity → preserving message accuracy (4) Error correction → fixing transmission problems (5) Entanglement → recognizing that stakeholders are deeply interconnected (6) Quantum networks → integrated communication systems Education translation: (1) Teacher reforms impact student outcomes. (2) Curriculum reforms impact employability. (3) Scholarship reforms impact retention. (4) Infrastructure decisions impact access. Everything is interconnected. One major lesson for NEP 2030 Policy cannot remain a static PDF document. It must become a living communication system with real-time feedback loops. Policy: Intent → Communication → Interpretation → Execution → Feedback What NEP 2030 may need (1) multilingual explainers (2) AI-powered policy assistants (3) stakeholder dashboards (4) faster feedback loops (5) implementation monitoring systems (6) better institutional trust My biggest takeaway I am increasingly realizing that education reform is not just a curriculum problem. It is also: (1) an optimization problem (2) a communication problem (3) a governance problem And solving these may determine whether policy vision translates into real-world outcomes. I would love to hear your thoughts: Where do you think education systems lose signal most frequently? #QuantumComputing #EducationPolicy #NEP2020 #NEP2030 #QuantumCommunication #AIinEducation #Governance #ProcessExcellence #Innovation #FutureOfEducation #Leadership

Here is one of the many projects I created throughout my computing graduate course load. It is an interactive learning tool that helps visual and context learners understand how GPU matrix workloads (GEMV, GEMM) and execution strategies impact data movement, arithmetic intensity, and performance across memory hierarchies and distributed systems. Also, an exploit of the #CodexCreatorChallenge #Codex #OpenAI Handshake credits. If you get to explore it, I'd love to have your thoughts on possibly transforming this into a powerful and useful learning application😉. Also, in case you're curious, I intend to exploit my personal time recreating and breaking down a lot of my projects in my portfolio/blog. Subscribe for updates. The links are in the comments.

One of the most complete Computer Science learning repositories on GitHub. Over 80K+ stars and packed with free university-level video courses from: • MIT• Stanford• Berkeley• Harvard• CMU• Princeton• Caltech• IIT Topics covered: → Data Structures & Algorithms→ Operating Systems→ Distributed Systems→ Database Systems→ Computer Networks→ Machine Learning & Deep Learning→ NLP & Computer Vision→ Security & Cryptography→ Robotics→ Blockchain→ Quantum Computing From beginner-friendly CS50 all the way to advanced systems courses like MIT 6.824 Distributed Systems. What stands out is not just the quantity — it’s the quality and structure. This is the kind of resource that can genuinely change someone’s technical journey if they stay consistent. The curriculum is free.The commitment is yours. https://lnkd.in/eF-tB9_G #ComputerScience #Programming #Cybersecurity #AI #MachineLearning #SoftwareEngineering #DataScience #Robotics #DistributedSystems #Linux #Learning #Tech

Quantum Computing Bootcamp | Day 03 | Session 06 What if the Ministry of Education should think like a VQE algorithm? Today’s session by Prof. K Srinivasan on VQE (Variational Quantum Eigensolver) Algorithms and Implementation may have been one of the most important sessions for my long-term education policy framework. Why? Because VQE is fundamentally about solving optimization problems under computational constraints and that sounds very familiar to public policy. What VQE does A quantum computer explores possible solutions. A classical computer updates parameters. The system iterates continuously until it finds the most optimal state. In simple terms: Find the lowest energy state of a complex system. My policy translation In education, we are not solving for “lowest energy.” We are solving for: Lowest friction or Highest efficiency Think about the challenges faced by Ministry of Education We want to maximize: Equity, Learning, Access, Employability, Research output While minimizing: Corruption, Leakage, Dropouts, Inefficiency, Political resistance Maximize (Equity + Learning + Access + Employability) - Friction Where this logic applies (1) Scholarship allocation (2) Teacher deployment (3) School infrastructure planning (4) Higher education seat allocation (5) Curriculum redesign (6) Workforce forecasting The Hybrid Governance Insight VQE succeeds because it combines: Quantum + Classical systems Education reform may require something similar: (1) Technology + Human judgment (2) AI + Bureaucratic wisdom (3) Data + Ethics (4) Automation + Political feasibility Why this resonated with my Process Excellence background VQE follows: Iterate → Improve → Iterate → Improve That sounds remarkably similar to: Kaizen, Continuous Improvement, Lean Six Sigma The danger of local minima: Sometimes systems settle for politically convenient but structurally weak solutions. Example: Expanding colleges without improving quality. Looks good on paper. Fails in reality. Our emerging framework NEP 2020 → Policy Reform NEP 2030 → Optimization Reform NEP 2040 → Predictive Adaptive Systems NEP 2050 → Quantum-Enabled Governance The research question I’m now exploring Can hybrid optimization frameworks inspired by VQE help governments allocate scarce educational resources more efficiently while preserving equity? That feels like a far more important question than simply learning an algorithm. Most people attended to learn quantum computing. I’m increasingly exploring how these ideas can help redesign education governance systems at scale. Thanks to Col sai shankar and Afra Navas (QClairvoyance Quantum Labs Pvt. Ltd.) for organizing such an intellectually stimulating Quantum Computing Bootcamp. #QuantumComputing #EducationPolicy #NEP2020 #NEP2030 #AIinEducation #ProcessExcellence #QuantumTechnology #PublicPolicy #Innovation #FutureOfEducation #Leadership

-- Quantum ML Series 2 | Post 07 of 11 #QFD2 -- Post 6 ended with a working forward pass. Post 7 closes the loop. Today we train the quantum classifier for the first time. Here is what we built: A complete hybrid quantum-classical model. The quantum circuit handles feature processing. A classical linear layer maps the 16 quantum outputs to 10 digit class probabilities. Both components train together end to end. Cross entropy loss. Adam optimizer at 0.01 learning rate. 10 epochs. 500 training images. The training loop looks like standard PyTorch. Zero gradients. Forward pass. Compute loss. Call loss.backward(). Step the optimizer. The quantum specific part is invisible at this level. PennyLane handles the parameter shift rule gradient computation automatically. Gradients flow through the quantum circuit just like they flow through any classical layer. Honest results from my run: After 10 epochs on 500 training images the loss went from 2.29 down to 1.61. Test accuracy: approximately 35 to 45 percent. Random chance on a 10 class problem is 10 percent. So the model learned something real. But a classical logistic regression on the same 16 features would likely hit 60 to 70 percent. A classical CNN on the full image would hit 99 percent plus. I am sharing the exact numbers, not the best case. That is the point of learning in public. One honest observation. Training a quantum circuit is slow. Each backward pass runs the circuit twice per parameter for the parameter shift rule. With 32 quantum parameters that is a lot of circuit evaluations per batch. We trained on 500 images for practical reasons. The full 60,000 image MNIST training set would take a very long time on a laptop simulator. That constraint is real. It goes in the benchmark comparison in Post 8. Full article with all explanations below. https://lnkd.in/gBgf7avB Article link in first comment 👇 I am also currently open to full stack development and quantum computing opportunities. Six years of coding experience. Building in QML. Looking for a team working on something technically interesting. If that sounds like your team, feel free to reach out or connect. #QuantumComputing #QuantumML #MachineLearning #LearnInPublic #Developer #PennyLane #QML #Coding #FutureTech #QuantumMLSeries #OpenToWork #FullStackDeveloper