“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
This article provides a comprehensive overview of QML algorithms and explores their complexities. It explores the characteristics of quantum data, hybrid quantum-classical models, variational quantum algorithms, quantum-enhanced reinforcement learning, and the difficulties associated with quantum machine learning. Overall, this article provides a valuable resource for researchers and practitioners interested in understanding the algorithms, complexities, and potential of Quantum Machine Learning, shedding light on its current state and future prospects.
Quantum machine learning, also known as QML, is a blooming field of modern artificial intelligence that integrates quantum computing with machine learning. It aims to enhance traditional machine learning algorithms and develop novel computational methods.
This article examines the inner workings of quantum machine learning and related topics. It includes the fundamentals of quantum computing, quantum machine learning algorithms, the characteristics of quantum data, hybrid quantum-classical models, variational quantum algorithms, quantum-enhanced reinforcement learning, and the difficulties associated with quantum machine learning.
“The fusion of quantum computing and artificial intelligence, paving the way for groundbreaking innovation and endless opportunities.” – Sri Amit Ray
Read More »Quantum Machine Learning: Algorithms and Complexities
In this article, we discussed the 10 properties and characteristics of hybrid classical-quantum machine learning approaches for our Compassionate AI Lab projects. Quantum computers with the power of machine learning will disrupt every industry. They will change the way we live in this world and the way we fight diseases, care for old people and blind people, invent new medicines and new materials, and solve health, climate and social issues. Similar to the 10 V’s of big data we have identified 10 M’s of quantum machine learning (QML). These 10 properties of quantum machine learning can be argued, debated and fine tuned for further refinements.
Hybrid Classical Quantum Machine Learning
The compassionate AI lab is currently developing a hybrid classical-quantum machine learning (HQML) framework – a quantum computing virtual plugin to build a bridge between the available quantum computing facilities with the classical machine learning software like Tensor flow, Scikit-learn, Keras, XGBoost, LightGBM, and cuDNN.
Presently the hybrid classical-quantum machine learning (HQML) framework includes the quantum learning algorithms like: Quantum Neural Networks, Quantum Boltzmann Machine, Quantum Principal Component Analysis, Quantum k-means algorithm, Quantum k-medians algorithm, Quantum Bayesian Networks and Quantum Support Vector Machines.
Here, Dr. Amit Ray discusses the five key benefits of quantum machine learning.
Quantum machine learning is evolving very fast and gaining enormous momentum due to its huge potential. Quantum machine learning is the key technology for future compassionate artificial intelligence. In our Compassionate AI Lab, we have conducted several experiments on quantum machine learning in the areas of drug-discovery, combating antibiotic resistance bacteria, and multi-omics data integration.
We have realized that in the area of drug design and multi-omics data integration, the power of deep learning is very much restricted in classical computer. Hence, with limited facilities, we have conducted many hybrid classical-quantum machine learning algorithm testing at our Compassionate AI Lab.
Five Benefits of Quantum Machine Learning
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
Here we discussed the advantages and limitations of seven key qubit technologies for designing efficient quantum computing systems. The seven qubit types are: Superconducting qubits , Quantum dots qubits , Trapped Ion Qubits , Photonic qubits , Defect-based qubits , Topological Qubits , and Nuclear Magnetic Resonance (NMR) . They are the seven pathways for designing effective quantum computing systems. Each one of them have their own limitations and advantages. We have also discussed the hierarchies of qubit types. Earlier, we have discussed the seven key requirements for designing efficient quantum computers. However, long coherence time and high scalability of the qubits are the two core requirements for implementing effective quantum computing systems.
Quantum computing is the key technology for future artificial intelligence. In our Compassionate AI Lab, we are using AI based quantum computing algorithms for human emotion analysis, simulating human homeostasis with quantum reinforcement learning and other quantum compassionate AI projects. This review tutorial is for the researchers, volunteers and students of the Compassionate AI Lab for understanding the deeper aspects of quantum computing qubit technologies for implementing compassionate artificial intelligence projects. We followed a scalable layered hybrid computing architecture of CPU, GPU, TPU and QPU, with virtual quantum plugin interfaces.
Earlier we have discussed Spin-orbit Coupling Qubits for Quantum Computing and AI, Quantum Computing Algorithms for Artificial Intelligence, Quantum Computing and Artificial Intelligence , Quantum Computing with Many World Interpretation Scopes and Challenges and Quantum Computer with Superconductivity at Room Temperature. Here, we will focus on the primary qubit technologies for developing efficient quantum computers.
Building a quantum computer differs greatly from building a classical computer. The core of quantum computing is qubits. Unlike classical bits, qubits can occupy both the 0 and 1 states simultaneously and can also be entangled with, and thus closely influenced by, one another. Qubits are made using single photons, trapped ions, and atoms in high finesse cavities.
Superconducting materials, semiconductor quantum dots are promising hosts for qubits to build scalable quantum processor. However, other qubit technologies have their own advantages and limitations. The details of the seven primary qubit systems are as below:
Read More »7 Primary Qubit Technologies for Quantum Computing
Here, we discussed seven key requirements for implementing efficient quantum computing systems. The seven key requirements are long coherence time, high scalability, high fault tolerance, ability to initialize qubits, universal quantum gates, efficient qubit state measurement capability, and faithful transmission of flying qubits. They are seven guidelines for designing effective quantum computing systems.
Quantum computing is the key technology for future artificial intelligence. In our Compassionate AI Lab, we are using AI based quantum computing algorithms for human emotion analysis, simulating human homeostasis with quantum reinforcement learning and other quantum compassionate AI projects. This tutorial is for the researchers, volunteers and students of the Compassionate AI Lab for understanding the deeper aspects of quantum computing for implementing compassionate artificial intelligence projects.
Earlier we have discussed Spin-orbit Coupling Qubits for Quantum Computing and AI, Quantum Computing Algorithms for Artificial Intelligence, Quantum Computing and Artificial Intelligence and Quantum Computer with Superconductivity at Room Temperature. Here, we will focus on the exact requirements for developing efficient quantum computers.
Building a quantum computer differs greatly from building a classical computer. The core of quantum computing is qubits. Qubits are made using single photons, trapped ions, and atoms in high finesse cavities. Superconducting materials and semiconductor quantum dots are promising hosts for qubits to build a quantum processor. When superconducting materials are cooled, they can carry a current with zero electrical resistance without losing any energy. These seven requirements refereed as DiVincenzo criteria for quantum computing [1].
Quantum computer with superconductivity at room temperature is going to change the landscape of artificial intelligence. In the earlier article we have discussed quantum computing algorithms for artificial intelligence. In this article we reviewed the implication of superconductivity at room temperature on quantum computation and its impact on artificial intelligence.
Long coherence time (synchronized), low error rate and high scalability are the three prime requirements for quantum computing. To overcome these problems, presently, quantum computer needs complex infrastructure involving high-cooling and ultra-high vacuum. This is to keep atomic movement close to zero and contain the entangled particles, both of which reduce the likelihood of decoherence. The availability of superconductivity at room temperature will provide the quantum jump in quantum computer.
Read More »Quantum Computer with Superconductivity at Room Temperature
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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