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10 Quantum Machine Learning Properties By Amit Ray

Quantum Machine Learning The 10 Key Properties 

Quantum Machine Learning and the Deep Intelligence Frameworks – The 10 Key Properties 

Dr. Amit Ray, Compassionate AI Lab

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. 

10 Quantum Machine Learning Properties By Amit Ray

Classical Quantum Hybrid

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.

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Five Key Benefits of Quantum Machine Learning

Five Key Benefits of Quantum Machine Learning

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. 

Benefits of Quantum Machine Learning

Five Benefits of Quantum Machine Learning

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Roadmap for 1000 Qubits Fault-tolerant Quantum Computers

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. 

Roadmap for 1000 Qubits Fault-tolerant Quantum Computers

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Quantum Computing with Many World Interpretation Scopes and Challenges

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. 

Quantum Computing with Many World Interpretation

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. 

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Quantum Computing Algorithms for Artificial Intelligence

Quantum Computing Algorithms for Artificial Intelligence

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.

Quantum Computing Algorithms for AI By Amit Ray

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

Spin-orbit Coupling Qubits for Quantum Computing

Spin-orbit Coupling Qubits for Quantum Computing and AI

The Power of Spin-orbit Coupling Qubits for Quantum Computing

Here, Dr. Amit Ray discusses the power, scope, and challenges of Spin-orbit Coupling Qubits for Quantum Computing with Artificial Intelligence in details. Quantum computing for artificial intelligence is one of the key research project of Compassionate AI Lab. We summarize here some of the recent developments on qubits and spin–orbit coupling  for quantum computing. 

In digital computing, information is processed as ones and zeros, binary digits (or bits). The analogue to these in quantum computing are known as qubits. The qubits are implemented in nanoscale dimensions, such as spintronic, single-electron devices and ultra-cold gas of Bose-Einstein condensate state devices. Manipulation and measurement of the dynamics of the quantum states before decoherence are the primary characteristic of quantum computing. 

 

Quantum Computing with AI

Involving electron spin  in designing electronic devices with new functionalities, and achieving quantum computing with electron spins is among the most ambitious goals of  compassionate artificial superintelligence – AI 5.0.  Utilizing quantum effects like quantum superposition, entanglement, and quantum tunneling for computation is becoming an emerging research field of quantum computing based artificial intelligence. 

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