M4D · SCIENTIFIC LEARNING RESOURCES
Research notebooks
Learn directly with established libraries. These resources are ordinary Jupyter notebooks, not Quantum Platform-specific commands, and do not allocate GPUs or QPUs.
FOUNDATIONS · BEGINNER · CPU · QISKIT / QISKIT AER
Introduction to quantum computing
Classical bits, qubits, superposition, measurement, Bloch visualisation, interference and two-qubit entanglement, with theory cells and executable Python.
This notebook demonstrates Bell-state correlations. A complete CHSH inequality/violation notebook is a separate follow-up lesson.
Open JupyterLab using the orange icon, follow the GitHub teaching notes, then use the terminal icon to copy a commit-pinned import command. Your workbench does not yet automatically import or execute the notebook.
BELL STATE · BEGINNER TO INTERMEDIATE · CPU FIRST · OPTIONAL GPU/QPU
Bell-state experiment: Qiskit Aer to hybrid execution
Prepare |Φ⁺⟩, run repeatable shot-based measurements and inspect correlations. The notebook starts with Qiskit Aer on CPU and discusses additional backends without requiring GPU/QPU access.
Prerequisites: Python basics, qubit notation and simple measurement probabilities. Source revision: 6867222b0eda (immutable). This is a curated reference, not an automatically approved ACP skill or an entitlement to execute on a remote backend.
Z-basis parity alone is not proof of entanglement or a CHSH Bell-inequality violation. See the notebook for basis-dependent measurements and limitations.
To work with your own copy, open a JupyterLab Terminal, paste the import command, then open notebooks/quantum-foundations/Bell_State_CPU_GPU_QPU.ipynb. The command downloads one reviewed, commit-pinned notebook into your own research home. It never creates a shared workspace or submits a job.
Research note: CPU Bell measurements, shots/seed, Qiskit versions and run artifact paths are reproducibility evidence. You can summarise them in Guide and explicitly approve an ACP memory; raw job output should not be silently promoted to long-term memory.
Notebook execution uses upstream Python SDKs directly. Resource entitlements, reviewed ACP skills and later Slurm/remote-QPU dispatch are separate capabilities.
