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.
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.
The name of this machine learning model comes from a combination of artist Salvador Dalí and Pixar’s beloved robot WALL·E. A truly creative name, if we dare say. DALL.E is a 12-billion parameter version of OpenAI’s GPT-3 specialising in image generation from text.
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.
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.
Google is no slouch when it comes to Quantum. Being the first company around a year ago to announce that it had achieved Quantum supremacy it has steadily been improving its offering with a range of tools that are firing on every cylinder. Some of those tools include the popular language cirq and the ever […]
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.
Being stuck in because of covid could a great time to brush up on the latest in machine and quantum machine learning. The summer school is hosted by Max Planck Institute for Intelligent Systems, Tübingen, Germany. You can find a summary of what talks are available virtually and online.
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 […]
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 […]