Q-SCAN

Q-SCAN is a quantum image segmentation application designed to efficiently detect flooded areas from satellite images.

Image segmentation is a computer vision technique that divides an image into several regions or segments to simplify its representation and facilitate analysis.

Imagine you want to automatically detect objects in images (forest fires in aerial photos, floods in satellite images, defects in photos of manufactured parts). To do this, segmentation algorithms divide the image into different groups of pixels called segments. Each segment corresponds to a part of the image with common characteristics, such as color, texture or intensity. This makes it possible to automatically identify and delimit objects in the image, facilitating image analysis.

Classical segmentation methods are widely used in certain contexts, but they have their limits.

On the one hand, traditional state-of-the-art methods, offering the best performance in terms of quality and accuracy of results, are based on supervised learning models. As a result, they are very costly to implement, due to the need for manually labeled data and the high cost in human and material resources required to train them before they can be deployed and used. On the other hand, existing classical unsupervised methods for segmenting images do away with the problems of labeled data and training, at the cost of the quality of the results. Indeed, the latter still struggle to deliver good performance, not least because of their sensitivity to noise and variations in brightness.

Q-SCAN uses an innovative quantum unsupervised segmentation method, offering an exceptional quality/cost compromise.

Q-SCAN is based on a graph problem that treats the spectral and spatial information contained in the image on an equal footing, thus improving the quality and robustness of the results obtained. This innovative formulation is ideally suited to quantum computation, enabling the problem to be solved efficiently without consuming excessive computing capacity. Q-SCAN therefore offers the best of both worlds: a low-cost, easy-to-implement, unsupervised solution with a quality comparable to that of state-of-the-art supervised methods. What's more, the architecture of this solution makes it versatile and flexible, enabling it to easily address a wide range of use cases without any reformulation effort.

What are the advantages of using Q-SCAN?

Thanks to an innovative formulation of the segmentation problem, ideally suited to quantum computing, our image segmentation solution overcomes the limitations of conventional unsupervised methods and achieves quality and accuracy comparable to the state-of-the-art in supervised methods, while remaining unsupervised. It thus offers advantages in terms of quality, ease of adoption, cost and carbon footprint, and flexibility.

Outstanding precision and quality

Thanks to this new approach to the problem and quantum computing capabilities, our solution has shown exceptional robustness against noise, clearly outperforming conventional unsupervised methods. What's more, it offers performances comparable to the best supervised methods, which are often much more costly and complex to deploy.

Simplified adoption

Supervised methods require intensive training on large databases annotated by experts, often unavailable for specific industrial cases. The quality of training data directly affects results and model generalization. Furthermore, training and optimization are time-consuming and require Machine Learning expertise, making their adoption complex. In contrast, Q-SCAN's unsupervised approach requires neither data nor a training phase, facilitating rapid and straightforward adoption.

Reduced environmental impact and costs

Supervised methods require intensive training on large databases annotated by experts, a very costly task in terms of human and material resources. On the other hand, Q-SCAN, being unsupervised, requires neither training nor massive computing capacity to be deployed, guaranteeing low costs and minimal ecological impact.

Versatile and flexible

Q-SCAN features a flexible architecture capable of adapting easily to specific customer needs and market constraints. Its formulation also makes it
versatile and applicable to a wide range of segmentation use cases, without requiring additional R&D or development efforts.

Examples of Q-SCAN use cases

Loss detection

In the event of a claim, an insurer wants to anticipate the number of claims in order to manage its human resources efficiently, ensure customer satisfaction and control costs.

Detail

Q-SCAN can be used to detect disasters, such as areas affected by flooding or fire. Floods are becoming increasingly violent and frequent climatic events, due to global warming. The losses they generate have doubled in recent decades, resulting in considerable costs for insurers. By way of example, the Ciaran storm of November 2023 generated several million euros in costs for some insurance companies in reimbursement costs alone! By mapping these affected areas and using the portfolio of insured customers, the company can estimate the number of customers impacted as early as possible, and thus deal with the flooding more effectively, thus reducing costs.

This makes it possible to :

Effectively manage human resources to allocate the right workforce at the right time
Guarantee customer satisfaction by minimizing claims processing times
Maintain control over costs while ensuring optimum service quality

In this way, the insurer can balance claims management with policyholder expectations.

Benefits: Q-SCAN is a low-cost solution that enables you to identify affected customers with a high degree of accuracy. What's more, it's a single solution for many types of disaster, such as floods and forest fires, without the need for additional development.

Medical imaging

A radiologist or researcher wants to analyze a number of medical images to detect tumors, skin lesions, and so on.

Q-SCAN can be used to analyze these medical images to monitor disease progression, prepare for surgery and pinpoint the precise location of a tumor or skin lesion.

Detail

This solution could make it possible to :

Monitor the patient's condition: Q-SCAN makes it possible to visualize the evolution of the disease over time, facilitating diagnosis and treatment.
Automate the detection process: thanks to advanced algorithms, the module can automate anomaly detection, guaranteeing high fidelity in the identification of tumors and lesions. This reduces the risk of human error and increases the efficiency of the analysis process.
Supporting medical decision-making: By providing fast, accurate analyses, Q-SCAN helps healthcare professionals make informed decisions about patient treatment and management.

Potential benefits: Q-SCAN is a cost-effective, easy-to-deploy solution. Because of these characteristics, it can be adopted by all healthcare players: research institutes, physicians, insurers, mutual insurance companies, etc.

Its ease of use would enable healthcare professionals to quickly integrate it into their daily practice, thus facilitating the analysis of medical images. Thanks to Q-SCAN, all the players mentioned above could improve the quality of their research, diagnostics or predictive tools, without having to invest large sums of money.

Quality control

A plant wants to automate the quality control process to reduce the costs associated with the allocation of experts and the time required for this task.

Detail

In this context, Q-SCAN could be used to quickly and efficiently detect anomalies and breaks in industrial parts.

The use of Q-SCAN for quality control could enable :

Early detection of anomalies: thanks to its performance, Q-SCAN can identify manufacturing defects at an early stage, in near-real time, thus minimizing the risk of producing defective parts.
Increased efficiency: by automating the quality control process, Q-SCAN could reduce the time spent on manual inspection. This would enable employees to concentrate on other essential tasks, increasing overall plant productivity.
Cost reduction: by reducing the need for human intervention and speeding up the inspection process, Q-SCAN
could help reduce the operational costs associated with quality management.
Improved product quality: by guaranteeing rigorous and constant quality control, Q-SCAN could help factories maintain high standards, boost customer satisfaction and enhance the company's reputation.

Potential benefits: by integrating Q-SCAN into their quality control process, factories could not only optimize their costs, but also improve product reliability while responding more effectively to market requirements.

Security and defense

The defense industry needs rapid image analysis to detect dangers that could threaten national security.

Detail

In this context, Q-SCAN can offer an effective solution thanks to its speed and the quality of its segmentation.

The use of Q-SCAN for image analysis in defense enables :

Real-time analysis: Thanks to Q-SCAN's processing speed, our module can be adapted to perform real-time analysis of images from multiple acquisition devices. This could enable operators to react quickly to critical situations.
Segmentation accuracy: high segmentation quality enables objects of interest and potential threats to be clearly distinguished in images, which is crucial for accurate risk assessment.
Versatility of acquisition devices: whether for images from drones, satellites or surveillance cameras, Q-SCAN is, in principle, capable of integrating and analyzing data from a variety of devices. This guarantees extensive coverage and constant vigilance.
Decision-making support: by providing rapid, accurate analyses, Q-SCAN could help decision-makers to make informed decisions about national security, thereby strengthening the ability to respond to threats.

Potential benefits: by integrating
Q-SCAN into their image analysis systems, defense players could significantly improve their ability to detect and react to hazards, while optimizing operational efficiency.