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Trending open-source repository: formalmind/formalmind

Qᴜᴀʟᴛʀᴀɴ is a Python library for expressing and analyzing Fault Tolerant Quantum algorithms.

Palomar formalizations of my preprints https://orcid.org/0009-0009-0198-085X

Quantum computing × AI from first principles in pure NumPy: Shor, Grover, BB84/E91, QAOA, VQE, quantum ML, quantum RL and quantum chaos. Readable statevector simulations, verified against exact physics in 100+ tests.

A learning operating system

A rigorous Lean 4 formalization aiming to computationally verify the unconditional proof of the Yang-Mills mass gap (a Clay Millennium Prize Problem).

A scientific computing environment similar to Octave/Matlab/SciPy in pure JavaScript for browser and nodejs

Repository for building numpy release artifacts and making releases to PyPI

Experimental L1 mined by formal proofs. Pre-launch research prototype, no token has value.

Calculate the variance of an array using the stats-array-variancepn library. Ideal for numerical and scientific computation in JavaScript. 🚀📊

Circular law for sparse random matrices with independent row slices at sufficiently large logarithmic degree, with Lean 4/mathlib proofs, manuscript, and reproducible verification.

Lean formalization of A Dichotomy for Complex Boolean Holant with Binary Disequality (paper 1), with the manuscript and official Comparator checks.

Nix overlay for Lean 4, and lake2nix

Open-source API library providing common operations for adding quantum behaviors to games.

A highlight.js language grammar for the Lean theorem proving language.

A Claude Code plugin that helps you learn how to build while AI writes the code.

Lean-verified square-packing records, provenance, exact certificates, and an interactive historical archive

viral-ngs: command line tools and wrappers for processing raw viral genomic data

Lean 4 formalization and research ledger for "Riemann Hypothesis Research Program: Complete Progress Report and Formal Ledger" by Logan Eberwein (year-long independent program). SR/Rees matrix theorems, 190 documented approaches, and corrections. Not a proof of RH.

📊 Compute the variance of strided arrays in WebAssembly for efficient numerical analysis and enhance performance in JavaScript environments.