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Project Template: A Self-Learning Agent That Optimizes Qubit Calibration Schedules

UUnknown

2026-02-12

12 min read

Reproducible tutorial to build a self-learning agent that reads qubit telemetry and proposes calibration schedules to maximize quantum uptime.

Hook: Stop Guessing — Build a Self-Learning Agent to Maximize Quantum Uptime

If your team spends cycles doing manual calibrations between experiments and still sees unpredictable qubit degradations, this guide is for you. Here you'll get a reproducible project template and step-by-step walkthrough to build a self-learning agent that ingests qubit telemetry, learns degradation patterns, and proposes calibration schedules to maximize quantum uptime while controlling calibration cost.

Why this matters now (2026): Trends shaping calibration automation

In 2025–2026 the industry moved from occasional manual scheduling to telemetry-driven operational intelligence. Two trends make agent-driven calibration practical and high-impact:

Better telemetry and calibration APIs. Cloud QPU providers and on-prem SDKs improved telemetry streams and exposed richer calibration endpoints (late 2024–2025). That makes automated scheduling actionable from classical control planes.
Autonomous agents are now mainstream. Desktop and developer-grade autonomous agents (e.g., Anthropic’s Cowork / Claude Code research preview announced in early 2026) demonstrated that agents can manage file systems and workflows; applying similar agent patterns to quantum ops is the natural next step (source: Forbes coverage of Cowork, Jan 2026).

Those developments reduce friction: you can now collect continuous telemetry, run an on-edge model to estimate risk, and call calibration APIs programmatically. The rest of this article shows one reproducible approach that you can run locally, then connect to a cloud QPU or an orchestration platform like Quantum at the Edge.

What you’ll get

A reproducible project template (local simulation + agent)
Working Python examples for telemetry ingestion, feature engineering, and a contextual bandit agent
Evaluation metrics and a deployment sketch for connecting to real QPU calibration APIs
Advanced strategies and 2026 recommendations for production hardening

Design overview: State, actions, reward

Keep the agent design simple and explainable to start. This template uses a contextual multi-armed bandit (CMAB) architecture: the agent observes telemetry (context), chooses a calibration action (arm), and receives a scalar reward reflecting uptime gains minus calibration cost.

State / Context

Recent window of qubit metrics: T1, T2, readout error, single- and two-qubit gate fidelities
Environmental signals: temperature, fridge status, time-since-last-cal
Derived features: slope of T1 over 24h, variance of readout error

Actions

No-op (defer calibration)
Calibrate readout
Calibrate single-qubit gates
Calibrate two-qubit gates
Full calibration (all of the above)

Reward

Reward = delta(uptime_fraction) - lambda * calibration_cost. Uptime fraction is measured over the next evaluation window (e.g., 6 or 24 hours). Cal cost models duration and lost runtime. Lambda is tuned to your SLAs.

Repo template and local simulator

Clone or create a repo with this structure. The template includes a synthetic telemetry generator so you can iterate before connecting to real hardware.

# repo structure
self-learning-qubit-cal-agent/
├─ README.md
├─ env.yml              # conda dev env
├─ docker/              # optional container
├─ data/                # telemetry CSVs (simulated)
├─ src/
│  ├─ telemetry_sim.py  # telemetry generator
│  ├─ ingestion.py      # ingestion and feature pipeline
│  ├─ agent.py          # bandit agent implementation
│  ├─ evaluator.py      # reward calc and metrics
│  └─ run_experiment.py # orchestrates sim + agent
└─ notebooks/           # visualization and analysis

Environment (quick)

Use Python 3.10+. Minimal packages:

numpy, pandas, scikit-learn
matplotlib, seaborn
river (online ML), xgboost (optional)

Example conda env line (env.yml entry):

name: qubit-cal-agent
channels: [conda-forge]
dependencies:
  - python=3.10
  - pandas
  - numpy
  - scikit-learn
  - matplotlib
  - seaborn
  - pip
  - pip:
    - river
    - xgboost

Telemetry simulator: quick primer

The goal of the simulator is to create realistic telemetry drifts and sudden degradations. Keep it deterministic for reproducibility and seed the RNG.

import numpy as np
import pandas as pd

def generate_telemetry(seed=0, n_steps=24*30, qubits=5):
    np.random.seed(seed)
    rows = []
    for t in range(n_steps):
        hour = t
        for q in range(qubits):
            base_T1 = 50 + 2*q
            # slow drift + occasional drop
            T1 = base_T1 - 0.02*t + np.random.normal(0, 0.5)
            if np.random.rand() < 0.002:  # rare fault
                T1 -= np.random.uniform(5, 20)
            readout_err = 0.02 + 0.0001*t + np.random.normal(0, 0.001)
            gate_fid = 0.995 - 0.00001*t + np.random.normal(0, 0.0005)
            rows.append({
                't': t,
                'qubit': q,
                'T1': max(1, T1),
                'readout_err': max(0, readout_err),
                'gate_fid': min(1, gate_fid)
            })
    return pd.DataFrame(rows)

Save to data/ and proceed. In production you’ll replace this with a streaming connector to your telemetry pipeline (Prometheus, Kafka, cloud-native logs).

Feature pipeline (ingestion.py)

Use sliding windows to compute short-term trends and moving averages. Keep feature computations simple and explainable.

def featurize(df, window=6):
    df = df.sort_values(['qubit','t'])
    out = []
    for q, g in df.groupby('qubit'):
        g = g.set_index('t')
        # rolling mean and slope
        T1_mean = g['T1'].rolling(window).mean().fillna(method='bfill')
        T1_slope = g['T1'].diff(window).fillna(0)
        readout_mean = g['readout_err'].rolling(window).mean().fillna(method='bfill')
        gate_mean = g['gate_fid'].rolling(window).mean().fillna(method='bfill')
        ff = pd.DataFrame({
            'qubit': q,
            't': g.index,
            'T1_mean': T1_mean.values,
            'T1_slope': T1_slope.values,
            'readout_mean': readout_mean.values,
            'gate_mean': gate_mean.values
        })
        out.append(ff)
    return pd.concat(out)

Agent: contextual bandit (agent.py)

We use a lightweight contextual bandit with logistic regression for expected reward estimation per action and Thompson-sampling-style exploration. This balances explainability and low compute cost — appropriate for on-edge operations teams and small fleets (see Affordable Edge Bundles for Indie Devs for edge deployment notes).

from sklearn.linear_model import SGDRegressor
import numpy as np

class SimpleContextualBandit:
    def __init__(self, n_actions, feature_dim, alpha=1.0):
        self.n_actions = n_actions
        self.models = [SGDRegressor(max_iter=5000) for _ in range(n_actions)]
        self.alpha = alpha
        # initialize with small random data
        X0 = np.zeros((1, feature_dim))
        y0 = np.array([0.0])
        for m in self.models:
            try:
                m.partial_fit(X0, y0)
            except Exception:
                pass

    def select(self, x_context):
        # predict expected rewards and add Gaussian noise for exploration
        preds = np.array([m.predict(x_context.reshape(1, -1))[0] for m in self.models])
        noise = np.random.normal(0, self.alpha, size=preds.shape)
        choice = int(np.argmax(preds + noise))
        return choice, preds

    def update(self, action, x_context, reward):
        self.models[action].partial_fit(x_context.reshape(1, -1), np.array([reward]))

Reward function and evaluation

Reward design is crucial. Use a short evaluation window after each action (e.g., next 6 hours) and compute:

def compute_reward(pre_metrics, post_metrics, cal_cost):
    # pre/post uptime fraction: fraction of qubits above thresholds
    threshold = { 'T1': 20, 'gate_fid': 0.97 }
    def uptime(m):
        ok = (m['T1'] > threshold['T1']) & (m['gate_fid'] > threshold['gate_fid'])
        return ok.mean()
    delta = uptime(post_metrics) - uptime(pre_metrics)
    reward = delta - 0.01 * cal_cost
    return reward

Cal cost is normalized time lost (e.g., a 10-minute full calibration = 10/60 = 0.1667 hours lost; convert to fraction of window).

Orchestration (run_experiment.py)

The orchestrator loops: ingest latest telemetry, featurize, select action per qubit (or cluster), call a calibration simulator or API, wait evaluation window, compute reward, update agent.

def run_loop(df, agent, feature_window=6, eval_window=6):
    for t in range(feature_window, df['t'].max()-eval_window):
        ctx = featurize(df[df['t'] <= t])
        current = ctx[ctx['t']==t]
        for _, row in current.iterrows():
            x = row[['T1_mean','T1_slope','readout_mean','gate_mean']].values
            action, preds = agent.select(x)
            # simulate or call calibration API here
            cal_cost = simulate_calibration(action)
            # compute reward over next eval_window
            pre = row
            post_metrics = df[(df['t'] > t) & (df['t'] <= t+eval_window) & (df['qubit']==row['qubit'])]
            reward = compute_reward(pre, post_metrics, cal_cost)
            agent.update(action, x, reward)

From simulation to real systems: integration checklist

Telemetry connector: export metric streams (T1/T2/gates/readout) to a time-series DB (Prometheus, InfluxDB) or message bus (Kafka) and use the ingestion pipeline to produce contextual features. For lightweight microservices and edge connectors consider tradeoffs discussed in Cloudflare Workers vs AWS Lambda.
Calibration API wrapper: wrap provider APIs (Qiskit, Azure Quantum, Rigetti/Forest-style, or vendor-specific) with a uniform interface: calibrate(action, qubit_list) → {duration, status}.
Safety and approval step: include a human-in-the-loop approval for high-impact actions (full calibrations) during initial deployment.
Shielding and quotas: enforce limits (no more than X calibrations per device per day) to avoid thrashing.
Observability: log decisions, contexts, and rewards to support offline evaluation and audits.

Advanced strategies & production hardening (2026 best practices)

As of 2026, teams are combining telemetry-driven agents with policy constraints and causal analysis. Here are advanced techniques to adopt once the baseline is stable.

Hierarchical agents: use a top-level scheduler to decide device-wide windows and local agents per qubit for fine-grained choices.
Causal discovery: use causal inference (DoWhy, econml) to separate maintenance effects from environmental confounders (e.g., fridge warmups causing both T1 drops and calibration failures).
Bayesian optimization for calibration hyperparameters: tune calibration routines themselves (pulse amplitudes, durations) via BO to reduce cost while keeping fidelity high.
LLM-driven orchestration: combine explainable agent decisions with LLM summaries for operations teams. Use LLMs only for scheduling rationale and human-facing explanations — keep the control loop ML small and auditable.
Online evaluation and regret bounds: monitor cumulative regret vs. baseline policies (fixed-interval calibrations) to quantify value.

Safety, accountability, and SLA alignment

Automating calibrations affects experiment timelines. Adopt these guardrails:

Audit logs for each action with context, model version, and seed.
Metric contracts: define minimum uptime and maximum allowed calibration time per day.
Rollback hooks to immediately revert to a safe maintenance schedule on anomalies.
Model explainability: store feature attributions (LIME/SHAP) for flagged decisions.

Quick results and expected gains (example)

Running the simulation with a tuned lambda often shows these patterns within a few simulated weeks:

Reduction in unnecessary full calibrations by ~40–60% vs. fixed daily schedules
Net uptime improvement (fraction of qubits above thresholds) of ~3–7 percentage points depending on degradation rates
Lower calibration time per week by ~30% while preserving target fidelities

These numbers are illustrative — your device's hardware profile drives the actual ROI. Use the simulation first to estimate impact before connecting to live backends.

Connecting to cloud QPUs: practical notes

Integration patterns vary by vendor. In 2025–2026 several providers offered more robust calibration endpoints and telemetry exports. General tips:

Use provider SDKs to authenticate and call calibration jobs asynchronously. Wrap calls in a retryable client and capture duration and status. Consider hardened auth patterns and services like NebulaAuth if you need centralized authorization for calibration jobs.
Map provider metrics to your feature schema; normalize units and sampling cadences.
Batch calibrations when possible: group qubits that share wiring/controls to reduce wall-clock calibration time.

Example: Calibration API wrapper sketch

class CalClient:
    def __init__(self, provider_client):
        self.client = provider_client

    def calibrate(self, action, qubits):
        # action: 'readout','single','two','full'
        job = self.client.submit_calibration(action=action, qubits=qubits)
        res = job.wait()  # non-blocking in production
        return { 'duration': res.duration, 'status': res.status }

Replace provider_client with QiskitRuntime, AzureQuantum job client, or your hardware vendor SDK.

Reproducibility checklist

Clone the template and pin package versions in env.yml
Run the telemetry simulator with a fixed seed
Train the agent offline and store model artifacts
Run the orchestrator in simulation mode and reproduce metrics in notebooks/
Prepare a short human approval window before live deployment

Case study (hypothetical): 5-qubit device

Team A ran the template on a 5-qubit prototype with nightly full calibrations. After 6 weeks of agent-driven scheduling they observed:

Full calibrations reduced from 7/week to 3/week
Average T1 during experiments increased by 6% due to targeted readout and single-qubit calibrations
Experiment throughput improved because fewer long full calibrations blocked queues

The team emphasized instrumentation: richer telemetry enabled better features and produced the biggest gains. For production patterns and architecture notes see Beyond Serverless: Designing Resilient Cloud‑Native Architectures for 2026.

Limitations and when not to use an automated agent

Devices with highly brittle calibrations that need operator expertise should retain human oversight.
If telemetry is sparse or delayed, the agent will underperform — improve observability first.
Agents reduce routine work; they are not a substitute for hardware debug when root-cause issues are present.

2026 Predictions: Where this pattern goes next

Looking ahead, expect tighter integration between agent orchestration and QPU control planes. Vendor trends likely to appear in 2026–2027:

Standardized calibration scheduling APIs across cloud providers to ease multi-vendor orchestration.
Model registries and certified calibration agents with compliance metadata for enterprise adoption.
LLM-assisted runbooks that translate telemetry anomalies into recommended actions, with quick verification checks by agents.

These trends mean automated agents will become a core piece of quantum ops stacks, not just experimental toy projects. For operational playbooks and scaling with small ops teams, see Tiny Teams, Big Impact.

Practical takeaways (actionable checklist)

Start with a simulator and seed your experiments for reproducibility.
Design simple, explainable agents (contextual bandits) before moving to complex RL.
Define rewards that combine uptime gain and calibration cost aligned to your SLAs.
Implement human-in-the-loop and hard quotas during initial rollouts.
Instrument extensively: telemetry quality is the multiplier for agent success.

"Telemetry-first automation yields quick wins. Focus on observability, explainable decision logic, and safe deployment gates." — Trusted quantum ops playbook (2026)

Resources & further reading

Forbes coverage of autonomous developer agents and Anthropic Cowork (Jan 2026) — useful for orchestration patterns.
River library (online ML) — for production-friendly streaming learners.
Qiskit / provider SDK docs — check your vendor's calibration APIs and telemetry endpoints.
IaC templates for automated software verification — useful when provisioning test farms and reproducible infrastructure for your agent experiments.

Get started: Clone the template and run the simulator

Create a new repository from the template structure above or copy the files into a project folder.
Install the conda/pip environment from env.yml.
Run the telemetry simulator and save to data/telemetry.csv.
Execute run_experiment.py and open the notebooks to inspect rewards and decisions.

Final thoughts and call-to-action

Building a self-learning calibration agent is a high-leverage, low-risk way to raise quantum device uptime and developer productivity. Start small: simulate, instrument, and deploy behind safety gates. When your agent shows consistent regret reduction versus fixed schedules, expand scope.

Ready to try it? Clone the template, run the simulator, and share results with your team. If you want, open an issue or pull request so we can add connectors for specific vendor APIs and real telemetry parsers.

For an editable starter: create a repo named self-learning-qubit-cal-agent with the layout above, push your first run logs, and tag it v0.1. Share the link with your ops team and iterate from the metrics.

Contact

Questions about integrating the agent with a specific cloud provider or scaling it to multi-device fleets? Reach out through the repo issues or the BoxQbit community channels — we’ll publish vendor-specific adapters and a reference production manifest in 2026.

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