⚙️ Automation Workflows

A workflow is a repeatable four‑phase loop that quansys drives for each unique sweep point:

Phase	What happens	Key WorkflowConfig fields
Prepare	Make an iteration folder, optionally copy a fresh template.aedt	prepare_folder
Build	Edit the project for the current parameters	builder, builder_sweep
Simulate	Run one or more Simulation objects	simulations
Aggregate	Flatten JSON results and write CSVs	aggregation_dict

Stateful & resumable

quansys hashes every sweep dict (e.g. {"chip_base_width": "3 mm"}) and keeps a ledger of completed phases.
- If the hash is new, it allocates the next zero‑padded UID folder (000,001, …).
- If the hash matches a previous run, the engine skips the entire sweep—or resumes at the first unfinished phase.

Reserved identifier: build

• quansys automatically writes build_parameters.json inside every UID folder.
• The identifier for those parameters is hard‑coded as build.
• Therefore: never name a simulation "build" or "prepare", but do use "build" when you want the parameter columns in aggregation_dict.

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Quick‑start (Python)

Below is a minimal workflow in pure Python. For this example we need a working AEDT, we'll use the simple_design.aedt (refer to Getting Started for more information) For demo purposes we sweep chip_base_width over "3mm" and "3.5mm".

from pathlib import Path
from quansys.workflow import (
    WorkflowConfig, PyaedtFileParameters, 
    DesignVariableBuilder, execute_workflow
)
from quansys.simulation import EigenmodeAnalysis
from pycaddy.sweeper import DictSweep

cfg = WorkflowConfig(
    pyaedt_file_parameters=PyaedtFileParameters(
        file_path=Path("simple_design.aedt")),

    builder=DesignVariableBuilder(design_name="my_design"),

    builder_sweep=[DictSweep(parameters={
        "resonator_length": ["3mm", "3.5mm"],
    })],

    simulations={
        "eigenmode": EigenmodeAnalysis(design_name="my_design", 
                                       setup_name="Setup1")
    },

    aggregation_dict={
        "results": ["build", "eigenmode"]
    }
)


execute_workflow(cfg)  

---

Folder layout

results/  
├─ iterations/  
│├─ 000/   # chip_base_width 3mm  
││├─ build.aedt  
││├─ build_parameters.json  
││└─ eigenmode.json  
│└─ 001/   # chip_base_width 3.5mm  
│├─ …  
└─ aggregations/  
└─ results.csv  

Re‑running the script creates 002, 003, … only for new parameter hashes.

Transmon + Resonator Example

For quantum analysis with junction coupling we'll need a more complex AEDT file. For this demo we'll use the complex_design.aedt file (refer to Getting Started for more information)

from pathlib import Path
from quansys.simulation import (QuantumEPR, ConfigJunction, 
                                EigenmodeAnalysis)
from quansys.workflow import (
    WorkflowConfig, PyaedtFileParameters, 
    DesignVariableBuilder, execute_workflow
)

from pycaddy.sweeper import DictSweep

cfg = WorkflowConfig(
    pyaedt_file_parameters=PyaedtFileParameters
    (file_path=Path("complex_design.aedt")),

    builder=DesignVariableBuilder(design_name="my_design"),
    builder_sweep=[DictSweep(parameters={
        "junction_inductance": ["10nh", "11nh"],
    })],

    simulations={
        "eigenmode": EigenmodeAnalysis(design_name="my_design", 
                                       setup_name="Setup1"),
        "quantum": QuantumEPR(
            design_name="my_design",
            setup_name="Setup1",
            modes_to_labels={1: "transmon", 2: "readout"},
            junctions_infos=[ConfigJunction(
                line_name="transmon_junction_line",
                inductance_variable_name="junction_inductance"
            )]
        )
    },

    aggregation_dict={
        "eigenmode_results": ["build", "eigenmode"],
        "quantum_results": ["build", "quantum"]
    }
)

execute_workflow(cfg)

---

Phase details

1 Prepare

Default: copy AEDT file into each UID folder.

2 Build

Builder	Goal	Docs
DesignVariableBuilder	Set HFSS design variables	DesignVariableBuilder
FunctionBuilder	Execute an inline Python callable	FunctionBuilder
ModuleBuilder	Import & call <module>.build()	ModuleBuilder

Each sweep’s parameters are recorded in build_parameters.json.

3 Simulate

simulations is a dict that maps identifier → Simulation instance.
Identifiers must be unique and must not be "build" or "prepare".

QuantumEPR label cap

A single QuantumEPR can label at most three modes.
Need more? Create additional entries, each with up to three labels:

simulations.update({  
    "epr_set1": QuantumEPR(..., modes_to_labels={1: "q0", 2: "r0", 3: "bus"}),  
    "epr_set2": QuantumEPR(..., modes_to_labels={4: "q1", 5: "q2"})  
})

4 Aggregate

Each CSV listed in aggregation_dict becomes a merged table—build columns first, followed by flattened result columns.

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YAML workflows (no‑code option)

A declarative workflow.yaml gives you the same power without rebuilding the config in Python—perfect for CLI runs or CI pipelines.

1 Save your current workflow to YAML

Save workflow

# cfg is the Python WorkflowConfig we created above  
cfg.save_to_yaml("complex_config.yaml")   # writes a self‑contained YAML file

2 Load the YAML later and execute

Load & run

from quansys.workflow import WorkflowConfig, execute_workflow  

cfg = WorkflowConfig.load_from_yaml("complex_config.yaml")  
cfg.pyaedt_file_parameters.non_graphical = False   # tweak if desired  
execute_workflow(cfg)

You can now hand‑edit complex_config.yaml—add new sweep ranges, change builders, or swap simulations—then re‑run the three‑line loader above.

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CLI Execution

Once you have a YAML workflow file, you can execute it directly from the command line:

• Local testing: quansys run config.yaml
• Cluster submission: quansys submit config.yaml env_name --name job_name

See the terminal guide for complete CLI examples and best practices for workflow optimization tips.

Typical usage pattern

Most workflows use quansys run on a PC for local testing and quansys submit on Linux endpoints for cluster execution.

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For advanced options—custom output paths, parallel workers, logging hooks—see the execute_workflow API.