Music is the language of the spirit. It opens the secret of life bringing peace, abolishing strife. -Kahlil Gibran
One of the most awaited and willful features was the interaction with machines in a natural form, similar to how human beings make conversations. Deep learning has been an efficacious field for Humanity in many ways. Specifically, the generative approach has been a turning point to the deep learning trend that unlocked several applications in Robotics, IoT, the Internet, telecommunication, and almost the software industry. The applications involved Natural language processing, Computer vision, speech to text and text to speech, Generating…
Shor’s Algorithm exposed/simplified/clarified with a quick overview.
Once you read this article, you’ll get a clear overview of how exactly Shor's algorithm works. I tried explaining it, without going into a much deeper section, because it will definitely confuse at the beginner stage.
RSA breaching isn’t only the application of Shor’s algorithm but there are several use-cases like quantum simulation, spin-off technology, Quantum cryptography, etc.
Introduction
Shor’s algorithm was developed by Peter Shor (American mathematician) in the year 1994. It performs integer factorization in polynomial time. The requirement of the algorithm is the quantum computer. The algorithm says that the quantum computer is capable of performing factorization on…
We gonna explore Quantum neural networks (QNN) in a much simplified manner, covering all the fundamentals concepts that will create a grasping impact. I’ll try making you understand with least mathematics to get a better overview as a beginner.
Introduction
Artificial Neural Networks (ANN) has been a generous algorithm on which the whole deep learning field is footing steady. ANN consists of the collection of neurons connected with each other. each neuron in the network can share information with each other using connections, which in terms of biology are called synapses of the biological neurons in the brain. …
Yes you, we are Itinerants, exploring quantum technology. Come along with me.
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In our last quantum ML post, we saw the fundamentals of Quantum machine learning and how can we get started with it. And surely we’ll be going to inspect each terminology in QML and hence here I’ll be explaining how exactly the embedding happens in quantum ML, and how it differs from the classical method.
Introduction
Quantum machine learning offers velocity with precision, and hence it is obviously to be the future of Artificial Intelligence. As we usually feed input to the neural network in embedded format…
Just beginning the Quantum programming? you’re at the right place. Shall we hang out with the qubits and get into superposition with them? Only if you don’t have any complications
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I’ll help you to make a quick start in quantum machine learning, so try reading it at least twice if you find it hard to read due to the mathematics used. I have tried to explain everything theoretically rather than much mathematical stuff.
Introduction
The need to locomote from classical to quantum computing is not just casual and hence accelerating the revolution of technology by unleashing the vision towards the quantum era. Blending Artificial Intelligence and Quantum computing can yield wonders. And that’s what in the coming generations is going to pop out. …
Let's go through the quantum era with the speed of light.
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Massive technologists like Google, Microsoft, IBM are trying to get the most out of quantum science. John Preskill has introduced the term quantum supremacy, which states the comparison between quantum and classical computing advantages in terms of speed and performance. And, it's also a need for us, to get into this, as fast as possible. So let's dig in without any further intro.
We would try to understand some fundaments of quantum computing in no time. I have tried making concepts clearer, rather than keeping them more complex. Please do hit claps only if you find it helpful…
In this blog, I’ll try to make you understand how exactly Grover’s search algorithm works and how it differs from the classical computing algorithms with much simplicity
Quantum computing
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When you change the way you look at things, the things you look at change. -Max Planck
I have tried simplifying the algorithm to max level, to make it understandable for a beginner too.
Introduction
Quantum computing has battered classical computers, in terms of their acceleration in solving complex problems. Quantum computers harness the phenomenon of quantum physics to store data and to perform computations. The basic unit of quantum memory is the quantum bit or qubit, similar to the bits 0’s and 1’s in classical computers. …
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Introduction
“Computer Vision”, a field of Artificial Intelligence that helps Machines to visualize this beautiful world. Computer vision has led to wonders in enhancing Artificial Intelligence. From pattern recognition to Human Pose estimation, And from Robot navigation to solid-state physics, computer vision has much more useful and helpful applications. Using computer vision and deep learning, we successfully give machines the ability to visualize and understand images, videos, etc. But revolution is fastened.
Earlier The Convolutions have given superlative contributions to computer vision and deep learning for medical research, business, technology, and many more. …
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Transformers have gained tremendous popularity since their creation due to their significant staging. They have ruled the NLP as well as the Computer vision. Transformers-based models have been the all-time dearest.
Overview
“Attention is all you need”. The paper describes the Transformer architecture where the encoder and decoder are stacked up. Both the architecture comprises normalization, feed-forward, and attention layers. The biggest advantage of transformers is their parallelizable nature. The Attention has played a crucial role in the transformers mechanism, which is responsible for its overall optimization
Parallelization replaces the seq2seq mechanism, which is feasible due to the positional…
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A quick tour of Variational Autoencoder (VAE)
In recent times the generative model has gained huge attention due to its state-of-art performance and hence achieved massive importance in the marketplace and is also used widely. Variational Autoencoders are deep learning techniques used to learn the latent representations they are one of the finest approaches to unsupervised learning. VAE shows exceptional results in generating various kinds of data.
Autoencoder (AE) at a glance
Autoencoder comprises an encoder, decoder, and a bottleneck. …
Amit Nikhade
Quantum Artificial Intelligence enthusiast | Robotics | IoT | Python | Julia | Meet me at amitnikhade.com
