QML or Quantum Machine Learning is a hot topic amongst those at the leading edge of technology. Many companies such as Xanadu, IBM, Honeywell and Cambridge Quantum are busy exploring and exploiting the field. Frank Zickert PhD is an IT professional who has taught himself Quantum Machine Learning over the last few years. In his book: Hands-On Quantum Machine Learning With Python, Frank distils his learning into the field, both Quantum and QML with the aim of taking away the difficulties of learning about these two nascent fields. We wanted to know more about the motivation behind the book and about Frank. We also have a brief review of the book.
There is no question that machine learning and AI have taken the world by storm, with myriad products and services exploiting the ability of computers to learn from data without providing explicit rules. Whether self-driving cars from the likes of Tesla or extracting labels from images or even playing Atari video games, the field has created a great deal of excitement for its impact on almost every aspect of our lives. It should come as no surprise then, that researchers and developers are keen to find new and novel ways to exploit quantum computing to achieve learning. Here we take a first look at the new book by Frank Zickert which aims to be a hands-on guide to learning all about QML or Quantum Machine Learning.
Just about every person is associated with the financial industry in their lifetime. Finance originated during the start of our civilization and since then it has been a substantial part of our lives. Finance deals with uncertainty and risks as the actual behaviour of the asset or security may differ from the expected return. To lower the risk we must analyze all the factors associated with it. This involves a calculation of infinite possibilities of different combinations of factors which gives minimum risk and maximum profit. These problems in finance can be expressed as optimization problems. These are the tasks that are particularly hard for classical computers as they may take millions of years.
To process classical data we often rely on RAM (Random Access Memory), simply providing an address yields at contents of that address, making for rapid access of data. In this study the team from Yale and Chicago show that bucket-brigade QRAM architecture possesses a remarkable resilience to noise.
We all know QML stands for Quantum Machine Learning and is the new cool kid town. QML presents one of the best use-cases of early quantum computing. This announcement of Qiskit’s summer school focusing on QML will go hand in hand with the latest version of Qiskit which has a new emphasis on QML in version 0.25. The previous Qiskit summer school was a resounding success and little wonder as summer 2021 rolls around Qiskit Sumer School will be again prove immensely popular. The Qiskit Global Summer School is back from July 12-23 2021!
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.
One of the exciting areas lighting up the world of quantum computing is that of Quantum Machine Learning. With the massive interest in classical machine learning which has affected all areas of our lives and threatens even more disruption such as self driving cars and automation beyond imagination, is it no wonder that researchers look to utilise the inherent power of Quantum Computing to drive innovation in Reasoning. The announcement from CQC (Cambridge Quantum Computing) highlights how quantum machine can learn to infer hidden information from very general probabilistic reasoning models.
The enormously popular machine learning frame work: TensorFlow that was adapted for the Quantum domain reaches its first birthday mile stone. The framework from Google/Alphabet has made machine learning and deep learning possible at scale and the software that is created from TF (TensorFlow) gets wrapped up into numerous projects and tools. Roughly one year ago back in March 2020 (how the world was so different), TensorFlow got a Quantum flavour.
One of the early use cases of Quantum Computing is Quantum Machine Learning, which is not surprising given the massive interest in classical Machine Learning. Now a new collaboration between Cambridge Quantum Computing (CQC), JSR Life Sciences and CrownBio are aiming to use the latest techniques in QML (Quantum Machine Learning) to help find biomarkers which could be used in novel cancer treatments.