# p10node/qcpa

> A research monorepo for reproducible laptop-scale quantum computing experiments: a small provider-agnostic qcore library and 38 independent studies with classical baselines and IBM hardware runs.

- Magnitude: 0.5 out of 10 — Quiet
- Stars: 2 total · +0 stars measured 2026-10-08, 19:38–21:23 UTC, ≈ 0 by evening
- Star trust: star growth looks organic
- Category: Science & research · Language: Python · License: Apache-2.0 · Created: 2026-10-04 · Last push: 2026-10-08
- GitHub: https://github.com/p10node/qcpa · Page: https://gitnova.dev/en/r/p10node/qcpa

## Useful for

- Run a QAOA MaxCut experiment and compare against classical baselines
- Switch the backend from numpy simulator to real IBM QPU with one string
- Reproduce any experiment's results from its experiments folder using run.py

## Why it’s here

- Star-counter measurements on 2026-10-08 (UTC), 19:38–21:23: 2 → 2 stars (+0). This is the change over that interval.
- The repository is 4 days old and already has 2 stars. With less than two weeks of history, there's no usual pace to compare the spike against yet.
- Hacker News: “Show HN: Quantum computing experiments with honest classical baselines” — 9 points, 2 days ago.

## Star trust

Star growth looks organic.

Star-trust labels are heuristics based on the repository’s behavior, not a check of every stargazer.

## Numbers

- Forks: 0
- Issues and pull requests: 0
- Watchers: 0
- Average over the last week: 0 per day
- Usual pace: too little history (under two weeks)
- Stars in the last hour (measured): 0

## Stars per day, last 5 days (oldest → newest, today is partial)

2026-10-04 … 2026-10-08: 0, 1, 1, 0, 0

## Hacker News

- Show HN: Quantum computing experiments with honest classical baselines — 9 points, 0 comments: https://news.ycombinator.com/item?id=49980821

## Spotted in now

- Spotted on Hacker News

## More in this category

1. **openai/math** — 9.9 · Breakout · Science & research · Lean · +2,586 stars measured 2026-10-08, 00:01–21:11 UTC, ≈ 2,928 by evening
   A collection of mathematical manuscripts and Lean formalizations produced by an internal OpenAI model while solving open research problems. Intended for mathematicians and researchers studying the results and verifying proofs.
   Full card: https://gitnova.dev/en/r/openai/math.md
2. **google-deepmind/alphaprotein-novo** — 3.7 · Early signal · Science & research · Python · +27 stars measured 2026-10-08, 00:02–21:24 UTC, ≈ 30 by evening
   A generative diffusion pipeline for de novo enzyme design via structural motif scaffolding: it co-generates protein structures and sequences conditioned on a catalytic motif and ligand, with optional LigandMPNN redesign, AlphaFold 3…
   Full card: https://gitnova.dev/en/r/google-deepmind/alphaprotein-novo.md
3. **OpenCourant/OpenCourant** — 3.4 · Early signal · Science & research · Fortran · +20 stars measured 2026-10-08, 00:02–21:13 UTC, ≈ 22 by evening
   Open-source finite element solver codebase Radioss for highly nonlinear problems under dynamic loadings; a community fork of OpenRadioss for researchers and engineers.
   Full card: https://gitnova.dev/en/r/OpenCourant/OpenCourant.md
4. **Queuingtheorydotcom/11SquaresFormalized** — 2.9 · Steady · Science & research · Lean · +6 stars measured 2026-10-08, 00:04–21:14 UTC, ≈ 7 by evening
   Lean formalization of the optimality proof for packing 11 squares, including the exact side length and native numerical certificate checks via native_decide.
   Full card: https://gitnova.dev/en/r/Queuingtheorydotcom/11SquaresFormalized.md
5. **aipoch/open-science** — 2.5 · Steady · Science & research · TypeScript · +39 stars measured 2026-10-08, 00:06–21:24 UTC, ≈ 44 by evening
   Local-first desktop workbench for scientific research with AI agents: runs Python/R, connects scientific data sources, and tracks artifact provenance for reproducibility.
   Full card: https://gitnova.dev/en/r/aipoch/open-science.md

---

Magnitude (0–10) measures how fast and how unusually interest in a repository is growing right now. It is not a quality score.
Days are UTC. “So far today” is a fact; “expected by the end of the day” is a forecast.
Summaries and use cases are written by an LLM (DeepSeek V4.1 Flash) from the README and may be inaccurate: verify specific claims (benchmarks, speed, hardware) in the repository itself.
Data as of 2026-10-08 21:25 UTC, updated every 30 minutes.