ARTIFICIAL INTELLIGENCE
Quantum + AI, a new paradigm
GGQUANTA builds an industrial path around the deep fusion of quantum computing and artificial intelligence — shipping the world's first AI-driven experiment platform for the full quantum research workflow, the first multi-agent OS for quantum research — QuantaMate — and grounding research strength in the Xenomi domain model family and LinkCorpus knowledge base.
Core breakthrough
The intelligent experiment platform closes the loop from local simulation to real-machine runs — job dispatch, scheduling, and result retrieval in one flow.
Multi-agent OS
QuantaMate orchestrates literature, planning, coding, compute, analysis, and review into an accountable research pipeline.
Domain model strength
Xenomi delivers privatizable, provenance-aware inference; LinkCorpus supplies a large-scale literature evidence pool.
Industry thinking
Layered delivery from knowledge base to application workbenches serves research, teaching, and enterprise intelligence.
Product matrix
From knowledge base to workbench
Seven directions ship independently and reinforce each other across data, models, and agent orchestration.
QuantaMateQuantaMateA multi-agent OS that turns complex research into accountable pipelines.
XenomiXenomiA privatizable, provenance-aware domain LLM family.
SmartXSmartXEnterprise smart-office desktop client.
LinkCorpusLinkCorpusLarge-scale literature knowledge infrastructure.
AI ClassroomAI ClassroomA shareable hub for collaboration practices and playbooks.
InkMoteInkMoteDraft, refine, layout, and publish in one document chain.
Quantum Computing Intelligent Experiment Platform
From local simulation
to real-machine experiments
GGQUANTA's self-built platform integrates LLMs, multi-agent orchestration, knowledge retrieval, and visualization, and can connect to real quantum computing resources in controlled environments. Programs written by AI assistants can be submitted to real machines — research no longer stops at paper reasoning.
Job dispatch, schedule allocation, and result retrieval form one continuous chain — lowering the last barrier to quantum computing for every researcher.
- Full research loop: literature, experiment design, hybrid programming, job submission, analysis, and reporting
- Multi-agent collaboration: intent routing and workflows that pick models and toolchains by task
- Quantum platform connectivity: circuit jobs and remote notebooks via superconducting cloud and toolchains
- Interactive knowledge demos: algorithm explainers and dynamic visualizations for teaching and outreach
01
A collaborative AI research team
QuantaMate OS
QuantaMate turns complex research into parallel, accountable pipelines on a desktop workbench, deeply integrated with the intelligent experiment platform — from literature to real-machine runs.
Orchestration, analysis, and decision-assist capabilities can be reused across business lines, or serve as a base for scenario partnerships and customized delivery.
- Three-pane agent workbench: scheduling, multi-agent dialogue, and artifact review
- Role relay: literature, planning, hybrid coding, quantum compute, analysis, academic research
- MCP and Skills compatible, wired to the self-built experiment platform backend
- Natural-language instructions auto-decompose into verifiable deliverables
02
Domain reasoning inside an evidence chain
Xenomi
Xenomi targets research, government, and enterprise documents with privatizable deployment and auditable output. Together with LinkCorpus it forms a data-plus-model base for RAG and citation checks.
Quality gains come from domain data and post-training engineering. Introducing quantum annealing algorithms expands the application boundary of quantum algorithms in AI research workflows and explores a new path for domain models and quantum computing to work together. Xenomi can serve as the default or optional inference backend for QuantaMate, the lab platform, InkMote, and SmartX skill chains.
- A domain model family sized for different scenarios, scheduled flexibly by task
- Retrieval–draft–revise loop with LinkCorpus to reduce hallucinations and unauditable citations
- Covers academic research, official docs and bids, polishing, and enterprise knowledge Q&A
- Staged material workflows: pool, structure, draft, and revise with checkable handoffs
- Quantum annealing exploration: bringing quantum algorithms into AI research workflows and exploring a new path for domain models and quantum computing to work together
- 01A domain model family sized for different scenarios, scheduled flexibly by task
- 02Retrieval–draft–revise loop with LinkCorpus to reduce hallucinations and unauditable citations
- 03Covers academic research, official docs and bids, polishing, and enterprise knowledge Q&A
- 04Staged material workflows: pool, structure, draft, and revise with checkable handoffs
More capabilities
Office, knowledge, and content production
Beyond the research spine, these products extend agent capabilities into enterprise office and knowledge collaboration.