Classiq is a high-level quantum algorithm design platform that lets you design, optimize, analyze, and execute quantum programs through either a web-based IDE or a Python SDK. Its native SDK allows you to express algorithms at a functional level—without committing to specific gate-level implementations—while the built-in synthesis engine compiles your models into optimized circuits tailored to your constraints. Classiq’s visualization tools and seamless cloud integration make it equally powerful for experts and accessible for newcomers seeking hands-on quantum programming experience.
These notebooks series comprises five interactive Jupyter notebooks that will take you step-by-step through some quantum programming concepts:
- Your first Classiq model: Build and execute a simple arithmetic quantum program—adding a classical integer to a superposition—using the Qmod language and Classiq’s simulation backend.
- State preparation: Learn how to encode arbitrary quantum states, compare approximation strategies, and understand the impact of state preparation on circuit complexity.
- Exponential speedup with the Deutsch-Jozsa algorithm: Implement and run the Deutsch-Jozsa algorithm to see how superposition and interference yield deterministic quantum speedups over classical approaches.
- GHZ State: Generate multi-qubit entanglement by constructing a GHZ state using Qmod’s functional constructs and the
repeat operator, and compare manual vs. built-in implementations.
- Grover’s search: Design and synthesize Grover’s unstructured search algorithm—first leveraging the built-in oracle, then crafting your own—gaining insight into amplitude amplification and oracle construction.
By completing these tutorials, you’ll gain practical experience with Classiq’s Python SDK and Studio environment, mastering how to model quantum algorithms at a high level and delegate compilation and optimization to Classiq’s engine.
