Modeling Worst-Case Execution Time for Quantum Control Software with Qiskit and Vector-like Tooling

Hook: Why your quantum control stack needs WCET now

Quantum experimenters and control-engineering teams face a familiar, growing pain: long-tailed latencies and opaque I/O boundaries in pulse-level drivers make real-time control fragile and unreproducible. In 2026 the problem is more urgent — more labs are deploying hybrid classical-quantum control loops for adaptive experiments, error mitigation, and closed-loop calibration. Inspired by Vector Informatik’s January 2026 acquisition of StatInf’s RocqStat (a move that highlights how timing safety is moving into new domains), this guide shows how to instrument pulse stacks and estimate Worst-Case Execution Time (WCET) for quantum control software using Qiskit and vector-like tooling principles.

Executive summary (what you’ll get)

This hands-on article gives you a reproducible workflow to:

• Map and instrument the quantum control stack (host code, drivers, AWGs, FPGA/RTOS)
• Collect latency traces using host timestamps, hardware markers, and loopback probes
• Estimate WCET using a hybrid approach: measurement-based statistics + static bounds
• Integrate results into CI and verification workflows (Vector-like timing tools)
• Design safer real-time control loops: deadlines, watchdogs, degraded modes

Context: Why 2026 makes timing analysis for quantum control critical

Late 2025 and early 2026 saw two key trends that change how we treat timing in quantum systems:

• Cloud and on-prem quantum providers improved streaming and lower-latency pulse APIs, enabling adaptive feedback within experimental windows that were previously impossible.
• Industry cross-pollination: automotive and aerospace verification approaches (e.g., WCET toolchains) are being adapted to scientific and safety-adjacent quantum applications. Vector’s acquisition of RocqStat reinforces this — timing verification is a solved problem in one domain and now an essential capability in quantum control.

1) Map the control stack and define timing boundaries

Before measuring anything, create a simple block diagram of your control stack. Typical layers:

• Experiment application (Python, scheduler, optimizer, high-level logic)
• Pulse driver or SDK (Qiskit Pulse, Cirq/Quantum Engine, Pennylane with hardware plugin)
• Transport (gRPC, TCP, DDS, custom streaming)
• AWG / FPGA (real-time waveform generators, marker outputs)
• Measurement chain (digitizers, classical FPGA processors, real-time analyzers)

Decide the timing domain boundaries you care about. Examples:

• Host-to-AWG command latency (send waveform -> AWG starts output)
• AWG processing latency (queued waveform -> waveform on analog line)
• Acquisition latency (excitation -> digitizer samples available)
• End-to-end loop latency (decision -> corrective pulse)

2) Instrumentation strategy — principles and probes

Combine three orthogonal probes to capture realistic worst-case behaviour:

1. Host timestamps (high-resolution monotonic clocks)
2. Hardware markers / digital loopback (toggle a digital out and record on an oscilloscope or digitizer)
3. FPGA / AWG event timestamps (if the hardware provides hardware event logs)

Why three? Host timestamps capture software jitter and scheduler effects. Hardware markers expose the true physical latency and the AWG/FPGA processing path. Hardware event logs give internal timing visibility you can map against software traces.

Practical probes you can add today

• Insert a marker pulse immediately before you issue a waveform and another shortly after acquisition start — record both on a scope.
• Use loopback: route an AWG marker to the digitizer input so the digitizer records host-driven events in the same acquisition as qubit signals.
• Enable any hardware event log on your AWG/FPGA and export timestamps.

3) Example: Instrumenting with Qiskit (host + hardware marker approach)

The following pattern is SDK-agnostic but uses Qiskit-friendly idioms. The goal: measure the time from issuing a control command to the first sample on the digitizer.

Step A — host-side timing wrapper (Python)

import time
from qiskit import assemble

def timed_run(backend, schedule):
    t0 = time.perf_counter_ns()
    job = backend.run(schedule)  # non-blocking in many SDKs
    t_submit = time.perf_counter_ns()
    result = job.result()       # blocking until complete
    t_end = time.perf_counter_ns()
    return {"submit_ns": t_submit - t0, "end_ns": t_end - t0, "result": result}

This gives you coarse host-side timing: submit latency and total blocking latency. But it misses AWG internal delays. Add hardware markers.

Step B — schedule a marker pulse (pseudo-concrete)

Most AWGs and pulse engines support a digital marker channel. The pseudo-code below shows a schedule that plays a drive pulse and toggles a marker. Adapt names to your SDK/hardware.

# Pseudocode / adapt to your vendor API
from qiskit import pulse
from qiskit.pulse.library import Gaussian

drive = pulse.DriveChannel(0)
marker = pulse.OutputChannel(0)  # replace with your AWG's marker channel

with pulse.build(name="marker_test") as s:
    pulse.play(Gaussian(duration=128, sigma=16), drive)
    pulse.play(pulse.Constant(1.0, 10), marker)  # short digital marker

job = backend.run(s)

Physically wire the marker channel to a digitizer input or oscilloscope. The scope will record an edge that corresponds to when the AWG physically began outputting the waveform.

Step C — correlate traces and compute latencies

Export hardware timestamps (scope/digitizer) and correlate them with host timestamps using monotonic clocks and known offsets. If your AWG provides event logs, use them to map host submit times to AWG sample times with microsecond precision.

4) Integrations: Cirq and PennyLane

Not using Qiskit? The same principles apply. Examples:

Cirq (for users of Quantum Engine or custom pulse backends)

Wrap your backend.run() with host timestamps as above. Insert a marker command or use a vendor-specific AWG call to toggle an output. If the backend supports measurement streaming, log the timestamp of the first streaming sample.

Pennylane (hardware plugin model)

Pennylane plugins often expose a low-level driver or a hardware API. Instrument the plugin's execute() or run() call and add a marker pulse through the plugin API. Capture plugin logs and hardware event traces.

5) From traces to WCET: hybrid measurement + static bounding

There are three useful approaches to WCET in this domain — don’t rely on one alone:

1. Measurement-based worst-case: collect a large sample of latency traces under realistic stress (background load, network contention). Use the maximum observed latency plus a safety margin.
2. Statistical tail modeling: fit an extreme-value distribution (e.g., GEV) or use Peak Over Threshold (POT) with generalized Pareto to extrapolate tail behavior and produce a high-confidence bound (e.g., 1e-9 exceedance probability).
3. Static analysis / code-level WCET: apply Vector-like static timing tools to the deterministic parts of the driver (device firmware, RTOS tasks, AWG microcode) to produce formal upper bounds where possible.

Hybrid approach: Use static analysis for on-hardware deterministic components (FPGA, RTOS tasks) and measurement + statistical extrapolation for network stacks and host OS scheduling. Combine the bounds conservatively: WCET_total = WCET_static + WCET_measured_tail + margin.

Practical recipe for a defensible WCET number

1. Collect N >= 10000 cycles of traces under multiple load scenarios (idle, CPU stress, network load).
2. Compute empirical max, p99.999, and fit an EVT model to the top 1% of samples.
3. Perform static WCET on the AWG/FPGA firmware where source or models are available.
4. Sum deterministic WCET and EVT-derived bound; add engineering margin (5–20% depending on criticality).
5. Validate bound with hardware-in-loop stress tests and watchdog trip confirmation.

6) Using vector-like tooling and CI for timing safety

Vector’s strategy — integrating RocqStat timing analytics into a testing toolchain — is instructive. You can emulate a similar workflow:

• Store latency traces as artifacts in CI (time-series, histograms, EVT model parameters).
• Run nightly or release-blocking timing regression tests that re-measure key WCET paths.
• Automate static timing checks where vendor toolchains are available (e.g., compile-time flow for AWG firmware).
• Alert on drift: if observed p99.999 increases by >X%, fail the pipeline and capture traces for triage.

Consider integrating eBPF or LTTng tracing on Linux hosts to get fine-grained syscall and scheduling events correlated with your host-side timestamps.

7) Designing safer real-time control loops with WCET inputs

Once you have WCET per path, apply these practical strategies to make the loop robust:

• Deadline budgeting: assign each pipeline stage a budget (e.g., transport 50 µs, AWG 100 µs) and enforce via admission control.
• Graceful degradation: if the loop cannot meet deadline, fall back to precomputed corrective pulses or safe idle mode rather than missing state updates.
• Watchdogs & fail-safe: add hardware watchdogs tied to markers that force a safe qubit idle waveform when a software deadline is missed.
• CPU isolation & real-time priorities: run hard real-time components on isolated cores with PREEMPT_RT or on an RTOS; pin critical threads and avoid garbage-collected languages for the tight loop.
• Predictive batching: when jitter is unavoidable, precompute multiple candidate control actions and send them to AWG with indexed triggers — the FPGA selects the correct one on a low-latency decision bit.

8) Example: Closed-loop adaptive calibration with bounded latency

Suppose you need to measure a qubit T1 in a shot-by-shot adaptive loop and apply a corrective pulse within 500 µs. Use the WCET workflow to prove you can meet the bound:

1. Instrument the path (host submit -> AWG output -> digitizer sample -> host decision -> AWG correction).
2. Collect traces under load and compute WCET_total = 420 µs using the hybrid method.
3. Allocate budget: measurement acquisition 200 µs, data transfer + decision 150 µs, corrective pulse issuance 70 µs.
4. Implement watchdog that asserts a hardware marker at 500 µs to force safe reset if no correction is applied.

This process turns a vague “we think it’s fast enough” into a verifiable claim you can check in CI and on-site.

9) Continuous monitoring and field validation

WCET isn’t a one-time calculation. Hardware/firmware updates and environmental changes affect tails. Build observability:

• In-situ histograms and rolling EVT fits shipped as telemetry.
• Automated trigger of deeper tracing when latency crosses thresholds.
• Baseline regression tests before every production deployment.

10) Common pitfalls and how to avoid them

• Too small a sample: long-tailed events may be rare — insufficient samples produce optimistic WCETs.
• Ignoring background load: run tests with realistic background CPU, disk, and network load.
• Mixing timing domains: don’t conflate AWG firmware WCET with host OS scheduling jitter; keep them separate and compose conservatively.
• Blind trust in SDK timestamps: SDKs may report logical timestamps; validate with hardware marker loopback.

11) Tools and libraries to accelerate your work

• Time-series storage (InfluxDB, Prometheus) for latency histograms
• EVT/statistics libraries (SciPy, statsmodels, pyextremes)
• Tracing (eBPF, LTTng) on Linux hosts
• AWG vendor event logs and SDKs — prefer deterministic firmware builds
• Static WCET analysis tools (in-house or vendor solutions inspired by RocqStat/Vector)

"Timing safety is becoming a critical requirement" — industry investment in timing verification (Vector / RocqStat, Jan 2026) shows this is not optional for production-grade quantum control.

12) Checklist: Instrumentation to deployment

• Map timing boundaries and identify deterministic vs stochastic components
• Add marker loopback and enable AWG event logs
• Collect >= 10k runs across load profiles
• Fit EVT models and run static WCET on firmware where possible
• Compose bound and add margin; integrate into CI tests
• Deploy watchlists and telemetry for production drift detection

Final thoughts and next steps

Quantum labs can no longer accept opaque latency claims when adaptive and safety-critical control is on the roadmap. By adopting a Vector-like timing and verification posture — combining measurement-based profiling, EVT tail modeling, and static analysis — you can produce defensible WCET guarantees and design control loops that fail predictably and safely.

Actionable takeaways (start now)

• Instrument one critical path with a digital marker loopback and collect 10k traces.
• Run a quick EVT fit on the top 1% of samples to get a provisional tail-bound.
• Integrate a nightly timing regression job in CI and add a watchdog at the firmware level.

Call to action

If you’re ready to harden your quantum control stack: publish your instrumented traces, schedules, and EVT models on qbitshare to build reproducible WCET datasets, or contact our team for a workshop that integrates your traces with vector-style timing verification. Share a reproducible experiment and we’ll help you turn measured latencies into verifiable safety budgets.

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