Prototype: A Desktop 'Cowork' Agent That Schedules Quantum Experiments

Hook: stop wrestling with calendar conflicts and QPU queues — build a local coworking agent that schedules your quantum experiments safely

Quantum developers and platform teams in 2026 face two stubborn realities: limited, contested access to real QPUs and a fragmented ecosystem of tooling. You want a reproducible way to get experiments running when resources are available and when stakeholders are free — without sending sensitive calendars and code to third-party services. This blueprint shows how to prototype a desktop Cowork agent that reads local calendar constraints, polls QPU availability, and schedules experiments according to user priorities — all while keeping data on your machine and enforcing safety guards.

The opportunity in 2026: local agents meet quantum access

Late 2025 and early 2026 saw a surge in desktop autonomous agents (Anthropic's Cowork and similar micro-app trends) that can orchestrate workflows on a user's machine. For quantum teams, that trend unlocks a new pattern: local automation that integrates classical dev workflows, calendar constraints, and cloud/hybrid QPU resources without handing your experiment metadata to unknown cloud agents.

Why now? Providers have improved job reservation APIs and runtime telemetry, simulators are faster on commodity hardware, and developer demand for reproducible, auditable experiments is high. Combine that with desktop agent UX patterns and you get a practical prototype surface: a local agent that schedules quantum runs based on who’s available, hardware windows, and prioritized experiment queues.

What this article delivers

• A pragmatic architecture for a local Cowork-style agent focused on quantum experiments
• Security and safety-first practices for handling calendars, API keys, and job submission
• Concrete code snippets and algorithms (Python) to prototype the scheduler and executor
• Actionable integration tips for common calendar and QPU providers
• Operational checks: sandboxing, validation, auditing, and human-in-the-loop controls

High-level architecture: components and data flow

Design the agent as a modular desktop service with clear separation of concerns. Keep all sensitive state local by default and make any external calls explicit and auditable.

Core components

• UI/Agent Controller — Electron or Tauri frontend to show status, queues, and require confirmations.
• Calendar Reader — reads local calendars (ICS, CalDAV, or encrypted OAuth tokens) and computes availability windows.
• QPU Inventory & Poller — caches QPU availability, queue lengths, and reservation windows from providers.
• Scheduler Engine — prioritization algorithm that maps experiments into target slots.
• Executor — submits jobs to simulators or real QPUs and monitors job state.
• Safety & Policy Module — enforces sandboxing, cost limits, depth/qubit checks and requires confirmations.
• Persistence — local SQLite or file-based ledger of experiments, audits, and logs.
• Credentials Store — OS keyring or encrypted file for API keys and service tokens.

Data flow (summary)

1. Calendar Reader computes availability windows for the user/team.
2. QPU Poller fetches live availability and reservation windows and stores cached metrics locally.
3. Scheduler Engine combines experiment metadata, user priorities, calendar windows, and QPU state to propose slots.
4. Safety Module runs static checks, simulates on local simulator for validation, and requests explicit approval for risky runs.
5. Executor submits validated jobs and streams telemetry into local logs and the UI.

Practical: pick the right stack for a desktop agent in 2026

For a secure, performant prototype:

• Frontend: Tauri (Rust + web) preferred for a smaller attack surface; Electron acceptable for faster iteration.
• Daemon/Agent: Python 3.11+ service using uvicorn + FastAPI for an internal RPC layer.
• Scheduler: Python with APScheduler or a custom async loop; use SQLite for state.
• Simulator: Qiskit Aer or Amazon Braket local simulator container. Containerize the simulator for isolation.
• Credential storage: OS keyring (Windows Credential Manager/macOS Keychain/Linux Secret Service) or an encrypted local file using libsodium.

Calendar integration: keep it local and auditable

Avoid sending raw calendar events to cloud agents. Use one of these patterns:

• ICS files — Many users export shared team calendars as ICS. Parse with the icalendar Python package.
• CalDAV — Connect to an internal CalDAV server if you have one; credentials stored on device.
• OAuth with token persistence — If you must access Google or Microsoft calendars, obtain tokens locally and store refresh tokens in the OS keyring. Only pull free/busy metadata (not event bodies) for privacy.

Example: parsing an ICS free/busy window locally

from icalendar import Calendar
from datetime import datetime, timezone

with open('team_cal.ics','rb') as f:
    cal = Calendar.from_ical(f.read())

availability = []
now = datetime.now(timezone.utc)
for comp in cal.walk():
    if comp.name == 'VEVENT':
        start = comp.get('dtstart').dt
        end = comp.get('dtend').dt
        # convert to UTC-aware datetimes and compute free/busy
        availability.append((start, end))
# Build free windows by inverting busy windows

QPU availability: polling, caching, and reservation APIs

By 2026, many providers publish job reservation and telemetry APIs. Your agent should implement a configurable poller that:

• Pings provider endpoints for available reservation slots and current queue metrics
• Normalizes the provider response into a small local schema (latency, estimated wait, cost, supported gates)
• Caches results and computes a freshness score; use short TTLs for volatile endpoints

Example normalized QPU metadata (local schema)

{
  'id': 'quantum-1',
  'provider': 'ProviderX',
  'qubits': 65,
  'min_queue_seconds': 120,
  'next_reservation_slots': [ ('2026-01-18T10:00Z','2026-01-18T11:00Z') ],
  'cost_estimate_usd': 3.5,
  'error_rate_estimate': 0.02
}

Scheduling algorithm: an actionable prioritization blueprint

Keep the scheduler deterministic and explainable. Use a configurable weighted score combining user priority, calendar flexibility, cost, QPU wait time, and experiment urgency. Human-in-the-loop confirmations are required for high-cost or long-qubit runs.

Scoring function (example)

# Score = w1 * user_priority - w2 * wait_time - w3 * cost + w4 * calendar_flex
score = w1*user_priority - w2*(wait_seconds/3600) - w3*cost_usd + w4*calendar_flex_hours

Practical defaults (tuneable): w1=10, w2=1, w3=2, w4=3. Define user_priority as an integer (1–10), and calendar_flex as the number of hours a user is available around the slot.

Algorithm steps:

1. Collect candidate QPU slots that match experiment resource needs (qubit count, gate set).
2. Filter out slots overlapping with busy calendar windows for the primary experiment owner.
3. Compute score for each (experiment, slot, QPU) tuple.
4. Sort tuples by score and propose the top N to the user for approval.

Python skeleton for scheduling loop

import heapq

def schedule(experiments, qpu_catalog, calendars):
    heap = []
    for exp in experiments:
        for q in qpu_catalog:
            if q['qubits'] < exp.qubits: 
                continue
            for slot in q['next_reservation_slots']:
                if overlaps_with_busy(slot, calendars[exp.owner]):
                    continue
                score = compute_score(exp, q, slot)
                heapq.heappush(heap, (-score, exp, q, slot))
    proposals = []
    while heap and len(proposals) < 5:
        _, exp, q, slot = heapq.heappop(heap)
        proposals.append({'exp': exp, 'qpu': q, 'slot': slot})
    return proposals

Safety-first: sandboxing, simulation, and hard gates

Safety is non-negotiable. The agent should implement multiple defense layers so a bug or misconfiguration doesn't waste credits or publish secret metadata.

Safety checklist

• Local validation/simulation — run the circuit on a local simulator with representative noise models and check runtime and memory usage.
• Resource caps — reject runs exceeding configurable qubit/depth/cost thresholds without elevated approval.
• Human-in-the-loop — require a signed approval for any reservation that would consume more than X credits or more than Y qubits.
• Input sanitization — block anything that would exfiltrate calendar bodies or URIs to providers not listed in the allowlist.
• Audit trail — immutable local ledger for all schedule proposals, approvals, submissions, and rejections.
• Network firewall — the agent's network calls should be whitelisted to known provider endpoints and to nothing else by default.

Example safety gate (pseudo-code)

if exp.qubits > safety.max_qubits_without_approval:
    require_approval(exp.owner, reason='High-qubit run')

# simulate on local noise-model
sim_result = local_simulator.run(exp.circuit, shots=1024)
if sim_result.runtime_seconds > safety.max_runtime_sim:
    reject('Circuit too slow in sim')

Executor: job submission and monitoring

Implement an executor that treats provider submissions as transactions with retries and deterministic backoff. Always attach a local job id and persist provider job ids in the ledger.

Key behaviors:

• Submit validated circuits to the provider SDK or REST API
• Poll or subscribe to job updates; map provider states to local states
• On failure, capture logs and optionally requeue if retryable
• Store results in a local artifacts directory and optionally push result digests (not raw data) to team repos

def submit_job(exp, qpu):
    local_job_id = ledger.create_job_record(exp, qpu)
    provider_job_id = provider_api.submit(exp.payload, qpu['id'])
    ledger.update_job_provider_id(local_job_id, provider_job_id)
    return local_job_id

Operational tips: testing, reproducibility, and debugging

• Unit tests for scoring, calendar inversion, and provider normalization — mock provider responses for deterministic tests.
• Replayable experiments — store seeds, simulator configs, and a hashed artifact of the circuit to reproduce runs locally.
• Containerized simulator for sandboxed validation. Use reproducible images with pinned versions.
• Feature flags to enable/disable auto-scheduling and auto-submission for new users.
• Telemetry — keep metrics locally or push anonymized telemetry to an observability backend only with explicit opt-in.

Integration notes for providers (practical)

Each provider has its own API surface. Here are pragmatic guidelines to integrate widely-used patterns:

• IBM/Qiskit: use Qiskit Runtime for job submission and runtime estimation APIs. Translate backend names to local IDs and cache calibration windows.
• Amazon Braket: use the Braket SDK or REST endpoints to query device_arn and reservation windows; estimate costs via pricing APIs.
• Other providers: implement an adapter pattern so your scheduler sees a uniform schema regardless of the vendor.

When tokens are required, store them in the OS keyring and rotate periodically. For enterprise settings, allow an HSM-backed option.

UX patterns and human-in-the-loop behaviors

Good UX is safety’s ally. Present clear, minimal prompts and default to conservative actions.

• Show a proposed schedule card with: experiment name, owner, start/end, QPU name, estimated wait, estimated cost, and safety flags.
• Allow one-click approve to schedule within a soft window (e.g., reserve + submit), and a stronger confirm for escalations.
• Expose an audit timeline for each job showing who approved and which validations passed or failed.

Prototype timeline and MVP checklist

Ship an MVP in 2–4 weeks with this focus:

1. Week 1: Local calendar reader (ICS + CalDAV), simple UI for adding experiments and priorities, local SQLite ledger.
2. Week 2: QPU poller with one provider adapter + local simulator validation, basic scheduler with scoring function.
3. Week 3: Executor that submits to simulator and to a real QPU behind a manual approval gate; add OS keyring integration.
4. Week 4: Safety module: resource caps, audit trails, and basic telemetry. Polish UI and document developer extension points.

Case study (mini): scheduling an error-mitigation experiment

Scenario: You need to run an error-mitigation experiment that requires 27 qubits for 45 minutes. Team members are available between 10–12 local time. There is a 60-minute reservation window on a backend with moderate error rates and a moderate cost.

Agent flow:

1. Calendar Reader confirms primary owner is free 10–12.
2. QPU Poller reports a reservation slot 10:30–11:30 and estimated queue wait 20 minutes.
3. Scheduler scores the slot high due to owner priority and calendar flex.
4. Safety Module simulates the circuit locally, sees expected runtime 900s and memory ok; cost estimate falls under approval threshold.
5. UI presents proposal; owner approves.
6. Executor reserves the slot and submits at 10:35, streams logs to local ledger, and notifies owner of completion.

Advanced strategies and future-proofing (2026+)

Look ahead with these strategies:

• Federated scheduling — allow multiple local agents to coordinate across a trust boundary using signed proposals and a distributed ledger to avoid centralization.
• Adaptive pricing-aware scheduling — integrate spot pricing and slippage models as providers offer more dynamic pricing.
• Policy-as-code — codify safety rules so teams can version control and review scheduling policies.
• Explainability — store why a slot was chosen (score breakdown) to satisfy compliance and reproducibility.

Common pitfalls and how to avoid them

• Over-automation: never auto-submit expensive experiments without explicit, logged approval.
• Leaky privacy: don’t transmit calendar event bodies to third-party agents; use free/busy only where possible.
• Ignoring simulator validation: always simulate representative workloads to avoid failed expensive QPU runs.
• Hardcoding provider logic: build adapters to avoid brittle integrations as provider APIs evolve.

Actionable takeaways

• Prototype fast: start with ICS/CalDAV + one provider adapter and local SQLite.
• Favor local-first: store tokens, calendars, and logs on-device; push only digests or anonymized metrics with explicit consent.
• Enforce safety gates: simulate, cap, and require approvals before expensive runs.
• Make scheduling explainable: surface score components and keep an immutable audit trail.

Final notes: the role of desktop Cowork agents in quantum workflows

Desktop

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