- Closed Beta Launch: Users can now request Beta access to TQ42 Studio, featuring QAI Hub (no-code quantum Machine Learning) and Qode Engine (Python SDK).
- No-Code Quantum ML: Our newly unveiled QAI Hub eliminates coding barriers, letting you build quantum-classical models through an intuitive visual interface.
- Agentic AI Copilot: TQ Copilot actively executes ML tasks, helping teams focus on real-world problems instead of quantum complexity.
- Shape the Future: Join the Beta to test features, share feedback, and influence accessible and practical quantum AI as it evolves.
Celebrating World Quantum Day with a No-Code Quantum ML Milestone—Reimagining Accessibility and Reducing Barriers
We told you that something was on the way. You saw glimpses during last week, but did you really see this coming?
In celebration of World Quantum Day, we’re unveiling our TQ42 Studio Closed Beta—an ecosystem designed to accelerate quantum AI adoption, featuring two key components: QAI Hub and Qode Engine. While Qode Engine (Python SDK) supports advanced developers, the highlight of this launch is QAI Hub, a no-code quantum machine learning platform designed to lower entry barriers.
Why Quantum AI Matters, Yet Remains Underused
Despite its transformative potential—driving breakthroughs in industrial forecasting, supply chain optimization, and more—quantum AI remains out of reach for most teams. Specialized knowledge and heavy coding requirements frequently stall adoption. With TQ42 Studio, our goal is to open doors for quantum AI innovation:
- Achieve Greater Model Generalization
Quantum neural networks can capture subtler data patterns, leading to more flexible models that adapt well to real-world complexity. - Encode Exponentially Richer Data
Quantum states allow for exponential data encoding possibilities, letting you explore higher-dimensional features without ballooning compute costs. - Gain Deeper Insights from Smaller Datasets
By leveraging hybrid quantum-classical layers, you can uncover patterns quickly, even with limited data—crucial for R&D scenarios. - Deliver More Accurate, Reliable Predictions
From advanced forecasting to near real-time optimization, quantum ML can push predictive performance beyond standard classical methods.
We’ve built QAI Hub within TQ42 Studio to widen accessibility (through no-code), enable experiment-driven R&D at lower risk, and upskill teams faster with (evolving) TQ Academy courses.
Introducing QAI Hub: No-Code Quantum Machine Learning (Beta)
QAI Hub is at the heart of our Beta launch: the no-code quantum ML environment handles quantum layers behind the scenes, specifically addressing the challenge of steep learning curves in quantum AI:
- Build Quantum Models Visually
A user-friendly interface allows you to create, tune, test and execute hybrid quantum-classical networks via a simple visual quantum AI workflow. - Leverage TQ Copilot: Our Agentic AI That Does the Work
Within QAI Hub, our TQ Copilot automates model-design steps. You interact via text or voice, telling TQai your objectives—and it executes tasks, from swapping quantum layers to tuning hyperparameters. While still evolving, it already has a great impact for those without a deep quantum background. - Tap Into Hybrid Compute Flexibility
Currently, QAI Hub taps HPC resources (CPUs, GPUs). Select QPU integration is on the roadmap. Over time, we’ll simplify classical-quantum integration so you can seamlessly optimize performance and resources. - Focus on Innovation, Not Complexity
By offloading quantum details to QAI Hub, your attention remains on real-world challenges—from improving supply chain forecasts to detecting anomalies in industrial processes. ML engineers, data scientists, and innovation leads can quickly prototype quantum solutions or test datasets for real-world feasibility. Our goal is to evolve into a production-ready environment where enterprise-scale features become standard.
Qode Engine: Python SDK for Deeper Developer Control
Need direct code-level access? Qode Engine (also part of TQ42 Studio) is built for seasoned developers who want:
- Advanced Quantum Libraries: Tap into both quantum ML and quantum-enhanced optimization libraries like TQml, TetraOpt, TQoptimaX, QuEnc, and more, with custom scripting freedom and control.
- Integrate with Existing MLOps: Plug into your CI/CD pipelines for advanced HPC usage or specialized QPU resources.
- Production-Ready Path: As the Beta evolves, Qode Engine will expand for enterprise-grade data pipelines, compliance, and multi-user orchestration.
We recommend teams to start in QAI Hub to get comfortable with quantum AI, then switch to Qode Engine for deeper optimization and production workflows.
Calling All Pioneers: Join Our Closed Beta
On this World Quantum Day, in honor of the collective push towards making quantum technologies more accessible and practical, we invite you to join our Closed Beta. Over the next few months, we’ll be rolling out features gradually, gathering feedback, and fine-tuning everything from the TQai Copilot to data pipelines and deployment options.
How to Participate
- Request Beta Access: Fill out our brief form and tell us about your quantum AI interests.
- Experiment & Explore: When a spot opens up, you’ll get QAI Hub (no-code) and Qode Engine (Python SDK) access, along with 100 free credits for HPC usage.
- Provide Feedback: Help shape QAI Hub’s final look and capabilities—especially around TQai’s agentic AI tasks, backend resources, and large-scale model deployment.
Join us in building a more accessible quantum future. Whether you’re exploring the no-code simplicity of QAI Hub or code-based control in Qode Engine, you’ll be at the forefront of quantum AI innovation.
Want to know more about how Quantum AI can solve complex challenges for your organization? Get in touch with our team.
Stay tuned for more updates, agentic AI enhancements, and TQ Academy expansions.
(We can’t wait to see what you create.)