NxQuantum

NxQuantum is a pure-Elixir quantum ML library for the Nx ecosystem. It is built for ML engineers and researchers who want quantum primitives inside the same BEAM stack used for training loops, inference services, and production pipelines.

Who It Is For

• Teams building ML systems in Elixir/Nx that need deterministic quantum primitives in the same runtime.
• Researchers who want reproducible, typed contracts and BEAM-native integration patterns.
• Not a primary fit (today) for teams whose top requirement is immediate broad hardware-provider coverage.

Why It Matters

Quantum tooling is mostly Python-first today. NxQuantum focuses on the Elixir/Nx community by providing:

• Elixir-native primitives (Estimator, Sampler, Kernels, Transpiler).
• Deterministic behavior with explicit runtime and seed contracts.
• A cleaner path from research code to BEAM production systems.

See positioning and comparison details:

• docs/product-positioning.md

Choose Your Path

• Evaluate vs Python-first workflows: docs/python-comparison-workflows.md
• Plan migration from Python workflows: docs/migration-python-playbook.md
• Use provider-specific migration packs: docs/v0.5-migration-packs.md
• Start interactive tutorials: docs/livebook-tutorials.md
• Check provider support tiers and limits: docs/v0.5-provider-support-tiers.md
• Review reproducible provider benchmark matrix: docs/v0.5-benchmark-matrix.md
• Review Python alternatives benchmark run: docs/python-alternatives-benchmark-2026-03-21.md
• Review benchmark narrative evidence: docs/case-study-beam-integration.md

Quick Start

mise trust
mise install
mix setup
mix run examples/quantum_kernel_classifier.exs

For full setup and API walkthroughs and usage examples:

• docs/getting-started.md

Main Features (Current)

• Circuit construction and expectation estimation.
• Shot-based sampling with deterministic seeds.
• Batched estimator/sampler APIs.
• Gradient modes (backprop, parameter_shift, adjoint).
• Error mitigation pipeline (readout, zne_linear).
• Topology-aware transpilation interface.
• Quantum kernel matrix generation.

What Is Still Planned

• Additional provider depth beyond top-3 v0.5 adapters.
• Broader provider-native dynamic and non-gate-model paths.
• More benchmark-backed case studies across real BEAM deployment patterns.

Track status here:

• docs/roadmap.md
• docs/v0.3-feature-spec.md
• docs/v0.4-feature-spec.md

Docs

• docs/getting-started.md
• docs/product-positioning.md
• docs/python-comparison-workflows.md
• docs/migration-python-playbook.md
• docs/decision-matrix.md
• docs/livebook-tutorials.md
• docs/case-study-beam-integration.md
• docs/axon-integration.md
• docs/model-recipes.md
• docs/backend-support.md
• docs/api-stability.md
• docs/architecture.md
• docs/observability.md
• docs/observability-dashboards.md
• docs/v0.5-feature-spec.md
• docs/v0.5-provider-implementation-plan.md
• docs/v0.5-acceptance-criteria.md
• docs/v0.5-migration-packs.md
• docs/v0.5-benchmark-matrix.md
• docs/python-alternatives-benchmark-2026-03-21.md
• docs/v0.5-provider-support-tiers.md

Contributing

• CONTRIBUTING.md
• docs/development-flow.md