Dive into the collaborative efforts of IBM's Quantum Working Groups as they aim to bring quantum computing closer to real-world applications. These groups focus on healthcare, materials science, high-energy physics, optimization, and sustainability, seeking to leverage quantum algorithms for tangible benefits.

Algorithm discovery is the bridge from quantum utility to quantum advantage. IBM's Quantum Working Groups bring together subject matter experts and quantum researchers to explore this frontier. Over the past two years, IBM has launched five working groups in healthcare, materials science, high-energy physics, optimization, and sustainability, all aimed at developing quantum algorithms for specific, impactful use cases.

These groups regularly convene to tackle problems, identify potential quantum benefits, and release white papers that guide future research collaborations. From simulating particle collisions in high-energy physics to optimizing sustainable energy solutions, these efforts highlight quantum computing's promise to revolutionize diverse fields.

Quantum working groups are essential for realizing quantum advantage. They ensure that quantum hardware can outperform classical computers and that quantum circuits are the optimal solution for specific problems. This collaborative approach, combining quantum and domain expertise, is key to unlocking the full potential of quantum computing.

Quantum Working Groups Push for Near-Term Use Cases

Explore the groundbreaking Prov-GigaPath, a whole-slide pathology foundation model revolutionizing digital pathology with state-of-the-art performance. Trained on over a billion image tiles, this model is set to transform cancer diagnostics and more.

Digital pathology is entering a new era with Prov-GigaPath, a whole-slide pathology foundation model pretrained on a massive dataset from Providence health network. Imagine analyzing gigapixel slides with tens of thousands of image tiles, all seamlessly integrated to capture both local and global patterns. Prov-GigaPath leverages the power of the novel GigaPath vision transformer architecture, adapted from the cutting-edge LongNet method, to achieve this feat.

With an impressive training set comprising 1.3 billion image tiles from 171,189 whole slides, this model is designed to handle the vast complexity of real-world pathology data. From cancer subtyping to prognostic predictions, Prov-GigaPath outperforms existing models across 25 out of 26 tasks, thanks to its innovative approach that combines large-scale pretraining with ultra-large-context modelling.

Not just a technical marvel, Prov-GigaPath also highlights the importance of community collaboration in advancing computational pathology. By making this model open-weight, researchers worldwide can tap into its capabilities, fostering further innovations in healthcare and life sciences. Whether it's simulating the dynamics of high-energy physics experiments or revolutionizing cancer diagnostics, Prov-GigaPath stands at the forefront of a quantum leap in digital pathology.

Healthcare Technology View All Blogs

Quantum Working Groups Push for Near-Term Use Cases

A Whole-Slide Foundation Model for Digital Pathology from Real-World Data

Healthcare TechnologyView All Blogs

Quantum Working Groups Push for Near-Term Use Cases

A Whole-Slide Foundation Model for Digital Pathology from Real-World Data

Healthcare Technology View All Blogs