Algorithms | General Technology | Machine Learning | Quantum Machine Learning | Quantum Research

Quantum Machine Learning making it to Primetime with new Qiskit release 0.25

We covered the recent release of Qiskit 0.25 which is one of the most popular Quantum toolsets and languages. Supported by IBM, the framework has an established following, and therefore when there are major changes to the framework, you could say the zeitgeist changes. That major change amongst a few others is the inclusion of a dedicated Quantum Machine Learning Module.

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Algorithms | IBM Quantum Computing | Machine Learning | Quantum Machine Learning

Bold New Version of IBM’s Qiskit Quantum Development Kit released includes Quantum Machine Learning and New Nature Module

One of the most popular Quantum languages and toolsets, Qiskit, now gets a new upgrade to version 025 which includes some new modules such as Nature. The already popular package Aqua gets a replacement which is aimed, not just at Chemistry but Physics too. There are also enhancements to Quantum Machine Learning (QML) which see Qiskit releasing a Machine Learning Module. QML has been perhaps one of the biggest areas of interest from Quantum Computing in application to a number of fields, so no surprise that the Qiskit toolset now sports QML capability. In total there are now four additional modules: Qiskit Nature (which will replace Aqua), Finance, Optimization and Machine Learning.

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Machine Learning | Quantum Machine Learning

Google develops language model with more than one trillion parameters

For machine learning algorithms, parameters are the building blocks. They are an important part of the historical training data. In the language domain, sophistication generally correlates with a higher number of parameters, and this has been proven to be a reliable standard. OpenAI’s GPT-3 has 175 billion parameters, making it one of the largest language models ever trained. It can make primitive analogies, generate recipes, and even code at a basic level.

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Algorithms | Machine Learning | Quantum Machine Learning

Qiskit + PyTorch + Python = Quantum Machine Learning. Hybrid Quantum Machine Learning is getting easier.

For many in the classical Machine Learning community the question is when Quantum Computing gets pulled into the mix to create Hybrid Learning networks that consist of both classical and quantum components. As more and more researchers are looking at this lucrative area it should come as no surprise that it is possible to combine the classical and the quantum world to potentially exploit the best of both worlds.

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Artificial Intelligence | Machine Learning

Microsoft’s AI Business School

We like to surface new learning materials and we came across Microsoft’s very business focused AI/Machine Learning portal which helps understand the importance of using AI in a variety of sectors from Retail to education. Each pathway of learning is tailored to a specific theme. There are learning paths for Financial Services, Healthcare, Retail, Manufacturing, Government and Education.

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Machine Learning | Quantum Companies | Quantum Computing News | Quantum Platforms

Canada’s Quantum Computing Company D-wave launches Leap 2 for Quantum Applications

Unless you have been sleeping you will have likely heard of the Quantum Computing Company D-wave and their Quantum Annealing computer – which works a little bit differently to gate based Quantum Computers such as IBM and Google. One of the first to commercialise quantum computing technology D-wave have steadily been increasing their Qubit count […]

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Machine Learning | Quantum Machine Learning | Quantum Tutorial

Quantum Assisted Feed-Forward Neural Network For MNIST Image Classification

This post discusses the potential of using Quantum Variational Circuits as feature extractors and as additionally as classification layers in a classical neural network. There is implementation in qiskit with code such that the user can also run working code. It’s assumed that the reader will have a basic understanding of machine learning. Regarding requisite […]

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