Quantum AI allows models to be compressed by up to 95%, reducing costs and energy while maintaining performance, for faster, local and sovereign AI.
In twelve months, AI has undergone rapid development, becoming an essential strategic pillar. In France, where scientific excellence meets a diversified industrial fabric and a strong culture of technological innovation, AI is no longer a simple trend: it has already entered operational uses, all sectors combined. But this enthusiasm is accompanied by a technical and economic reality: AI models, particularly the most powerful large language models (LLMs), are becoming increasingly large, extremely energy-intensive and expensive to deploy on a large scale.
The constant increase in the size of LLMs leads to an increasing demand for computing resources, high-end GPUs and massive cloud infrastructures. For many businesses, operating costs become prohibitive.
Applied quantum AI: tensor networks for radical efficiency
Faced with this situation, a new approach is emerging to make AI more accessible, efficient and adaptable to local conditions: tensor networks inspired by quantum mechanics. These offer several advantages over traditional compression techniques. Rather than designing ever-larger models, the goal is to compress existing models, via tensorization (identifying layers of a neural network that can be collapsed and fragmenting their large matrices into smaller, interconnected matrices), and via quantization (reducing numerical precision). These processes make it possible to reduce the size of models by up to 95% while preserving their performance and significantly improving their efficiency. Concretely, the technology restructures the representation of neural networks to eliminate superfluous parameters while retaining full functionality. It works by identifying and retaining only the most relevant correlations between data.
The result: an AI model compact enough to run on devices previously excluded from AI deployment. With a simplified internal architecture, compressed models also process requests faster (measured in tokens per second), resulting in faster user interaction, system responses, and results. Energy efficiency is also optimized: as fewer operations are required per inference, energy consumption can drop by up to 50%, reducing operating costs. Finally, a decisive advantage lies in hardware independence: these ultra-compressed models can be deployed on a wide range of platforms, from large servers to edge devices, without relying on rare or expensive GPU clusters or a permanent internet connection.
Although the theoretical foundations of tensor networks come from quantum mechanics, their application in AI remains fully compatible with classical digital infrastructures. In other words, ideas from quantum science directly benefit traditional computing environments.
These advances make it possible to create much more compact AI models, capable of achieving performances equivalent to, or even superior to, those of the original LLMs. In operational conditions, this translates into faster analyses, increased responsiveness and significantly reduced infrastructure constraints. This approach could have a major impact on French industry.
From the power of the cloud to the agility of the edge: AI deployed everywhere
Until now, cloud architecture has dominated the AI sector. But ultra-compressed models fundamentally change this paradigm. Smaller, more efficient and more adapted to processors, they allow local so-called edge deployment. This approach is not only more practical but also opens up new application possibilities.
Examples abound in different sectors. In automobiles, for example, AI systems for navigation and safety can operate directly on board, independent of cloud services, even in tunnels or remote areas. In consumer electronics and wearables, AI capabilities can now be available offline, enhancing both privacy and user experience. In industrial automation, edge AI can monitor machines and optimize production workflows without transferring sensitive data externally, a critical asset for regulated industries like life sciences or sites without a stable internet connection.
Health: compact and secure AI at the heart of hospitals
In healthcare, data privacy is not just a regulatory issue, but an ethical imperative. Medical records are among the most sensitive data, and hospitals should avoid cloud AI systems that transfer this data to external providers.
Compressed AI models offer a decisive alternative: they allow complex models to be run directly on local infrastructures or secure private clouds. This could be the hospital data center, or even endpoints like iPads and internal workstations. Patient data thus remains protected behind the establishment’s firewall.
The squeeze also paves the way for smaller healthcare facilities, often limited in budget or infrastructure, to now access these advanced capabilities. Concretely, diagnostics become faster and more reliable. Medical staff benefit from AI support without the risk of data leaks, while complying with regulatory and operational requirements.
Defense: compressed AI, a strategic asset without network dependence
The defense sector also benefits from compressed models. Modern military operations rely more and more on the real-time analysis of data from drones, surveillance systems or tactical decision aids. As these systems are often deployed in remote or hostile areas without a reliable internet connection, local AI solutions are essential.
Compressed models offer a decisive advantage: they can be deployed locally on hardware with limited computing capacity, such as drones or embedded systems. By reducing model size and hardware requirements, AI can operate entirely at the edge of the network, providing immediate real-time intelligence, without relying on external infrastructure or consuming too much power.
Local deployment also strengthens security: sensitive data remains in the operational zone, increasing tactical reliability, particularly in cybersecurity and electronic warfare. The key technology challenge is balancing compression and performance. Thanks to tensor network compression, defense agencies can maintain model reliability while using more compact and efficient hardware.
Industry: produce faster, more efficiently, with lightweight AI
One of the most compelling validations of compressed models took place at a European aeronautical component manufacturing plant. The objective: to reduce the size of the AI model used in production, without sacrificing its performance.
Using advanced tensor network compression methods, the model size has been significantly reduced, enabling approximately twice the response time, better integration with existing systems, and approximately 50% lower power consumption. The compressed model thus made local decision-making possible in real time, in robotics, quality control or maintenance, without data transfer to remote servers or dependence on an unstable internet connection.
For French manufacturers engaged in lean and environmentally friendly production, these savings not only mean a measurable reduction in costs, but also a further step towards smarter and more efficient production.
Digital sovereignty: France facing the opportunity of compressed AI models
France, a country of engineering and innovation, particularly in aeronautics, energy, health and the digital industry, is today in a privileged position to adopt these compression techniques early. From manufacturing to the operating room, compressed models deliver faster analytics, better power efficiency, and increased data privacy without compromising accuracy. The priority given to sovereign and local data management also aligns with French ambitions in terms of digital sovereignty and technological independence.
AI is no longer defined by the excess of its models, but by the intelligence of their design. Compressed AI marks a major breakthrough in the way machine learning systems are developed, deployed and used. It demonstrates that it is possible to combine performance, energy efficiency and technological sovereignty. More compact but just as powerful, it embodies a new generation of AI ready to transform French industry – today and for decades to come.
