# QuipNetwork/xquad

A rust implementation of the Quip Network's quantum virtual machine.

Repository: https://github.com/QuipNetwork/xquad
Canonical: https://ross.abutalabs.com/products/xquad
Language: Python
License: AGPL-3.0
License Family: copyleft
Last push: 2026-07-27T21:28:54+00:00

## Health v2 (maintenance only)
Score: 57/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 94, release rhythm 35, longevity 10
- inputs: {"age_days": 153, "days_push": 37, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases, young
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 5588, forks 18 (observed 2026-08-28T04:09:22.654822+00:00)

## What it is
XQuad is a hardware-agnostic toolchain for expressing and running quadratic optimization problems (QUBO/Ising/discrete) across quantum annealers and classical solvers, built around a virtual-machine specification with Rust crates and Python distributions. It acts like 'LLVM for quadratic models', providing a common intermediate representation that can be retargeted to backends including CPU simulated annealing, CUDA/Metal GPUs, and D-Wave QPUs.

## Use cases
- run QUBO optimization problems on quantum annealers
- compile quadratic models to a portable bytecode format
- simulate Ising models on CPU or GPU
- solve discrete optimization with D-Wave Advantage
- embed a quadratic VM in WASM or Substrate pallets
- prototype optimization problems in Jupyter or a REPL
- verify cross-implementation parity of quantum VM bytecode

## When to choose
- you need a backend-agnostic IR for QUBO/Ising problems
- you want to target both classical solvers and D-Wave hardware from one codebase
- you need a no_std embeddable VM for constrained environments
- you want spec-first conformance testing across implementations

## When to avoid
- you need production-grade stability before v1.0
- you need gate-based quantum computing rather than annealing
- you need general-purpose linear or continuous optimization
- you are not comfortable with early APIs that may change

## Facets
- artifact type: library
- maturity: experimental
- function: interpreter, compiler, simulation, sdk, cli
- domain: quantum-computing, performance, developer-tools
- platform: rust, python, windows, wasm, cli
- tags: quantum-vm, qubo, ising-model, quadratic-optimization, quantum-annealing, bytecode, assembler, dwave, simulated-annealing, no-std, intermediate-representation, algorithms, linux, macos

## Member repositories
- QuipNetwork/xquad (main) score 57

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:09:22.654822+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-29T17:55:31.506887+00:00, confidence not recorded.
  - readme: https://github.com/QuipNetwork/xquad (fetched 2026-08-28T04:09:22.654822+00:00, sha 4d8f06885c67)
- Data as of 2026-08-30T08:39:29.467469+00:00.