Decision Guide¶

Choosing the right quantum encoding for your problem is one of the most consequential decisions in a quantum machine learning pipeline. This guide helps you navigate that choice systematically.

Three Ways to Choose¶

Which Encoding?

Answer a few questions about your data and priorities, and get a recommendation with reasoning.
Decision Flowchart

A visual flowchart that walks you from problem characteristics to encoding choice in under a minute.
Recommendation Architecture

Deep dive into how the scoring engine works — hard filters, soft scoring weights, confidence mapping, and worked examples.
FAQ

Quick answers to the most common questions about encoding selection.

Programmatic Recommendations¶

The atlas includes a recommendation engine that you can call directly from Python:

from encoding_atlas.guide import recommend_encoding

rec = recommend_encoding(
    n_features=4,
    n_samples=500,
    task='classification',
    hardware='simulator',
    priority='accuracy'
)

print(rec.encoding_name)   # e.g., 'IQPEncoding'
print(rec.explanation)     # Human-readable reasoning

You can also query the rule engine directly:

from encoding_atlas.guide import get_matching_encodings

matches = get_matching_encodings(
    n_features=4,
    is_entangling=True,
    max_depth=50,
)

See the API Reference for the full guide interface.

Quick Rules of Thumb¶

Your Situation	Start With
First time, just exploring	Angle Encoding
Need provable quantum advantage	IQP Encoding
Many features, few qubits	Amplitude Encoding
Data has known symmetry	Equivariant encodings
Training a variational model	Data Re-uploading or Trainable
Deploying on real hardware	Hardware Efficient or Angle
Comparing quantum vs classical	IQP + classical baselines