QuipNetwork/xquad

Health v2 · maintenance only

Flags: no_releases young

Adoption not part of the score

5588 stars · 18 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-29, confidence not recorded

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

library · maturity experimental

interpreter compiler simulation sdk cli quantum-computing performance developer-tools rust python windows wasm cli quantum-vm qubo ising-model quadratic-optimization quantum-annealing bytecode assembler dwave simulated-annealing no-std intermediate-representation algorithms linux macos

Member repositories

For agents

markdown · JSON · MCP: product_card(name="QuipNetwork/xquad")