QuipNetwork/xquadPublic

Early public release. The instruction set, binary format, and public API may still change before v1.0. Production use is not recommended yet.

XQuad is a hardware-agnostic toolchain for expressing and running quadratic optimization problems (QUBO / Ising / integer) across quantum annealers and classical solvers. It ships as three Rust crates plus five Python distributions built around a single virtual-machine specification.

Think of it as LLVM for quadratic models -- a common intermediate representation that lets you write a problem once and retarget it to any supported backend.

• Spec-first. Every behaviour is nailed down in spec/xqvm/SPEC.md and cross-implementation parity is mechanically enforced: every committed conformance vector runs on both the Rust production VM (xqvm) and the Python reference VM (xqvm_py) in CI; disagreement fails the build.
• Embeddable. The Rust core supports no_std + alloc, so the same VM runs inside WASM runtimes, Substrate pallets, and native binaries.
• Interactive. The Python umbrella (xquad) is REPL and Jupyter-friendly: load a program once, run it with different calldata, inspect outputs by slot. Low-level FFI lives in xqffi; user-facing Program / Session / RunResult live in xquad.program.

cargo install xqcli           # gives you the `xquad` binary

pip install xquad             # umbrella -- full pipeline
# or pick individual pieces:
pip install xqffi xqcp xqsa   # FFI bindings + DSL + solvers

Prebuilt wheels are available for Linux x86_64 and aarch64 (CPython >= 3.13). On macOS and Windows, pip builds the xqffi Rust extension from source -- this requires a Rust toolchain (>= 1.85).

The base install includes CPU simulated annealing only. To use GPU or quantum hardware solvers, install the corresponding extra:

Extras are composable: pip install xquad[cuda,dwave]. See xqsa/README.md for driver prerequisites and per-solver quick-starts.

For a guided walkthrough that installs XQuad, verifies it, and runs a complete problem end to end, see Install XQuad in the book.

Three Rust crates on crates.io:

Five Python distributions on PyPI:

The one Rust package without a crates.io artefact is the xqffi pyo3 crate itself -- it ships as the xqffi PyPI wheel rather than a reusable Rust library.

; add.xqasm -- push two integers and add them
PUSH 10
PUSH 32
ADD
HALT

xquad asm add.xqasm -o add.xqb && xquad run add.xqb

The recommended user-facing surface is xquad.program.Program + Session + RunResult. Load once, run many times with different calldata, inspect outputs by slot:

from xquad.program import Program

program = Program.from_source("""
PUSH 0
INPUT r0
PUSH 1
INPUT r1
LOAD r0
LOAD r1
ADD
STOW r2
PUSH 0
OUTPUT r2
HALT
""")

session = program.session(output_slots=1)
session.set_calldata([40, 2])
result = session.run()
assert dict(result.outputs) == {0: 42}

Program.load(bytes) takes wire-format bytecode; Session.run() returns a RunResult with dict-keyed outputs (unset slots present as None), residual stack, and step count. See Running Programs for the full tour.

For conformance harnesses and one-shot execution, the raw xqffi.vm.Vm is still available directly:

from xqffi.asm import assemble_source
from xqffi.vm import Vm

bytecode = assemble_source(src)
v = Vm()
v.set_calldata([40, 2])
v.set_output_slots(1)
v.run(bytecode)
assert v.outputs() == [42]

The xquad.vm.VM class offers a middle tier -- a unified wrapper with backend dispatch (VMBackend.RUST / VMBackend.PYTHON) that accepts .xqasm source and normalises types across the two interpreters.

examples/tsp/ shows a full Travelling Salesman Problem driven from the xqcp DSL through the VM and xqsa solver, runnable on either the Python reference VM or the Rust VM:

uv run --no-sync python examples/tsp/runner.py --seed 42
uv run --no-sync python examples/maxcut/runner.py --seed 42 --interpreter rust

# Run every example on both interpreters, check each finds a valid solution:
make example-smoke

XQuad is a stack-based interpreter with a 256-slot register file. Registers hold typed values -- integers, integer vectors, QUBO/Ising models (XqmxModel), and candidate solutions (XqmxSample). A dedicated loop stack drives RANGE/ITER iteration.

The opcode table is declared once in xqvm/src/bytecode/types/table.rs via the opcodes! x-macro; conformance/opcodes.yaml is the machine-readable mirror that all three representations (YAML, Rust macro, xqvm_py.opcodes) are checked against at build time and in CI (scripts/check-opcode-parity.py).

The binary format (.xqb) opens with a fixed 15-byte XQBC header -- magic, version, calldata/output-slot counts, code length, and a CRC-32 checksum of the payload -- followed by the instruction stream: an opcode byte followed by its operands in big-endian byte order.

See conformance/opcodes.md for instruction-by-instruction semantics and spec/xqvm/SPEC.md for the normative spec.

curl --proto '=https' --tlsv1.2 -sSf https://sh.rustup.rs | sh   # stable Rust
make deps                                                          # dev tools

make xquad

This syncs the Python workspace (xqvm_py, xqcp, xqsa, xqffi, xquad) into .venv/, builds the xqffi pyo3 extension via maturin, puts the repo root on sys.path so scripts anywhere in the repo can import xqcp / xqsa / xqvm_py / xqffi / xquad naturally, and installs the xquad CLI binary under ~/.cargo/bin/.

Open a REPL with everything ready:

make repl

make preflight    # what CI enforces, split into preflight-rs / -py / -parity / -docs / -policy
make conformance  # cross-impl parity suite (Rust + Python)

See CONTRIBUTING.md for the development workflow, commit conventions, and DCO sign-off requirements. Agent guidelines for AI coding assistants are in AGENTS.md; cutting a release is documented in RELEASING.md.

Licensed under the GNU Affero General Public License v3.0 or later.