Core Viewpoint - The introduction of new pear varieties, particularly "Ningli Early Dew," showcases advancements in breeding techniques aimed at enhancing taste, appearance, and cultivation efficiency in the pear industry [1][2]. Group 1: New Pear Varieties - "Ningli Early Dew" is a new pear variety that matures in late June, which is half a month earlier than traditional early-ripening pears, with a growth period of approximately 90 days from flowering to maturity [1][2]. - The variety has a fruit weight of 280-320 grams and features a small core, providing a sweet and juicy taste experience [1]. - Other new varieties presented include "Ning Early Gold," "Ning Late Green," and a red-skinned pear series, all developed by the Nanjing Agricultural University pear innovation team [1][2]. Group 2: Breeding Techniques - The breeding process for new pear varieties traditionally takes 12 to 15 years; however, the research team has implemented image recognition and machine learning technologies to accelerate this process [2]. - The development of the "Cloud Shang Hou Ji" breeding information platform has standardized data collection and improved the efficiency of new pear variety creation [2]. - The combination of hybrid breeding, bud mutation, and molecular marker selection has significantly enhanced the speed and effectiveness of new variety development [2]. Group 3: Market Impact - The newly introduced varieties cover a range of maturity periods from extremely early to mid-late, ensuring a continuous supply of fresh pears in Jiangsu from late June to early September [4].

Group 1 - The core viewpoint of the articles highlights the significant advancement in quantum artificial intelligence (AI) through the application of boson sampling for image recognition, marking a crucial step towards practical quantum AI applications in real-world scenarios [1][2]. - The research team from Okinawa Institute of Science and Technology successfully utilized a quantum AI system for image classification using only three photons and a linear optical network, demonstrating the potential for low-energy, hybrid quantum methods [1][2]. - The developed quantum AI architecture compresses grayscale image data into single-photon quantum states, which are then processed through a complex optical network to create high-dimensional patterns, achieving superior accuracy compared to traditional machine learning methods [2]. Group 2 - The principle of boson sampling is explained through an analogy of a "marble board" game, illustrating how photons, unlike marbles, exhibit wave-like behavior that leads to complex interference patterns that are difficult to predict even for supercomputers [2]. - The experimental results indicate that the quantum AI system outperformed traditional machine learning approaches across all tested image datasets, showcasing its effectiveness in image recognition tasks [2].