QuantFenix Open - Complete Overview

QuantFenix Open is a pragmatic CLI/SDK for running optimization workloads — Vehicle Routing (VRP), Knapsack, Scheduling — across classical solvers, quantum services, and specialized hardware. You set the goals for cost, latency, and quality. QuantFenix routes to the right backend and enforces your budget.

Think of it as a router and orchestrator for optimization jobs.

The problem it solves

When you model an optimization task (e.g., “best route for 50 deliveries”), you face:

• Many backends: OR‑Tools (local), IBM Quantum, AWS Braket, Rigetti, D‑Wave — each with its own API, cost, and latency.
• Hard trade‑offs: cost vs. quality vs. runtime. You need a consistent way to choose.
• Operational friction: multiple SDKs and credentials; cost control; reproducibility and audits.

QuantFenix simplifies this with:

• A **unified CLI (qf)**
• Policy‑based selection (e.g., 60% cost, 30% latency, 10% quality)
• Budget caps and alerts to prevent runaway spend
• Reproducible runs via signed manifests
• Offline baselines with OR‑Tools

How it works

Workflow at a glance

1. Define your problem and backends in qf.yaml
2. Run with qf run --config qf.yaml
3. Inspect JSON/CSV reports and the run manifest for audits and comparisons

Core components

1. **CLI (qf)**
2. Configuration (qf.yaml) with typed validation
3. Provider adapters (pluggable)

• OR‑Tools: local classical solver
• IBM Quantum via Qiskit _(optional)_
• AWS Braket _(optional)_
• Rigetti _(optional)_
• Custom (BYOC): bring your own adapter through a small interface

1. Policy engine to score/route by cost, latency, quality, and budget constraints
2. Input adapters: CSV files, S3 paths, or in‑memory DataFrames
3. Reports: JSON (machine‑readable), CSV (spreadsheet‑ready), Markdown summaries; run manifest for audit trails

Use cases

• E‑commerce delivery optimization (VRP)
• Warehouse inventory allocation (Knapsack / variants)
• Staffing and job scheduling (hard constraints + soft penalties)

Architecture principles

• Thin, composable client: small surface; adapters over inheritance
• Offline‑first: always runnable locally with OR‑Tools
• Reproducible by design: seeds and manifests for deterministic comparisons
• Security‑aware: no secrets printed; budget caps; optional local PII checks
• Type‑safe and testable: strict typing, clear protocols, strong unit coverage
• Developer‑friendly: task‑oriented docs and minimal examples that run out of the box

Security and compliance

• Budget enforcement to prevent runaway costs
• Local PII detection (optional) to help meet compliance needs before remote execution
• No secrets in logs; use environment variables or profiles for credentials
• Audit trails via per‑run manifests
• Deterministic modes for repeatable results

Tech stack

• Language: Python 3.10+
• CLI: qf (Typer/Click‑style ergonomics)
• Baselines: OR‑Tools
• Optional providers: Qiskit (IBM), Amazon Braket SDK, etc. (installed separately)
• Artifacts: JSON/CSV reports, run manifests
• Quality: pytest, mypy (strict), ruff

Project scope (this repository)

Included

• CLI/SDK for running optimization tasks
• Templates: VRP, Knapsack, Schedule
• OR‑Tools baseline (local)
• Provider adapters (hooks/shims) for external services
• Config parsing and validation
• Report generation (JSON/CSV/Markdown)
• Client‑side budget enforcement
• Optional, local PII checks

Not included in this repository

• Organization‑wide policy management
• Managed scaling/queues/retries
• Web dashboard and user management
• SSO/RBAC
• PDF report packs

These capabilities are typically provided by managed/hosted platforms and are intentionally not part of this open repository.

Summary

QuantFenix Open is a policy‑driven orchestrator for optimization jobs. Define your model in YAML, run it across diverse backends, keep costs in check with budget caps, score by cost/latency/quality, and reproduce every run with manifests — all while remaining fully functional offline via classical baselines.

Who uses it

• Data scientists running optimization experiments
• DevOps/MLOps teams needing reproducible workflows
• Enterprises with audit and compliance needs
• Teams exploring quantum alongside classical baselines

Get started

QuantFenix Open is available as an open‑source project on GitHub:

**View QuantFenix Open on GitHub**

The repository includes:

• Complete CLI/SDK implementation
• Example configurations for VRP, Knapsack, and Scheduling
• Provider adapters for major quantum and classical backends
• Comprehensive documentation and tutorials
• Active community support

Next steps

1. Clone the repository and follow the setup guide
2. Try the examples with your own CSV
3. Join the community on Discord for support and discussions
4. Contribute by opening issues or submitting pull requests

Star the project to stay updated with the latest releases and features.

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