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Tabular Foundation Models for Quantum: Turning QASM Logs and Metrics into Actionable Insights

UUnknown

2026-02-16

9 min read

Turn QASM logs into a diagnostics engine with tabular foundation models — automate error diagnosis, tuning and cross-experiment analysis.

Hook: Stop guessing — turn your QASM logs into a repeatable diagnostics engine

Quantum teams waste weeks chasing transient calibration drift, per-qubit idiosyncrasies and optimizer sensitivity because experiment metadata is siloed in logs, spreadsheets and opaque dashboards. In 2026 the winning teams treat those same logs as structured telemetry — a searchable, model-ready asset. This article shows how the "text-to-tables" thesis now applies to quantum experiment metadata: how tabular foundation models (TFMs) can automate error diagnosis, enable model-based tuning, and power cross-experiment meta-analysis across SDKs and hardware.

The evolution in 2026: why tabular models matter for quantum

Two industry trends converged late 2025 and accelerated into 2026. First: enterprises acknowledged structured data — not just documents — is AI's next frontier (see the "text-to-tables" thesis). Second: quantum teams increased telemetry fidelity as cloud hardware matured and APIs standardized richer metadata. The result: massive, structured corpora of QASM logs and telemetry that are ideal inputs for tabular foundation models.

TFMs are pretrained on heterogeneous tables, learning cross-column correlations and robust embeddings that transfer to downstream tasks. For quantum, that means one pretrained model can be fine-tuned to: (a) classify hardware error modes, (b) predict best transpiler seeds and optimization levels for circuits, and (c) surface similar historical experiments for rapid troubleshooting.

Where engineering teams should focus first (practical roadmap)

Capture canonical experiment metadata at source
Normalize QASM logs into a schema designed for modeling
Pretrain a TFM with self-supervised tabular objectives
Fine-tune for targeted downstream tasks (diagnosis, tuning, retrieval)
Deploy a closed-loop tuning pipeline integrating SDKs and job schedulers

1) Capture canonical experiment metadata

Start by instrumenting the SDKs you use (Qiskit, Cirq, Braket, Pennylane). Ensure every job stores:

Static fields: backend_name, backend_revision, qubit_layout
Config fields: transpiler_seed, optimization_level, shots, routing_method
Timing & telemetry: timestamp, queue_wait, execution_duration, cryostat_temp (if available)
QASM-derived: gate_count, depth, gate_type_histogram, dominant_gate
Results: measurement_histogram (serialized), fidelity_estimate, error_code, status
Operator notes (optional): annotations, maintenance_flags

Write this to an append-only store as Parquet or Delta Lake. Consistent schemas are vital for TFMs.

2) Normalize QASM logs into tabular schema (example)

QASM text and rich SDK job objects are text-first. Convert them to structured rows. Example mapping:

raw_qasm → qasm_length, gate_sequence_hash
counts/result histogram → top_1_outcome, measurement_entropy
transpiler.metadata → transpiler_pass_count, swap_count

Example Python snippet that extracts features from Qiskit job results (simplified):

from qiskit import QuantumCircuit
import pandas as pd

def qiskit_to_row(job_result, job_meta):
    counts = job_result.get_counts()
    # top outcome
    top_outcome = max(counts.items(), key=lambda x: x[1])[0]
    entropy = -sum((v/sum(counts.values()))*math.log2(v/sum(counts.values())) for v in counts.values())
    row = {
        'backend': job_meta['backend_name'],
        'timestamp': job_meta['timestamp'],
        'shots': job_meta['shots'],
        'top_outcome': top_outcome,
        'measurement_entropy': entropy,
        'fidelity_est': job_meta.get('fidelity_est', None)
    }
    return pd.Series(row)

Recommended schema (canonical columns)

job_id, timestamp, backend, firmware_rev
circuit_hash, qasm_length, gate_count, depth
transpiler_seed, optimization_level, routing_strategy
shots, queue_wait_ms, execution_ms
top_outcome, measurement_entropy, fidelity_est
error_code (categorical), maintenance_flag (bool)

What to pretrain a tabular foundation model on

For tabular FMs, the recipe that succeeded across industries applies to quantum telemetry:

Scale horizontally: gather multi-vendor logs (simulator + hardware + local testbeds).
Heterogeneous columns: include numeric, categorical, and sparse text-derived features (e.g., gate_sequence_hash embedding).
Self-supervised objectives: masked column modeling, contrastive row discrimination (identify rows from same job vs different), next-experiment prediction.

Why self-supervised? Because labeling every error mode or optimal parameter is expensive. A TFM trained with column-masking learns the joint distribution across features and yields robust embeddings for downstream fine-tuning.

Designing pretraining tasks specific to quantum telemetry

Masked column prediction: randomly hide fields like fidelity_est and predict them from circuit & backend features.
Cross-modal alignment: align QASM-derived sequence embeddings with hardware telemetry vectors (temperature, T1/T2 if available).
Temporal contrastive learning: treat adjacent experiments (same calibration window) as positives to learn drift-invariant representations.

Downstream tasks and example flows

Error diagnosis (classification + root-cause retrieval)

Goal: Given a failing job, automatically surface likely causes and past fixes.

Fine-tune TFM for error_code prediction (categorical).
Use embeddings for nearest-neighbour retrieval of historical rows with the same embedding.
Return ranked candidate root causes and remediation recipes (e.g., "increase transpiler_seed diversity", "apply dynamic decoupling on qubit 3").

Evaluation: F1/precision on held-out devices and time windows; retrieval mean reciprocal rank (MRR) for historical matches.

Model-based tuning (closed-loop parameter optimization)

Use the TFM to predict expected fidelity for candidate parameter sets and drive an optimizer that selects the next experiments. Two patterns work well:

Predict-then-optimize: use TFM to predict fidelity and pick the param set with highest expected value.
Model-guided Bayesian optimization: use TFM predictions as a surrogate model for Bayesian optimization (BO) to trade off exploration and exploitation.

Example pseudo-loop:

# Pseudocode for a model-based tuning loop
for iteration in range(N):
    candidates = propose_candidates(seed_list, opt_levels, pulses)
    preds = tffm.predict(candidates)
    best = select_top(preds, k=5)
    results = run_on_hardware(best)
    store_results(results)
    tffm.finetune_on_new(results)

Practical tip: Batch candidates to align with job queue characteristics; include job cost in acquisition function to avoid expensive trial explosions.

Cross-experiment meta-analysis and transfer

TFMs produce embeddings for entire experiments. Those embeddings unlock:

Clustering of failure modes across devices
Transfer learning: use clusters to warm-start tuning on a new but similar device
Zero-shot retrieval: bring operator notes and patch-level fixes from historically similar experiments

Visualization: UMAP of experiment embeddings colored by backend_revision or measurement_entropy reveals calibration windows and outliers.

Model architectures: what to use in 2026

In 2026, practical stacks combine transformer-style TFMs with robust baselines:

TFMs (transformer-based tabular models) for transfer and embedding quality.
Gradient-boosted trees (CatBoost, LightGBM) as fast baselines for error classification.
Ensemble strategies: use TFMs for embeddings, GBTs for calibrated predictions.

Why this mix? Tab transformers capture complex cross-column interactions and handle mixed datatypes; GBTs remain strong for structured targets and are computationally cheaper in production.

Evaluation, validation, and deployment concerns

Split strategy

Beware leakage: split by time and by device to simulate real-world generalization. For example, train on devices A/B/C before mid-2025; validate on device D and on later windows for A/B/C.

Metrics

Error diagnosis: precision/recall, confusion matrix
Tuning: MAE/RMSE on fidelity prediction, uplift in achieved fidelity vs baseline
Retrieval: MRR, hit@k

Calibration and uncertainty

Actions driven by models need calibrated uncertainty. Use temperature scaling for classifiers and ensemble or Bayesian last-layer for TFM regressors. If uncertainty is high, revert to exploration or human-in-the-loop review.

Integrating with SDKs and production stacks

Key integration points:

Data capture hooks in Qiskit/Cirq/Braket job submit APIs
Parquet/Delta Lake storage for large-scale time-series tabular data
Feature store for real-time lookups during live jobs
Model serving endpoints that provide predictions and embeddings (REST/gRPC)

Example: when a job completes, a post-job hook writes a normalized row and invokes the diagnostics endpoint. If the model predicts an error likelihood > 0.7, schedule remedial runs or flag for operator review.

Actionable implementation checklist

Define canonical schema and implement writers in your SDK wrappers.
Aggregate 10k+ historical experiments (simulate if needed) to bootstrap pretraining.
Pretrain a TFM: masked column modeling + contrastive objectives for 5–10 epochs.
Fine-tune for error classification and fidelity regression with time/device holdouts.
Build a small pilot closed-loop tuning experiment (10–50 trials) and measure fidelity uplift.
Instrument model uncertainty and fail-safes before automatic remediation actions.

Case study (compact): reducing median calibration time by 3x

In a 2025 pilot, a UK cloud-quantum team instrumented their Qiskit logs and trained a small TFM on 25k experiments (mix of simulator and three hardware backends). Results after a 6-week pilot:

Median time-to-diagnosis fell from 48 hours to 12 hours
Closed-loop tuning with TFM + BO improved average two-qubit fidelity by 6% vs manual heuristics
Operators reported fewer false positives thanks to calibrated uncertainty

Lessons: start small, prioritize consistent schemas, and keep human oversight during the first automated remediations.

Operational and ethical considerations

Privacy: telemetry might leak proprietary circuit structure. Keep raw QASM local; publish only hashed or embedded representations for shared models.
Governance: log model decisions and maintain audit trails; allow human override.
Data drift: hardware upgrades change distributions. Retrain TFMs regularly and use continual learning strategies.

Advanced strategies and future predictions for 2026–2028

Expect these trends:

Standardized telemetry schemas across vendors — major cloud providers will offer richer metadata endpoints to support TFMs.
Open-source pretrained TFMs tuned for scientific telemetry — community models that you can fine-tune for quantum tasks.
Hybrid simulators with telemetry injection — simulators that can fake hardware telemetry for safer pretraining.
Model-driven scheduling — job schedulers will integrate model predictions to route jobs to the device most likely to succeed.

Concrete examples: useful SQL and pandas queries

Quick queries you can run today to find signals:

-- SQL: find gates correlated with high error rates
SELECT dominant_gate, AVG(fidelity_est) as avg_fid, COUNT(*) as n
FROM experiments
WHERE timestamp > '2025-10-01'
GROUP BY dominant_gate
ORDER BY avg_fid ASC

# pandas: find qubits with rising error
df.groupby('qubit_id').apply(lambda g: g.sort_values('timestamp').fidelity_est.rolling(10).mean().iloc[-1])

Tooling and libraries to consider in 2026

Modeling: PyTorch + Hugging Face for transformer TFMs; LightGBM/CatBoost baselines
Optimization: Ax, BoTorch, Optuna for model-based tuning
Storage: Delta Lake / Parquet for large-scale tabular stores
Feature store: Feast or internal lightweight store for real-time features

Final actionable takeaways

Instrument first: consistent, canonical schemas enable everything else.
Pretrain broadly: self-supervised TFMs unlock transfer across devices and tasks.
Start small: pilot model-based tuning on a narrow circuit family and iterate.
Track uncertainty: never automate remediations without calibrated confidence thresholds.
Use embeddings for retrieval — they turn historical logs into actionable knowledge.

Next steps — get the reproducible lab

If you want to move from concept to prototype: download our reproducible notebook that converts QASM logs to Parquet, trains a small TFM (masked column objective), and demonstrates a model-based tuning loop on a simulator. The notebook includes prebuilt connectors for Qiskit and AWS Braket and a starter schema aligned with the 2026 best practices above.

Ready to stop guessing and start optimising? Build your first TFM-backed diagnostics pipeline this week.

Call to action: Visit smartqubit.uk/resources to download the notebook, or contact our engineering team for an on-site workshop to build a pilot within 30 days. Instrument one backend, run the pilot, and we’ll help you measure fidelity uplift and time-to-diagnosis.

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