Torrent details for "Ross O. Artificial Intelligence and Quantum Computing...Innovati…" Log in to bookmark
Controls:
×
Report Torrent
Please select a reason for reporting this torrent:
Your report will be reviewed by our moderation team.
×
Report Information
Loading report information...
This torrent has been reported 0 times.
Report Summary:
| User | Reason | Date |
|---|
Failed to load report information.
×
Success
Your report has been submitted successfully.
Checked by:
Category:
Language:
None
Total Size:
40.3 MB
Info Hash:
8D0E82DC643BA4AF8DA0B4D0F0546DFA342BD920
Added By:
Added:
Sept. 30, 2025, 10:02 a.m.
Stats:
|
(Last updated: Sept. 30, 2025, 10:08 a.m.)
| File | Size |
|---|---|
| Ross O. Artificial Intelligence and Quantum Computing...Innovations Vol 1. 2025.pdf | 40.3 MB |
Name
DL
Uploader
Size
S/L
Added
-
40.3 MB
[25
/
10]
2025-09-30
| Uploaded by andryold1 | Size 40.3 MB | Health [ 25 /10 ] | Added 2025-09-30 |
-
5.3 GB
[14
/
12]
2025-10-01
| Uploaded by IGGGAMESCOM | Size 5.3 GB | Health [ 14 /12 ] | Added 2025-10-01 |
-
309.0 MB
[10
/
1]
2023-10-29
| Uploaded by Morgaretor | Size 309.0 MB | Health [ 10 /1 ] | Added 2023-10-29 |
NOTE
SOURCE: Ross O. Artificial Intelligence and Quantum Computing...Innovations Vol 1. 2025
-----------------------------------------------------------------------------------
COVER
-----------------------------------------------------------------------------------
MEDIAINFO
Textbook in PDF format In this era of rapid technological advancement, the integration of artificial intelligence (AI) and quantum computing serves as a transformative force across various domains. Artificial Intelligence and Quantum Computing: Early Innovations emerges as a comprehensive guide and scholarly resource that aligns with the United Nations Sustainable Development Goals (SDGs). These goals, which aim to ensure peace and prosperity for both people and the planet, frame the innovative research showcased in this volume—research that promises to elevate the quality of life, foster sustainable practices, and enhance decision-making processes in complex environments. This book delves into how these transformative technologies are being integrated to revolutionize fields such as health care, infrastructure, and data security. In an era dominated by rapid advancements in technology, this book stands at the forefront of the intersection between AI and quantum computing. Quantum Machine Learning in supervised tasks represents a promising frontier that lies in the intersection of classical Machine Learning and quantum computing, enabling the handling of both classical and quantum data. Despite still being in an early stage of development and with QPUs lacking sufficient fault-tolerant qubits, significant advances in hybrid algorithms and techniques suggest a future where this emerging technology could address complex problems more efficiently than traditional classic methods. Further research and interdisciplinary collaboration are essential to address current challenges and unlock the full potential of this field, where quantum principles are integrated into classical Machine Learning algorithms to enhance performance. For instance, quantum Kernel-based algorithms are applied in supervised learning tasks and parametrized quantum circuits are utilized for classification and regression tasks. Approximation optimizers and quantum heuristics are examples of algorithms within the quantum optimization field. Quantum data processing, on the other hand, involves encoding classical data into quantum states and applying dimensionality reduction techniques. Despite promising results in QML, technological advancements in building better QPUs with higher-quality resources and setting aside physical constraints quantum hardware development, there are well-defined problems that shape the field. Arbitrary classical data encoding, optimization of the training process and, notably, the lack of clarity on which types of real-world classical data benefit from QML over conventional classical algorithms. Key Highlights: -· Quantum Architectures: Explore revolutionary quantum architecture search methods that leverage quantum evolutionary algorithms, setting the stage for next-generation computing power and efficiency. This section delves into the mechanisms that could significantly enhance quantum device functionality and scalability. -· Quantum-Enhanced Machine Learning: Discover how supervised quantum machine learning is redefining problem-solving in various industries by enabling faster, more efficient data processing and opening new frontiers in artificial intelligence. -· Agricultural Innovations: Delve into the application of AI in agriculture with a focus on improving crop yield predictions and optimizing resource management, showcasing how technology is transforming traditional farming practices. -· Health Care: Examine the transformative impact of AI and quantum computing in health care—from improving diagnostic accuracy with advanced imaging techniques to optimizing treatment protocols and patient management systems. -· Software Reliability: Explore the vital role of machine learning algorithms in enhancing software reliability, focusing on predictive techniques that identify potential faults and improve the quality of software development processes. This book is an essential resource for researchers, engineers, and policymakers, providing both theoretical foundations and practical applications of AI and quantum computing. It is designed to inspire innovation and encourage the adoption of these technologies in solving some of the most pressing challenges of our time. This book is more than just a technical guide—it is a blueprint for the future, demonstrating the profound impact of these technologies on global health, sustainable infrastructure, and beyond. Dive into this pivotal work to understand how Artificial Intelligence and Quantum Computing are not only shaping our world but also paving the way for a brighter, more sustainable future. Preface SDG 3 Good Health and Well-Being Hybrid Quantum Convolutional Network for Spine Fracture Classification RSNA 2024: Multi-view Quantum Deep Learning Model for Degenerative Lumbar Spine Classification Enhancing Nocturnal Blood Pressure Detection Through Interval Type-2 Fuzzy Classification Systems Glioma Segmentation in T1-Weighted MRI Images Using Deep Learning Vision Transformers for the Segmentation of Patterns Associated to Pathology in Diabetic Retinopathy: A Literature Review and Clinical Relevance in Eye Fundus Images Segmentation of High Quality Histopathological Micrographs Using a Genetic Algorithm and Otsu’s Method SDG 9 Industry, Innovation, Technology and Infrastructure AI-Based Algorithm for the Automatic Detection of Missing Solder Balls in Encapsulated Semiconductors Membrane Pseudo-Bacterial Potential Field with GPU Acceleration for Mobile Robot Path Planning Error Correction in Quantum Computing Supervised Quantum Machine Learning Hybrid Quantum Genetic Algorithm with a Strongly Entanglement Recombination Process for Maximizing the Coverage Area of a Set of Unmanned Aerial Vehicles in Natural Disaster Locations Review on Quantum Optimization of Type-1, Type-2 and Type-3 Fuzzy Systems Review on Quantum Optimization of Neural Networks and Fuzzy Systems Finance in the Quantum Computing Era: A Survey from 2019 to 2024 From Classical Challenges to Quantum Solutions: Grover’s Algorithm for the N-Queens Problem Oscar Montiel, Ulises Orozco-Rosas, Daniel López, and Moisés Sánchez Cross-Platform Analysis of Quantum Circuit Simulators with State Collapse Mechanisms and Back-Action Tracking in C and Python Performance Dynamics of BB84 QKD Protocol in Noisy Quantum Channels: A Simulation Study Benchmarking ML, DL, and Transformer Models for Document Classification Artificial Intelligence in Astrophysics: Analytical Enhancements Towards a Fully Quantum Learning System: Quantum Architecture Search with Quantum Evolutionary Algorithms Roach Infestation Optimization Algorithm with Enhanced Performance Using Automatic Parameter Adaptation Based on Fuzzy Systems From Containers to the Cloud, Scaling Event-Driven Evolutionary Algorithms Comparative Evaluation of Machine Learning Algorithms for Predicting Defects Using Object-Oriented and Software Complexity Metrics Application of IMU Sensors and AI for Infrastructure Monitoring SDG 11 Sustainable Cities and Communities Analysis of Geographic Coordinates Initialization Methods for Unmanned Aerial Vehicles Localization SDG 15 Life on Land An Intelligent System Design for Automated Quality Control of Agricultural Produce Based on Computer Vision
×