Interactive Notebooks¶
Hands-on Jupyter notebooks for every encoding in the Quantum Encoding Atlas. Each notebook is a self-contained guide that walks you through creation, circuit generation, analysis, and a complete ML workflow using the library.
Running locally
Download any notebook and run it in your own environment:
Encoding Notebooks¶
| Encoding | Notebook | Highlights |
|---|---|---|
| Amplitude | amplitude_encoding.ipynb | Logarithmic qubit scaling, normalization, cross-backend verification |
| Angle | angle_encoding.ipynb | Rotation axis comparison (RX/RY/RZ), product states, baseline benchmarking |
| Basis | basis_encoding.ipynb | Binary encoding, Clifford simulability, minimal depth |
| IQP | iqp_encoding.ipynb | Entanglement topologies, provable classical hardness, expressibility |
| ZZ Feature Map | zz_feature_map.ipynb | Pairwise interactions, Pauli-ZZ entanglement, feature correlations |
| Pauli Feature Map | pauli_feature_map.ipynb | Configurable Pauli operators, multi-body interactions |
| Data Re-uploading | data_reuploading.ipynb | Repeated data injection, universal approximation, trainable layers |
| Hardware Efficient | hardware_efficient_encoding.ipynb | Native gate sets, device-aware topology, NISQ optimization |
| Higher-Order Angle | higher_order_angle_encoding.ipynb | Polynomial feature maps, non-linear transformations |
| QAOA-Inspired | qaoa_encoding.ipynb | Mixer/cost layer structure, combinatorial encoding |
| Hamiltonian | hamiltonian_encoding.ipynb | IQP/Pauli-Z/XY/Heisenberg types, evolution time, Trotter steps |
| Trainable | trainable_encoding.ipynb | Parameter management, initialization strategies, variational training loop |
| Symmetry-Inspired | symmetry_inspired_feature_map.ipynb | Symmetry-preserving circuits, structured feature maps |
| SO(2) Equivariant | so2_equivariant_feature_map.ipynb | Continuous rotational symmetry, equivariance verification |
| Cyclic Equivariant | cyclic_equivariant_feature_map.ipynb | Discrete cyclic symmetry, group actions, statistical verification |
| Swap Equivariant | swap_equivariant_encoding.ipynb | Permutation symmetry, particle-exchange invariance |
What each notebook covers¶
Every notebook follows a consistent structure:
- Setup & instantiation -- creating the encoding with different configurations
- Core properties -- inspecting qubits, depth, gate counts, and lazy-loaded properties
- Circuit generation -- building circuits for PennyLane, Qiskit, and Cirq
- Batch processing -- encoding multiple data samples efficiently
- Analysis tools -- simulability, expressibility, entanglement, and trainability
- Advanced features -- registry, serialization, thread safety, protocols
- Complete workflow -- end-to-end quantum kernel or variational classifier example