mqss-benchmarking-framework 1.0.0

Automated and reproducible benchmarking framework for quantum computing workflows.

MQSS Benchmarking Framework

Overview

MQSS Benchmarking Framework is an automated and reproducable tool for uniting Quantum Computing Benchmarks. It has 4 main pillars:

• Hardware Benchmarks
• Software Benchmarks
• Simulator Benchmarks
• Algorithmic Benchmarks

🛠️ Installation

This project leverages uv for dependency management and reproducibility, making setup and collaboration straightforward. To get started, ensure you have Python installed. Then, follow these steps to set up your environment using uv:

1. Install uv (if you don't have it already):

   pip install uv
   

2. Sync your environment with the project's dependencies:

   uv sync
   

🚀 Usage

MQSS Benchmarking Framework can be used in two ways: through a simple command-line interface or directly as a Python library.

💻 CLI Usage

After installation, the command mqssbench becomes available system-wide.

Listing benchmarks

mqssbench list

This prints every benchmark registered under the origin/source/name structure, including internal, external and user-defined benchmarks.

Running benchmarks

mqssbench run --config path/to/config.yaml

The --config file may contain either:

1. a single benchmark configuration (dict)
2. or a list of benchmark configurations (each a full config dict)

When a list is provided, mqssbench runs each benchmark sequentially using the same execution engine.

In development setups, you can run the CLI commands through uv:

uv run mqssbench list
uv run mqssbench run --config path/to/config.yaml

To explore all available commands and options:

mqssbench --help

Verbosity and logging

The CLI supports adjustable logging verbosity for debugging and inspection.

By default, only warnings and errors are shown.

Increase verbosity by repeating the -v / --verbose flag:

• No -v → WARNING (default)
• -v → INFO
• -vv → DEBUG

Examples:

mqssbench -v run --config path/to/config.yaml
mqssbench -vv run --config path/to/config.yaml

Benchmark results are printed to standard output, while logs are sent to standard error.

Configuration file format

A typical benchmark configuration file follows the structure shown below:

# Benchmark configuration template

# Specify the benchmark to run. Two formats are supported:
# 1. String format: "origin/source/name"
# 2. Structured format:
#      origin: <ORIGIN>
#      source: <SOURCE>
#      name: <BENCHMARK_NAME>
benchmark: <ORIGIN>/<SOURCE>/<BENCHMARK_NAME>

# Benchmark specific parameters
benchmark_params:
  <PARAM_1>: <VALUE_1>
  <PARAM_2>: <VALUE_2>
  <PARAM_3>: <VALUE_3>

# Adapter configuration
adapter: <ADAPTER_NAME>
backend: <BACKEND>

# Credentials for the adapter or backend
credentials:
  <CREDENTIAL_KEY>: <CREDENTIAL_VALUE>

# Number of measurement shots
shots: <NUM_SHOTS>

# output directory
output_dir: <PATH>

# Controls what is generated (analysis, visualizations, reports)
report:
  analysis:
    enabled: <true_or_false>
      visualization: <true_or_false>
        enabled: <true_or_false>
        show: <true_or_false>

# Controls how results are persisted
storage:
  enabled: <true_or_false>
  type: <STORAGE_TYPE>  # "file" or "sqlite"
  file:
    format: <FILE_FORMAT> #"json"
  sqlite: # this will be implemeted in future
    db_path: <DATABASE_PATH>

# Profiling controls
profiling:
  enabled: <true_or_false>
  metrics:
    # List of profiling metrics. If omitted, all supported metrics are collected.
    # Available metrics depend on your selected adaptor.
    # For example, for MQSS adaptors, these are valid metrics:
    #   mqp_api, quantum_database, quantum_job_runner, isv_job_runner,
    #   quantum_daemon_job_runner, generator, scheduler, pass_runner,
    #   transpiler, submitter, pass_selection, knitter, job_execution
    - <METRIC_1>
    - <METRIC_2>

Example of a Single benchmark config:

benchmark: core/native/randomized_benchmarking

benchmark_params:
  num_qubits: 2
  lengths: [2, 4, 8, 16]
  num_sequences: 2

adapter: mqss_qiskit
backend: QExa20

credentials:
  mqss_token: ""

shots: 1000

output_dir: "./results"

report:
  analysis:
    enabled: true
    visualization:
      enabled: true
      show: false

storage:
  enabled: true
  type: "file" 
  file:
    format: "json"

profiling:
  enabled: true
  metrics:
    - transpiler
    - submitter

Example of a multi benchmark config:

- benchmark: core/native/randomized_benchmarking
  benchmark_params:
    num_qubits: 2
    lengths: [2, 4, 8]
    num_sequences: 2
  adapter: mqss_qiskit
  backend: QExa20
  credentials:
    mqss_token: ""
  shots: 200
  output_dir: "./results"
  storage:
    enabled: true
    type: "file" 
    file:
      format: "json"

- benchmark: core/native/quantum_volume
  benchmark_params:
    num_qubits: 3
    depth: 3
    trials: 2
  adapter: mqss_qiskit
  backend: QExa20
  credentials:
    mqss_token: ""
  shots: 200
  output_dir: "./results"
  storage:
    enabled: true
    type: "file" 
    file:
      format: "json"

🧩 Python API Usage

MQSS Benchmarking Framework can also be used directly as a Python library when more control is needed.

from mqssbench.runtime import BenchmarkManager

list_of_benchmarks = BenchmarkManager.get_available_benchmarks()

config = {
  "benchmark": "core/native/randomized_benchmarking",

  "benchmark_params": {
    "num_qubits": 2,
    "lengths": [2, 4, 8, 16],
    "num_sequences": 2
  },

  "adapter": "mqss_qiskit",
  "backend": "QExa20",

  "credentials": {
    "mqss_token": ""
  },

  "shots": 1000,

  "output_dir": "./results"

  "report": {
    "analysis": {
      "enabled": True,
      "visualization": {
        "enabled": True
      }
    }
  },

  "storage": {
    "enabled": True,
    "type": "file",
    "file": {
      "format": "json"
    }
  },
  
  "profiling": {
    "enabled": True,
    "metrics": ["transpiler", "submitter"]
  }
}

benchmark_manager = BenchmarkManager(config)
benchmark_manager.dispatch()

The benchmark field must always follow the strict origin/source/name format. For example:

• Core native benchmark: core/native/quantum_volume - uses native (native provider) as source
• Core provider benchmark: core/mqt_bench/vqe_su2 - uses mqt_bench (a circuit provider) as source
• User defined benchmark: user/my_source/my_benchmark_name — uses my_source (arbitrary user source), can also leverage circuit providers

Both CLI and Python API share the same execution engine, registry system, and adapter logic.

Latest supported adapters

• Qiskit Adapter (config value mqss_qiskit)
• Pennylane Adapter (config value mqss_pennylane)

A complete list of configuration options will be listed and constantly updated for the upcoming releases

🛠️ Upcoming Features

• Integration of the Toolchain Project to the Framework for Simulator Benchmarks
• Improving the benchmark set for all types of benchmarks

📝 Contributing

Feel free to open issues or submit pull requests to improve this project!