“Quantum Cheshire Cat Generative AI Model” is a book written by Sri Amit Ray is a groundbreaking exploration into the realm of Quantum Machine Learning, introducing a novel model that integrates the principles of Quantum Cheshire Cat phenomenon and Quantum Generative Adversarial Networks (QGANs). This book also introduced the concepts of Quantum Mirage Data in the field of machine learning for the first time.
Quantum Cheshire Cat Generative AI Model
How many qubits are needed to outperform conventional computers? How to protect a quantum computer from the effects of decoherence? And how to design more than 1,000 qubits fault-tolerant large-scale quantum computers? These are the three basic questions we want to deal in this article.
Qubit technologies, qubit quality, qubit count, qubit connectivity and qubit architectures are the five key areas of quantum computing. In this article, we explain the practical issues of designing large-scale quantum computers.
Read More »Roadmap for 1000 Qubits Fault-tolerant Quantum Computers
Probably you know the concept of many world interpretation of quantum mechanics. In this article, we will explain how this concept can be used in quantum computing.
Many scientist believe that Many World Interpretation (MWI) of quantum mechanics is self-evidently absurd for quantum computing. However, recently, there are many groups of scientist increasingly believing that MWI has the real future in quantum computing, because MWI can provide true quantum parallelism. Here, I briefly discuss the scopes and challenges of MWI for future quantum computing for exploration into the deeper aspects of qubits and quantum computing with MWI.
This tutorial is for the researchers, volunteers and students of the Compassionate AI Lab for understanding the deeper aspects of quantum computing for implementing large-scale compassionate artificial intelligence projects.
Read More »Quantum Computing with Many World Interpretation Scopes and Challenges
Dr. Amit Ray explains the quantum annealing, Quantum Monte Carlo Tree Search, Quantum algorithms for traveling salesman problems, and Quantum algorithms for gradient descent problems in depth.
This tutorial is for the researchers, developers, students and the volunteers of the quantum computing team of the Sri Amit Ray Compassionate AI Lab. Many of our researchers and students asked me to explain the quantum computing algorithms in a very simplistic term. The purpose of this article is to explain that.
Earlier we have discussed Spin-orbit Coupling Qubits for Quantum Computing and the foundations of Quantum computing and artificial intelligence. This article is to explain the foundation quantum computing algorithms in depth in a simplistic way. Here we explained the concepts of quantum annealing, Quantum Monte Carlo Tree Search, quantum algorithms for traveling salesman problem and Quantum algorithms for gradient descent problems. Read More »Quantum Computing Algorithms for Artificial Intelligence
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