Sri Amit Ray Quantum AI Lab
Quantum Computing Projects
We focused on building strong quantum computing research for the betterment of the Humanity. Quantum machine learning is our key focus area. International collaboration with university, industry and governments are welcome.
Our focus remains on innovation to improve human conditions at each step we walk. We are always open to new ideas. Sri Amit Ray Quantum AI Lab research uses quantum theory to develop technologies that can bring betterment in human lives.
Here, we focus mostly on the applications and the algorithms for quantum machine learning, quantum deep learning, quantum neural networks and quantum deep reinforcement learning, quantum-classical interfaces for our human-centered projects.
Our research includes the emerging research fields of AI and quantum computing such as compassionate care-giving, compassionate health care, multi-omics data integration, precision medicine, Quantum Computing, quantum machine learning, quantum digital medicine, brain-computer interface, combating antibiotic resistant bacteria, Balance Control of Elderly People, computer aided drug design, AI to fight Antimicrobial Resistance etc. Compassionate AI motivates the systems to go out of their way to help the physical, mental or emotional pains of the humanity.
Compassionate artificial intelligence systems are increasingly required for looking after those unable to care for themselves, especially sick, physically challenged persons, children or elderly people. How AI and quantum computer can help the emotional, social, and spiritual needs of humanity; specially for the poor, patients and elderly people are the scope of our research work.
Our Key Quantum Computing Projects
Our research lab is fully compatible with the current development of research on Quantum Computing. We focused our research activities on Quantum Artificial Intelligence. There are three approaches to quantum computing: Gate-based Quantum Computing, Quantum Annealing (QA) and the Adiabatic quantum computation (AQC). Here, we focus mostly on quantum annealing implementation of the algorithms for quantum deep neural learning and quantum deep reinforcement learning for our human-centered projects.
The 10 Properties of Quantum Machine Learning
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.
The field of hybrid classical-quantum machine learning has been maturing rapidly with the availability of the prototype universal gate-model quantum processors of IBM, Google, and Rigetti as well as more sophisticated quantum annealers of D-Wave systems. Moreover, the availability of various high-performance computing (HPC) simulation of quantum circuits facilities improving the possibilities of exploring the power of QML in various application areas. Quantum hardware dedicated to machine learning are also becoming reality. They too can provide much faster processing power than a general-purpose quantum computer.
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.
The heart of quantum machine learning (QML) is the quantum mechanical properties like superposition, interference, tunneling, entanglement, measurement, coherence and many-body localization. Theoretically sub-atomic particles can be located in a potentially unlimited number of places at once, and to behave in a potentially unlimited number of different ways. Quantum computer promises to take advantage of a sub-atomic particle’s ability to be in a superposition of states to produce prodigiously increased power and speed of calculations.
Interference is the mechanism by which a hypothetical quantum computer would combine its multiple calculations into one answer. With the advent of the uncertainty principle, particles could no longer be said to have separate, well-defined positions and velocities, but only a quantum state, a combination of position and velocity.
Schrodinger equation, is the cornerstone of quantum mechanics in describing how quantum states evolve. Schrodinger equation also supports the idea of superposition – particles exist in a “probability cloud” sate. Max Born, interpret the wave as the probability. It is proportional to the square of the probability amplitude of the wave function. Given a wave function ψ, the probability density function p(x,y,z) for a measurement of the position at time t0 will be given by p(x,y,z) = |ψ(x,y,z,t0)|2. Born’s rule connects quantum theory to experiments and measurements.
Any attempt to measure or obtain knowledge of quantum superposition by the outside world, the wave-function will collapse. Measurement causes them to decoherence, effectively destroying the superposition and reducing it to a single location or state, and also destroying the ability of its individual states to interfere with each other. Decoherence, results in the collapse of the quantum wave function and the settling of a particle into its observed state under classical physics.
Quantum entanglement is the basis of the quantum parallelism. If you perform an operation on one element of the entangled pair, it will automatically impact the other one. Nonlocality occurs due to the phenomenon of entanglement, whereby particles that interact with each other become permanently correlated, or dependent on each other’s states and properties, to the extent that they effectively lose their individuality and in many ways behave as a single entity. Through cluster qubits and entanglement swapping techniques individual ions, the carriers of the qubits are deterministically entangled and separated.
Quantum Artificial Intelligence Modules
Primarily, there are two types of quantum processing: 1) Gate model universal quantum processing and 2) Quantum Annealing processing. Both of them have their own advantages and limitations. Quantum machine learning can be implemented on both of them. HHL algorithm, quantum Fourier transform algorithm, variational quantum eigensolvers, quantum minimization algorithm, quantum phase estimation, quantum walk and Grover’s search algorithms are the key technologies for quantum machine learning.
Machine learning (ML) is dynamic and does not require human intervention to make certain changes. Machine learning programs, in a sense, adjust themselves in response to the data they’re exposed to. The “learning” part of machine learning means that ML algorithms attempt to optimize some objective functions along a certain dimension. They usually try to minimize error or maximize the likelihood of their predictions being true.
In a simplistic way a machine learning is just solving multivariate optimization problems. Hence, theoretically quantum annealing and adiabatic quantum computing is most suitable for that. However, human intelligence is much more than that. We have classified quantum artificial intelligence in four groups: shallow quantum intelligence, deep quantum intelligence and quantum creative or abstract intelligence. Shallow quantum intelligence primarily focused on traditional machine learning techniques like: supervised learning, unsupervised learning and reinforcement learning. The deep quantum intelligence modules focused on multi-agent, multi-domain intelligence, explanation engine and symbolic intelligence. Our quantum abstract intelligence focuses on mimicking the out of the box thinking and creative aspects of human intelligence.
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.
How many qubits are needed to out-perform conventional computers, how to protect a quantum computer from the effects of decoherence and how to design more than 1000 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 are discussed.
Earlier we have discussed 7 Core Qubit Technologies for Quantum Computing, 7 Key Requirements for Quantum Computing. 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 practical issues related to designing large-scale quantum computers.
Instead of running on zeros and ones, quantum computers run on an infinite number of states between zero and one. Instead of performing one calculation before moving on to the next, quantum computers can manage multiple processes all simultaneously.
Unlike binary bits of information in ordinary computers, “qubits” consist of quantum particles that have some probability of being in each of two states, represent as |0⟩ and |1⟩, simultaneously. When qubits interact, their possible states become interdependent (entangled), each one’s chances of |0⟩ and |1⟩ hinging on those of the other. Moreover, quantum information does not have to be encoded into binary bits, it could also be encoded into continuous observables bits (qubits).
The speed requirements for various applications grows with the complexity of the problems and the speed advantage of quantum computers are enormous compare to classical computers. The key to quantum computation speed is that every additional qubit doubles the potential computing power of a quantum machine.
The objective of 1000 qubits fault-tolerant quantum computing is to compute accurately even when gates have a high probability of error each time they are used. Theoretically, accurate quantum computing is possible with error probabilities above 3% per gate, which is significantly high. The resources required for quantum computing depends on the error probabilities of the gates. It is possible to implement non-trivial quantum computations at error probabilities as high as 1% per gate.Read more ..
Quantum Computing with Many World Interpretation Scopes and Challenges
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.
The heart of Copenhagen interpretation based quantum computing is wave function collapse. However, the Copenhagen interpretation fails to specify precisely where and how the collapses occurs and is therefore an imprecise theory. Moreover, the exact collapse mechanism is not scientifically well defined. But the beauty of Copenhagen interpretation is that “it works” and it makes no unnecessary assumptions. According to the Copenhagen interpretation there is no “game” only the result is real – the physical reality is a result of the collapse of the wave function as a “local” manifestation of the non-local wave-function. However, wave function collapse is widely regarded as artificial and adhoc.
On the other hand, MWI removed the wave function collapse theory and focused on quantum parallelism thesis (QPT). It states that none of the quantum states vanishes at all, except to our perception. It says, in essence, let’s just do away with wave function collapse altogether. The entire universe (all the universes together) is described by a gigantic wave function that contains within it all possible realities. This wave function is known as “universal wave function”. The universe is a single reality. It hypothesized that, at the quantum level, whenever the universe is confronted with a choice of paths, reality splits into branches and both choices or paths happen simultaneously.
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.
7 Core Qubit Technologies for Quantum Computing
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:
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 .
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Quantum Computer with Superconductivity at Room Temperature
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.
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.
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 ..
Spin-orbit Coupling Qubits for Quantum Computing and AI
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.
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.
Quantum Computing and Artificial Intelligence
Here, Sri Amit Ray discusses the power, scope, and challenges of Quantum Computing and Artificial Intelligence in details.
In recent years there has been an explosion of interest in quantum computing and artificial intelligence. Quantum computers with artificial intelligence could revolutionize our society and bring many benefits. Big companies like IBM, Google, Microsoft and Intel are all currently racing to build useful quantum computer systems. They have also made tremendous progress in deep learning and machine intelligence.
Artificial intelligence (AI) is an area of science that emphasizes the development of intelligent systems that can work and behave like humans. Quantum computing is essentially using the amazing laws of quantum mechanics to enhance computing power. These two emergent technologies will likely have huge transforming impact on our society in the future. Quantum computing is finding a vital platform in providing speed-ups for machine learning problems, critical to big data analysis, blockchain and IoT.
The main purpose of this article is to explain some of the basic ideas how quantum computing in the context of the advancements of artificial intelligence; especially quantum deep machine learning algorithms, which can be used for designing compassionate artificial superintelligence.Read more ..