Day 30 — Quantum30 Challenge 2.0 Interested in joining the next cohort? The link is provided at the end of this post. Once again, I’ve reached another milestone by taking up the #Quantum30 Challenge presented by QuantumComputingIndia. …

Day 29 — Quantum30 Challenge Data Loading: In the realm of hybrid classical-quantum (CQ) machine learning, one crucial challenge is the transformation of classical data into a quantum state. Traditional methods for achieving this include Quantum Encoding, Quantum Circuits, and Oracles for Data Querying. …

Day 28 — Quantum30 Challenge Wow! This week, I learnt a lot of new concepts that were pretty interesting. Linking down all the articles of the week QNN with Qiskit — The fundamental modules Day 22 — Quantum30 Challenge 2.0medium.com QNN with PennyLane — Overview of the steps Day 23 — Quantum30 Challenge 2.0medium.com Painting with Qubits on the Quantum Canvas Day 24 — Quantum30 Challenge 2.0medium.com

Day 27 — Quantum30 Challenge 2.0 Note: Do check out the link for the github page in the ‘Reference’ Section of the article for a better understanding and implementation. Introduction Hybrid Computing and AWS Hybrid computing in quantum computing harnesses both classical computing and quantum properties. It involves classical preprocessing tasks such as data processing…

Day 26 — Quantum30 Challenge 2.0 Transfer learning in the context of neural networks is a powerful technique that can be likened to leveraging knowledge and skills acquired from one domain to excel in a related but distinct domain. …

Day 25 — Quantum30 Challenge 2.0 Quantum Image Processing (QIP) is an emerging field that offers promising solutions for various image processing tasks. One critical aspect of image processing is Edge Detection, which plays a crucial role in identifying object boundaries within images. Quantum Edge Detection (QED) is a cutting-edge…

Day 24 — Quantum30 Challenge 2.0 In the realm of computers and image processing, one of the fundamental aspects is image representation. Traditionally, in classical computing, digital images are encoded using matrices, where each value in the matrix corresponds to the pixel’s intensity or color value. …

Day 23 — Quantum30 Challenge 2.0 Today, let’s again look at quantum neural networks, but this time, using PennyLane and TensorFlow. The following article is an overview of everything mentioned in the QNN chapter from PennyLane. Learning to learn with quantum neural networks | PennyLane Demos Use a classical recurrent neural network to initilize the parameters of a variational quatum algorithm.pennylane.ai

Day 22 — Quantum30 Challenge 2.0 Classical Neural Networks draw inspiration from the human brain and can be trained to recognize patterns. Quantum Neural Networks (QNNs), on the other hand, amalgamate quantum computing with classical machine learning, forming an intersection between these two realms. To delve deeper into the world…