Imagine yourself immersed in a 6-week virtual program designed to bring you up to speed on Quantum Computing, a practical program resulting in a Certificate of Completion, an item assured to smooth your entry into this game-changing technology now emerging from the lab and steadily marching toward Main Street.6 weeks. 20 Learners. Meet, Learn and Build Together.
Introduction to Quantum Computers (Current Trends)
Quantum Computing platforms
IBM Quantum Cloud account creation
IBM QISKit Demo
Mathematics for Quantum Computing
Physics behind Quantum Computing
Quantum Computing Algorithms
Deutch-Jozsa, Bernstein-Vazirani, Simons, Quantum Phase Estimation, Shor, Grover, Quantum Counting & Quantum Key Distribution
Once the first 3 weeks of foundations are over, you can choose a path/specialisation and you get to work on the live projects!
Mathematical Physics, Intro to Natural Qubits, Trapped Ion, Spin QUbits, Intro to Artificial Atoms & Superconductivity, Basics of QuantumIntegratedCircuits, Qubits & SC Qubits
Introduction to Cryptography (Symmetric, Asymmetric, Hash functions, DS, KM, etc)
Intro to Quantum Safe Cryptography
Introduction to Quantum Key Distribution
Post Quantum Cryptography
QRNGs, QI, Quantum Error Correction
Current Implementation, Research Projects, Products in the market, Security Services through Quantum.
Machine Learning Vs QML, Applications of Classical ML, Applications of QML, Approaches for Classical ML & for QML.
Software platform for QML (IBM QISKit, Xanadu Pennylane, DWave Ocean.
Quantum Computation for QML, Variational Circuits, QISKit Demo, Pennylane Demo.
Classical-Quantum Hybrid Learning Algorithms, Encoding Classical information, Variational methods for unsupervised learning, Kernel Methods, Probalistic Graphic Methods.
Quantum Machine Learning Models, Quantum Phase Estimation, Quantum Matrix Inversion, Gaussian Process.
Quantum Machine Learning Models, QKNN, QSVM, Quantum NN, Quantum Convolutional NN, Quanvolutional NN
Quantum Computing Foundations
AiQyam, being a first ever quantum hardware focused community in India. It aims at being a venture which produces scalable quantum hardware products. As all the journey's have a begining, so did AiQyam had. Abeer, Archit and Nilay were part of Quantum Computing India over the past one year. These three are the hardware geeks, who were the minds behind AiQyam. In Quantum Computing India, we have a vertical dedicated to Quantum Hardware. With the uprising in the international quantum community with introductions to various scalable quantum hardware architectures, we discussed that the entire world focuses majorly on Development of softwares in regards to quantum computing. We to go all hardware, as that's what we all three liked. The first step as AiQyam, was setting up Quantum Hardware Learning Circle. Where we recruited only 7 out of 29 entries we recieved. With IBM Qiskit Metal, being an open source tool and also Qiskit Pulse. In our learning circle we work as two verticals, one focuses on using IBM Qiskit Metal, for designing Quantum Chip and the one focuses on optimising the pulse schedules for better interaction with the qubits.We further will bring in more interaction with the universities and hold fellowship program. Further to involve the entire public, we'll be coming up with hackathons and workshops focusing on quantum hardware
I am working as Assistant Professor, Division of Computer Science & Engineering at Cochin University of Science and Technology and Research Scholar at IIIT Kottayam working towards developing Quantum-Safe Cryptographic scheme. I previously, worked as SAP Techno-Functional at Accenture Services Pvt. Ltd. My research areas include Quantum-Safe: Post-Quantum Cryptography, Network Security, Ethical Hacking, Digital Forensics and Delay Tolerant Networks. I was also associated with Cyber Cell, Kerala, Alappuzha as project Intern. I have given multiple talks on Quantum Safe Cryptography, Digital awareness and hands on sessions on Ethical Hacking for students, faculty and industry professionals.
QCI QML Team members are working building libraries for bridging various Machine Learning models with available QML frameworks, Quantum NLP, Application QNLP for Machine Reading Comprehension, generalizing Feature Map problems, Quantum Circuits to increase the accuracy of classic Machine Learning models.
The proceeds go proportionately to everyone involved in curating and making this fellowship possible. This is our first step in making sure the community becomes self-sustainable in the long run.