Introduction to Quantum Computing (D-Wave)

D-Wave’s free foundational course for anyone curious about quantum computing who wants a grounded, non-hype introduction. Unlike most free quantum intro courses, this one is explicitly focused on quantum annealing and optimization — the domain where D-Wave operates — rather than gate-based quantum computing.

What you’ll learn

• The differences between classical and quantum computing at a conceptual level
• How quantum computers physically work, without requiring a physics background
• The distinction between quantum annealing systems (D-Wave) and gate-model systems (IBM, Google, IonQ)
• What categories of problems quantum annealers are suited for: combinatorial optimization and sampling
• Real-world examples of optimization problems that quantum-classical hybrid systems are being applied to today
• An overview of recommended skills for a career in quantum computing

Course structure

Three modules, eight lessons total, delivered asynchronously. Access is provided for 90 days from registration at no cost.

Who is this for?

Anyone who wants to understand what quantum computing is and what D-Wave’s technology can actually do — without needing a math or physics background. The course is designed for business decision-makers, students exploring the field, and technical professionals who want the conceptual foundation before going deeper.

Context: D-Wave’s approach

D-Wave builds quantum annealers, not gate-based quantum computers. Quantum annealers are purpose-built for optimization: finding the lowest-energy state of a system, which maps to finding the optimal solution in a combinatorial problem. This is different from and complementary to gate-based quantum computing. Understanding this distinction early saves significant confusion when comparing D-Wave to IBM or Google.

This course sets that context clearly, which makes it a useful starting point even for learners who ultimately want to study gate-based systems — because understanding what each type of quantum computer is good for is foundational knowledge for the field.