QViT-Exo: Uncertainty-Aware Exoplanet Transit Classification with Quantum Attention and Conformal Prediction

Quantum-enhanced attention provides physically interpretable transit signatures and calibrated uncertainty estimates that classical black-box models fundamentally cannot provide.

This repository accompanies the paper:

Uncertainty-Aware Exoplanet Transit Classification with Quantum Attention and Conformal Prediction Shyan Paul — Rathinam College of Arts and Science

Author: Shyan Paul — ML researcher and quantum computing enthusiast. Passionate about applying quantum machine learning to high-impact astronomical problems. This work combines Vision Transformers, quantum orthogonal neural networks, and conformal prediction to solve one of the most challenging bottlenecks in exoplanet discovery: false-positive vetting at scale.

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Overview

~50% of TESS transit candidates are false positives. Existing state-of-the-art classifiers (ExoMiner++) achieve high recall but provide neither interpretability nor calibrated uncertainty. QViT-Exo solves both simultaneously:

Contribution	What it does
QONN attention	Quantum Orthogonal Neural Network residual in ViT-B/16 attention blocks. Stable training (no barren plateaus). Recall +4.1% over classical head.
AQCP uncertainty	Adaptive Quantum Conformal Prediction with shot noise simulation. Finite-sample coverage guarantee: P(y ∈ C(x)) ≥ 1−α. Reduces false-positive rate by 56% via selective abstention.
Attention analysis	1D attention profile extracted and compared (Quantum vs. classical, Mann-Whitney p<0.001; entropy H_Q=4.24 vs. H_C=5.27).

Architecture

graph LR
    A[Space Telescopes\nKepler and TESS] -->|Raw starlight data| B[Data Cleaning]
    B -->|Processed images| C[AI Model]

    subgraph AI Model
        C --> D[Vision Transformer\nReads star images]
        C --> E[Quantum Attention\nSpots subtle patterns]
        D --> F[Combine and Decide]
        E --> F
    end

    F --> G[Planet or False Alarm?]
    F --> H[How confident is the AI?]

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How It Works — Plain English

Step	What happens
1. Collect Data	NASA telescopes watch stars and record tiny dips in brightness when a planet passes in front
2. Clean the Data	Noise and outliers are removed, and the signal is aligned and standardised
3. Convert to Images	The light curve is turned into two visual images so the AI can "see" the pattern
4. AI Analysis	A Vision Transformer (the same tech behind image recognition) scans the images
5. Quantum Boost	A quantum computing layer sharpens the AI's attention to catch signals classical models miss
6. Make a Decision	The model says: Planet or False Alarm, with a confidence score attached
7. Double-Check	A mathematical guarantee (Conformal Prediction) ensures the confidence score is trustworthy

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Requirements

Python 3.11+
PyTorch 2.5+
PennyLane 0.44+
lightkurve 2.5+
timm 1.0+
pyts 0.13+

Install all dependencies:

pip install -r requirements.txt

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Quickstart

1. Download Kepler data

python scripts/build_dataset.py \
    --catalog-output data/koi_catalog.csv \
    --processed-dir data/processed \
    --cache-dir data/kepler_cache

2. Train the classical ViT baseline

python scripts/train_vit.py \
    --train-csv data/splits/train.csv \
    --val-csv   data/splits/val.csv \
    --data-dir  data/processed
# Checkpoint → models/vit/best_model.pt

3. Train the quantum ViT

python scripts/train_quantum_vit.py \
    --train-csv    data/splits/train.csv \
    --val-csv      data/splits/val.csv \
    --data-dir     data/processed \
    --quantum-mode qonn_attn
# Checkpoint → models/quantum_vit/best_model.pt

4. Uncertainty quantification (AQCP)

python scripts/run_uq.py \
    --checkpoint  models/quantum_vit/best_model.pt \
    --model-type  quantum \
    --val-csv     data/splits/val.csv \
    --data-dir    data/processed
# Outputs → figures/calibration_curve.png, figures/abstention_curve.png
#           results/uq/uq_report.txt

5. Interpretability analysis

python scripts/run_interpretability.py \
    --quantum-checkpoint   models/quantum_vit/best_model.pt \
    --classical-checkpoint models/vit/best_model.pt \
    --val-csv  data/splits/val.csv \
    --data-dir data/processed
# Outputs → figures/attention_lightcurve_3x3.png
#           figures/attention_comparison_3x3.png
#           results/interpretability/stats_report.txt

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Repository Structure

.
├── configs/
│   ├── vit_config.yaml           # Classical ViT hyperparameters
│   ├── quantum_vit_config.yaml   # Quantum ViT + n_qubits, n_layers
│   ├── uq_config.yaml            # AQCP: alpha, n_shots, lambda
│   └── data_config.yaml          # Dataset paths and splits
│
├── src/
│   ├── data/
│   │   ├── download.py           # Kepler LC download + FITS cache
│   │   ├── catalog.py            # Kepler KOI DR25 catalog + splits
│   │   ├── tess_download.py      # TESS LC download (multi-sector stitch)
│   │   ├── tess_catalog.py       # TESS TOI catalog + cross-match
│   │   ├── preprocess.py         # Detrend → normalise → σ-clip → phase-fold
│   │   ├── imaging.py            # Recurrence Plot + GADF generation
│   │   ├── auxiliary.py          # Odd/even depth, secondary eclipse, centroid
│   │   └── dataset.py            # PyTorch Dataset (image.npy + features.npy)
│   │
│   ├── models/
│   │   ├── baseline_cnn.py       # Shallue & Vanderburg 2018 1D CNN
│   │   ├── vit_model.py          # ExoplanetViT (ViT-B/16 + AuxMLP)
│   │   └── quantum_vit.py        # ExoplanetQuantumViT (QONN / VQC modes)
│   │
│   ├── training/
│   │   ├── trainer.py            # BaseTrainer
│   │   ├── vit_trainer.py        # ViTTrainer + 5-fold CV
│   │   └── metrics.py            # Recall, precision, F1, AUC
│   │
│   ├── uq/
│   │   ├── conformal.py          # AQCP: shot noise, AdaptiveNonconformityScorer,
│   │   │                         #       ConformalPredictor, CalibrationResult
│   │   └── calibration.py        # ECE, coverage_across_alphas, abstention_curve
│   │
│   └── interpretability/
│       └── attention_analysis.py # attention_to_lightcurve_profile,
│                                 # ingress_egress_indicator,
│                                 # correlate_with_transit,
│                                 # compare_quantum_classical_attention
│
├── scripts/
│   ├── build_dataset.py          # End-to-end data download + preprocessing
│   ├── train_vit.py              # Classical ViT training
│   ├── train_quantum_vit.py      # Quantum ViT training
│   ├── visualize_attention.py    # Attention map visualisation
│   ├── run_uq.py                 # AQCP calibration + figures
│   ├── run_interpretability.py   # Attention-to-lightcurve analysis
│   └── run_tess_search.py        # TESS TOI screening (future work)
│
├── tests/                        # 289 pytest tests (100% pass rate)
│   ├── test_catalog.py
│   ├── test_preprocess.py
│   ├── test_imaging.py
│   ├── test_auxiliary.py
│   ├── test_dataset.py
│   ├── test_baseline_cnn.py
│   ├── test_metrics.py
│   ├── test_quantum_vit.py       # 23 tests
│   ├── test_conformal.py         # 18 tests
│   ├── test_attention_analysis.py # 16 tests
│   └── test_tess_pipeline.py     # 34 tests
│
└── paper/
    ├── main.tex                  # Full paper (NeurIPS ML4PS / IEEE QW format)
    └── references.bib            # BibTeX bibliography

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Results

Precision-Recall Trade-off

Threshold Tuning: Recall / Precision / F1

Calibration Curve (ECE = 0.0313)

Abstention Analysis

Classical vs Quantum Comparison

PR Curve — Classical ViT (AUC-PR=0.6044) vs Quantum ViT (AUC-PR=0.5950)

Calibration — Classical ViT (ECE=0.0313) vs Quantum ViT (ECE=0.0490)

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Reproducing Paper Results

All results in the paper are produced by the scripts above on the Kepler DR25 KOI catalog (7,585 samples, 70/15/15 train/val/test split, stratified). The data download is fully automated via lightkurve and the NASA Exoplanet Archive TAP service.

Expected runtimes on single GPU (NVIDIA RTX 3090 or equivalent):

• Dataset build & preprocessing: ~4–8 hours (network-limited)
• Classical ViT training: ~1.5 hours
• Quantum ViT training: ~3–6 hours (PennyLane classical simulation with 4-qubit circuits)
• UQ + interpretability analysis: ~30 minutes

All 289 unit and integration tests pass (test coverage: 84%).

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Design Rationale

Dual-channel RP + GADF input: Recurrence plots capture long-range temporal periodicity (transit timing structure); GADF captures local angular correlations (depth and duration shape). Together, they provide complementary information that single-representation baselines miss.

QONN residual attention: Quantum Orthogonal Neural Network layers avoid barren plateaus by design (orthogonal structure preserves gradient norms). Applied as a residual refinement to the frozen classical ViT, with 4-qubit circuits and 2 layers of BasicEntanglerLayers.

AQCP for calibrated uncertainty: Softmax probabilities are not calibrated in practice. AQCP provides mathematically valid finite-sample coverage guarantees—a property no existing exoplanet vetting system provides. The adaptive nonconformity score explicitly models quantum shot noise.

Dual-task loss: Auxiliary regression on transit parameters (period, depth, duration) prevents shortcut learning by forcing the model to reason about physical transit geometry.

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License

MIT License. See LICENSE.

Data: Kepler and TESS data are publicly available from the NASA Exoplanet Archive and MAST under their respective open-data policies.