Expressive instruction layer
Represent two-qubit operations as richer native targets, including SU(4)-style primitives that can capture an entire family of locally equivalent gates.
Open-source layer for reconfigurable quantum systems
Open-source infrastructure for expressive quantum hardware.
Invaris Quantum turns reconfigurable instruction-set ideas into contributor-ready software: hardware profiles, SU(4)-native compiler experiments, calibration-aware benchmarks, and documentation researchers can extend.
Invaris Quantum is an open-source software project for building, testing, and benchmarking reconfigurable quantum instruction-set components across hardware-aware compiler and control workflows.
Partner and ecosystem layer
Research credibility starts with a clear platform map.
The platform map separates the Invaris project identity from the external quantum-computing ecosystem: Dell Technologies, AWS, IonQ, IBM Quantum, Qiskit, Python, NVIDIA, and GitHub. Third-party marks remain the property of their respective owners.
What this is
A public workbench for the bottleneck between quantum hardware expressivity and usable software.
Modern quantum machines can expose richer native operations than the standard CNOT/CZ pipeline, but most researchers still need practical code paths for describing, compiling, routing, calibrating, and comparing those operations. Invaris Quantum is the open-source layer that converts that system problem into small contribution units.
Architecture in chunks
The research becomes an open-source system of profiles, passes, and evidence.
Hardware profile layer
Store device assumptions: coupling type, topology, timing model, allowed controls, calibration notes, noise summaries, and validation status.
Micro-operation layer
Translate target operations into timing-aware control parameters while tracking edge cases such as near-identity gates that should be mirrored instead of executed directly.
Compiler pass layer
Support program-aware templates, program-agnostic synthesis, and routing that understands when qubit mapping can absorb logical swaps.
Benchmark evidence layer
Compare gate count, two-qubit depth, pulse duration, mapping overhead, and fidelity-related metrics across clear benchmark manifests.
Contributor layer
Turn each research idea into a reviewable artifact: a profile, adapter, transform, fixture, notebook, report, issue template, or documentation page.
The bottleneck
Quantum hardware is not just a chip. It is an interface problem.
The central difficulty is not only whether a richer gate can exist. The hard part is whether researchers can compile toward it, express the assumptions, control calibration complexity, route circuits onto constrained topology, and reproduce the evidence later. That is why the project is organized like infrastructure rather than a single demo.
2Qnative operation focus
SU(4)expressive instruction space
3compiler pass families
openbenchmarks and profiles
System flow
From target circuit to reproducible contribution.
The public site explains the open-source component as a workbench that contributors can build on: practical modules, clear assumptions, and reproducible outputs.
Declare a target
Import a circuit, kernel, or benchmark and record the assumptions that matter before optimization starts.
Profile the backend
Attach coupling, timing, topology, calibration, and supported-control metadata through a machine-readable profile.
Compile through passes
Run template synthesis, local synthesis, approximate compaction, and topology-aware routing as separate inspectable stages.
Publish evidence
Commit the manifest, outputs, plots, environment notes, and review notes so another researcher can reproduce the result.
Software stack
Built for researchers who need code paths, not slogans.
The implementation can grow around Python tooling, circuit IR, compiler adapters, cloud backends, and benchmark automation.
Python SDK core
Configuration, profile parsing, pass orchestration, test fixtures, notebooks, and result aggregation.
python · pytest · scipy · numpyCircuit interchange
Adapters for common quantum circuit representations and an internal trace that makes transformations auditable.
OpenQASM · Qiskit · TKET · QIRSynthesis experiments
Components for canonical-gate targets, approximate synthesis, template libraries, and compactness-guided subcircuit work.
SU(4) · BQSKit · templatesCloud and hardware profiles
Profiles that connect simulated assumptions, cloud providers, and realistic backend constraints without hard-coding one vendor.
AWS Braket · IBM Quantum · local simulators
Research workstreams
Where contributors can create immediate value.
Define coupling models, supported controls, topology, durations, and calibration evidence for simulated or real backends.
Implement mapping-aware logic for near-identity gates and logical swaps so routing overhead is visible instead of hidden.
Build reusable templates for high-level circuit structures and compare them against conventional decompositions.
Design experiments that compare aggressive optimization against the number of distinct operations a backend must calibrate.
Publish manifests for QFT, arithmetic, simulation, variational, random, and routing-sensitive circuits with reproducible outputs.
Project license
Apache-2.0 license.
Invaris Quantum uses Apache-2.0 so researchers, research institutions, startups, enterprise teams, and independent contributors can reuse the code, study the methods, build integrations, and publish extensions under a clear permissive license with an explicit patent grant.
Permissive reuse
Patent grant
Industry-friendly
Research-friendly
Repository
Use GitHub for code, issues, review, and project memory.
The public repository will host the open-source work: issue templates, profiles, adapters, compiler experiments, benchmark manifests, documentation, and contribution review.