This fourth retrospective looks at some examples of how the technology has been incorporated into machine learning and natural language processing solutions.
Quantum-Enhanced Machine Learning
The complex routines used to train machine learning algorithms involving vast quantities of data can often be executed faster using a quantum computer, a process known as quantum machine learning (QML).
The financial services industry is often promoted as one of the earliest sectors to benefit from QML, and an important use case is fraud detection. In December, an
explained how quantum computers can select the features that best train the machine learning model.
Research by Multiverse Computing and technology transfer center Ikerlan demonstrated that
quantum artificial vision systems
powered by quantum machine learning can outperform their
Hyundai Motor Company is using QML techniques on IonQ’s quantum computer to develop machine vision algorithms capable of conducting
from autonomous vehicles. They are also investigating quantum to simulate electrochemical reactions of different metal catalysts to develop more efficient and environmentally friendly electric vehicles.
Standard Chartered bank and the Universities Space Research Association are developing quantum-inspired machine learning applications for environmental, social and governance applications, including
predicting extreme weather events
And Bosch and Multiverse are researching the use of quantum and quantum-inspired machine learning tools for
, including quantum-computing powered digital twin technology.
Quantum Natural Language Processing
Natural language processing is a potential use case for quantum computing as it is a complex optimization process, an application known as quantum natural language processing (QNLP).
JPMorgan Chase researchers have successfully demonstrated using quantum algorithms to
using natural language processing. Banks often need to provide summaries of documents for their customers that might otherwise be too long to read. They automate this using a process called extractive summarization, which uses NLP to extract sentences that represent the most relevant information from the original text.
Finally and perhaps most surprisingly, the British Broadcasting Corporation (BBC) is joining a new quantum consortium formed to find ways to use QNLP to support tasks such as
content discovery and archive retrieval.
To give an idea of the scale of the task, the BBC has an archive with more than 15 million items, including audio, film and text documents, as well as toys, games, merchandise, artifacts and historic equipment, representing a century of historical and cultural records.
One of the companies aiming to make QNLP more accessible is Quantinuum, which this year launched an update to its
QNLP software development toolkit. The new feature aims to make QNLP applications such as advanced automated dialogue, text mining, language translation, text-to-speech and language generation easier for developers to create.
Read more about:
Enter Quantum Newsletter
To get the latest quantum computing news, advice and insight, sign up to our newsletter