amazon-braket-schemas
An open source library that contains the schemas for Amazon Braket
Decision gist · record as of 2026-08-14
Yes, if you are working with Amazon Braket quantum tasks or need to serialize/deserialize quantum program schemas. The package is actively maintained, has no known vulnerabilities, minimal dependencies, and supports current Python versions. It is permissively licensed and well-suited for both standalone schema validation and as a dependency of larger Braket workflows.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires Python 3.11 or later.
- Low installation friction with a single runtime dependency (pydantic).
- Actively maintained with a release 15 days ago; repository is not archived and supports current Python versions (3.11, 3.12, 3.13).
License · maintenance · safety
Apache License 2.0 (permissive) — Licensed under Apache License 2.0 (permissive), allowing commercial and private use with minimal restrictions; suitable for most projects.
last release 2026-07-30 (15 days) · last repo commit 2026-08-05 · 58 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 116,325 downloads/mo, #12,211 on PyPI
Alternatives
Verify before relying
pip install amazon-braket-schemas
from braket.ir.openqasm import Program as OpenQASMProgram
program = OpenQASMProgram(source="OPENQASM 3.0; cnot $0, $1;")
print(program.json(indent=2))- Whether the package is typically installed standalone or primarily as a transitive dependency of amazon-braket-sdk-python.
- Performance characteristics when serializing/deserializing large quantum programs or device capability payloads.
What it is and what it does
Amazon Braket Python Schemas is a library that defines and manages the data structures used by Amazon Braket for quantum computing. It provides pydantic-based schemas for quantum task intermediate representations (such as OpenQASM programs), S3 result payloads, and device capability descriptions. The library handles serialization to JSON and deserialization back into Python objects, establishing a contract between the Braket SDK and the Braket API.
The package is designed to be used either standalone for schema validation and data transformation, or as a dependency of the broader Amazon Braket SDK. It supports OpenQASM 3.0 programs and other quantum task formats, allowing developers to construct, validate, and exchange quantum program definitions programmatically.
Use it for
- Serialize quantum programs written in OpenQASM to JSON payloads for submission to Amazon Braket.
- Deserialize and validate quantum task results returned from S3 after execution on quantum devices.
- Validate device capability descriptions to determine supported gates and parameters before submitting tasks.
- Build custom quantum program representations that conform to Braket's intermediate representation contract.
- Integrate quantum program definitions into CI/CD pipelines with schema-based validation.
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes, if you are working with Amazon Braket quantum tasks or need to serialize/deserialize quantum program schemas.
The package is actively maintained, has no known vulnerabilities, minimal dependencies, and supports current Python versions. It is permissively licensed and well-suited for both standalone schema validation and as a dependency of larger Braket workflows.
Install
amazon-braket-schemas on PyPI
Before you install
Low installation friction with a single runtime dependency (pydantic). Actively maintained with a release 15 days ago; repository is not archived and supports current Python versions (3.11, 3.12, 3.13).
Requires Python 3.11 or later.
License in practice
Licensed under Apache License 2.0 (permissive), allowing commercial and private use with minimal restrictions; suitable for most projects.
Quickstart
pip install amazon-braket-schemas
from braket.ir.openqasm import Program as OpenQASMProgram
program = OpenQASMProgram(source="OPENQASM 3.0; cnot $0, $1;")
print(program.json(indent=2))
Verify before relying
- Whether the package is typically installed standalone or primarily as a transitive dependency of amazon-braket-sdk-python.
- Performance characteristics when serializing/deserializing large quantum programs or device capability payloads.
Package facts
| License | Apache License 2.0 permissive |
| Python support | Supports the current Python release >=3.11 |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | 1 packagepydantic |
| Maintenance | Actively maintained 15 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 116,325 / month, #12,211 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
| Classifiers | Development Status :: 5 - Production/StableIntended Audience :: DevelopersLicense :: OSI Approved :: Apache Software LicenseNatural Language :: EnglishProgramming Language :: PythonProgramming Language :: Python :: 3.11Programming Language :: Python :: 3.12Programming Language :: Python :: 3.13 |
Evidence: amazon_braket_schemas-1.32.1-py3-none-any.whl
Tags
Let your AI agent find packages like this
Example. Real query, live index.
You found this page by searching. An agent finds it by wishing: SkillFed indexes 14,416 PyPI packages by what they can do, searchable in plain language.
wish › “quantum task serialization”
- amazon-braket-schemasProvides serialization, deserialization, and schema definitions for…
- amazon-braket-sdkProvides a Python framework to design, simulate, and execute quantum…
- qm-octaveSDK for controlling Quantum Machine's Octave hardware using QUA,…
Give your agent the search over MCP, or paste the wish link into any chat.
More Scientific/Engineering packages
NumPy provides an N-dimensional array object and a comprehensive suite of mathematical, linear algebra, Fourier transform, and random number functions for scientific computing in Python.
pandas provides fast, flexible data structures (Series and DataFrame) for loading, cleaning, transforming, and analyzing labeled or relational data in Python.
scipy provides numerical algorithms for mathematics, science, and engineering—including optimization, integration, linear algebra, Fourier transforms, signal and image processing, and ODE solvers—built on numpy arrays.
scikit-learn provides a comprehensive Python library for supervised and unsupervised machine learning, including classification, regression, clustering, dimensionality reduction, and model evaluation tools built on NumPy and SciPy.
Install it if you need to train, evaluate, or deploy supervised or unsupervised learning models.
dill extends Python's pickle module to serialize and deserialize a much wider range of Python objects, including functions, lambdas, classes, and interpreter sessions, to byte streams for storage or network transmission.
Multiprocess is an enhanced fork of Python's standard multiprocessing library that uses dill for better serialization, allowing you to spawn processes with a threading-like API and share complex objects between them.
Install it if you use multiprocessing and encounter pickle serialization limits with lambdas or complex objects.
See also amazon-braket-default-simulator · amazon-braket-sdk · ibm-quantum-schemas · openqasm3 · py-avro-schema · qiskit-terra · hugr · qiskit · amazon-ion · sagemaker-schema-inference-artifacts