D-Wave and Zapata AI have joined forces in a strategic partnership to harness the capabilities of quantum computing and generative AI. This collaboration aims to develop commercial applications for quantum-enabled machine learning, focusing on accelerating discoveries and solving complex optimization problems in various industries.
Researchers at MIT CSAIL and Project CETI have discovered a sperm whale 'alphabet' using machine learning technologies. The study, titled 'Contextual and Combinatorial Structure in Sperm Whale Vocalizations,' points to key breakthroughs in understanding cetacean communication by analyzing sperm whale codas in context.
Togal.ai is transforming the construction industry by automating the estimation process with advanced AI, reducing time and enhancing accuracy. This case study explores how Togal leverages machine learning to streamline construction workflows, making them more efficient and less error-prone.
Discover how machine learning is pivotal in overcoming the 'reality gap' in quantum devices, enhancing predictions and performance by addressing inherent variability through a physics-informed approach.
Exploring the unprecedented capabilities of quantum neural networks, this article delves into how quantum computing could renaissanceize machine learning, offering advantages over classical approaches through increased model capacity and trainability.
IBM researchers have demonstrated a quantum advantage in machine learning, revealing that quantum kernels can identify patterns in data sets that appear as random noise to classical computers, offering a new pathway for quantum machine learning.
A groundbreaking study by Los Alamos National Laboratory reveals that quantum entanglement significantly enhances the scalability of quantum machine learning, overcoming the previous assumption of needing exponentially large datasets for training quantum neural networks.