Quantum Machine Learning

QUANTUM MACHINE LEARNING

Hybrid Quantun-Classical Algorithms

Today, QML lives in the hybrid era, where quantum processors work alongside GPUs and CPUs. In this setup, the quantum device acts as an AI co-processor, boosting performance in training and inference. This hybrid approach is already showing promise in fields like pattern recognition, generative AI, and even next-generation architectures such as Visual Transformers, pointing to how QML will transform future AI systems.

Quantum Technology Roadmap

From today’s acceleration to tomorrow’s scale

We are in the hybrid era of Quantum Machine Learning: quantum processors already accelerate training and enrich AI workflows when paired with classical hardware. These early algorithms are showing promise in classification, generative AI, reinforcement learning, and more.

The next milestone is fault-tolerant quantum computing, where reliable large-scale systems will make quantum-enhanced models deployable across industries. This is central to Quandela’s roadmap: from accelerating today’s tasks to enabling industrial-scale quantum intelligence tomorrow.

In parallel, Quantum-Inspired methods are also emerging: classical techniques that adopt quantum design principles. They create value today, while preparing organizations for a smooth transition to quantum hardware.

QML Advantages

Accelerating AI & Enriching outcomes

Quantum Machine Learning is not just about the future — it is already beginning to accelerate performance and enrich AI models today. As the technology scales, these benefits will grow even stronger:

1. Higher Accuracy
   Quantum models capture correlations that classical algorithms often miss, improving precision in classification, prediction, and generation tasks.
2. Resource Efficiency
   Quantum layers can enhance accuracy without always requiring massive parameter counts. This opens the way to smaller, more compact models that achieve comparable or better performance — a path to lighter AI.
3. Scalable Intelligence
   Quantum models can represent far richer data spaces than classical models, opening a pathway to handle more complex correlations as AI scales.
4. Faster Convergence
   Hybrid quantum–classical routines can reach target accuracy in fewer iterations or with smaller datasets than purely classical methods — reducing the effort needed to train advanced models.
5. Lower Energy Usage
   Quandela’s photonic processors consume dramatically less power than GPU-based training, offering a tangible energy advantage for intensive AI workloads.

Quantum MACHINE LEARNING Framework

MerLin: Making QML Practical Today

Quandela developed MerLin to bring Quantum Machine Learning directly into the hands of AI practitioners. It is a GPU-accelerated framework that makes it simple to embed quantum models into everyday workflows.

MerLin is built for data scientists and ML engineers:

• Familiar environment – works seamlessly with popular machine learning libraries.
• Hybrid-ready – designed for rapid prototyping of quantum–classical architectures.
• Scalable – run on simulators today and on real quantum processors through Quandela Cloud.

With MerLin, QML moves from research to practice: accelerating training, enriching models, and preparing teams for the quantum future.

AI’s Quantum Future

The next generation: accelerated, enriched, quantum-ready

Quantum Machine Learning is not just an incremental step for AI – it is a new foundation for intelligent systems. As photonic quantum computers scale toward fault tolerance, QML will deliver AI that is not only smarter, but also more efficient and more sustainable.

INDUSTRIAL APPLICATIONS

QML is already being explored in industries where AI models face limits in accuracy, scalability, or energy cost. From finance (risk scoring) to energy (materials discovery) to mobility (autonomous vision systems), Quandela works with partners to bring hybrid quantum–classical methods into real-world workflows. These collaborations show how QML is moving from research to practice.