zhinst-timing-models
Feedback Data Latency model for PQSC, SHF- and HDAWG systems.
Decision gist · record as of 2026-08-14
Yes, if you are working with Zurich Instruments quantum control hardware and need to understand or predict feedback latencies. The package is actively maintained, has no known vulnerabilities, and carries a permissive license. Install friction is low and the API is straightforward. Not relevant for users outside the Zurich Instruments ecosystem.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Low friction install with only two lightweight runtime dependencies (numpy, pyyaml).
- Active maintenance with a release 15 days old indicates ongoing support.
License · maintenance · safety
MIT (permissive) — MIT license is permissive and places no restrictions on use, modification, or distribution in commercial or private projects.
last release 2026-07-30 (15 days)
0 known vulnerabilities (OSV.dev, 2026-08-14) · 87,642 downloads/mo, #13,778 on PyPI
Alternatives
Verify before relying
pip install zhinst-timing-models
from zhinst.timing_models import (
QCCSFeedbackModel,
get_feedback_system_description,
SGType,
QAType,
PQSCMode,
TriggerSource,
)
model = QCCSFeedbackModel(
description=get_feedback_system_description(
generator_type=SGType.HDAWG,
analyzer_type=QAType.SHFQA,
pqsc_mode=PQSCMode.DECODER,
trigger_source=TriggerSource.ZSYNC,
)
)
awg_clock_cycles = model.get_latency(...)- Whether the package supports all PQSC, SHF, and HDAWG system variants or only a subset
- Whether latency calculations account for all sources of delay or only specific feedback paths
- Whether the model is validated against actual hardware measurements
What it is and what it does
This package provides a computational model for calculating feedback latencies in Zurich Instruments quantum control systems. It encapsulates timing parameters for PQSC, SHF, and HDAWG hardware, allowing users to query expected delays in quantum feedback loops given a system configuration (generator type, analyzer type, PQSC mode, and trigger source).
The package is designed for quantum researchers and engineers who need to understand or predict timing behavior in their quantum control setups. It depends on numpy and pyyaml and supports Python versions from 3.8 through 3.14. The API centers on instantiating a QCCSFeedbackModel with a system description, then calling methods to retrieve latency values in clock cycles.
Use it for
- Calculate expected feedback loop latency for a given HDAWG-SHFQA-PQSC configuration before running experiments
- Validate timing assumptions in quantum control sequences by querying model latencies
- Determine whether a proposed system architecture meets latency requirements for real-time feedback
- Compare latencies across different trigger sources and PQSC modes to optimize system design
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes, if you are working with Zurich Instruments quantum control hardware and need to understand or predict feedback latencies.
The package is actively maintained, has no known vulnerabilities, and carries a permissive license. Install friction is low and the API is straightforward. Not relevant for users outside the Zurich Instruments ecosystem.
Install
zhinst-timing-models on PyPI
Before you install
Low friction install with only two lightweight runtime dependencies (numpy, pyyaml). Active maintenance with a release 15 days old indicates ongoing support.
License in practice
MIT license is permissive and places no restrictions on use, modification, or distribution in commercial or private projects.
Quickstart
pip install zhinst-timing-models
from zhinst.timing_models import (
QCCSFeedbackModel,
get_feedback_system_description,
SGType,
QAType,
PQSCMode,
TriggerSource,
)
model = QCCSFeedbackModel(
description=get_feedback_system_description(
generator_type=SGType.HDAWG,
analyzer_type=QAType.SHFQA,
pqsc_mode=PQSCMode.DECODER,
trigger_source=TriggerSource.ZSYNC,
)
)
awg_clock_cycles = model.get_latency(...)Verify before relying
- Whether the package supports all PQSC, SHF, and HDAWG system variants or only a subset
- Whether latency calculations account for all sources of delay or only specific feedback paths
- Whether the model is validated against actual hardware measurements
Package facts
| License | MIT permissive |
| Python support | Supports the current Python release >=3.8 |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | 2 packagesnumpypyyaml |
| Maintenance | Actively maintained 15 days since the last release |
| First released | |
| Downloads | 87,642 / month, #13,778 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
| Classifiers | Development Status :: 4 - BetaProgramming Language :: PythonProgramming Language :: Python :: 3.10Programming Language :: Python :: 3.11Programming Language :: Python :: 3.12Programming Language :: Python :: 3.13Programming Language :: Python :: 3.14Programming Language :: Python :: 3.8Programming Language :: Python :: 3.9 |
Evidence: zhinst_timing_models-26.7.0-py3-none-any.whl
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