CQT researchers and their collaborator present a quantum speed-up for machine learning
One of the ways that computers ‘think’ is by analysing relationships within large sets of data. CQT’s Jansen (Zhikuan) Zhao, Anupam Prakash and their collaborator have shown that quantum computers can do one such analysis faster than classical computers, for a wider array of data types than was previously expected.
The team’s proposed ‘quantum linear system algorithm’ is published in the 2 February issue of Physical Review Letters. In the future, it could help crunch numbers on problems as varied as commodities pricing, social networks and chemical structures.
“The previous quantum algorithm of this kind applied to a very specific type of problem. We need an upgrade if we want to achieve a quantum speed up for other data,” says Jansen, who is corresponding author on the work.
That’s exactly what the team is offering. The CQT researchers began collaborating with Leonard Wossnig when he visited the Centre. He was then a Master’s Student at ETH Zurich. Jansen is a PhD student, and Anupam is a research fellow. Jansen’s PhD is with the Singapore University of Technology and Design.
The first quantum linear system algorithm was proposed in 2009 by a different group of researchers. That algorithm kick-started research into quantum forms of machine learning, or artificial intelligence.
A linear system algorithm works on a large matrix of data. For example, a trader might be trying to predict the future price of goods. The matrix may capture historical data about price movements over time and data about features that could be influencing these prices, such as currency exchange rates. The algorithm calculates how strongly each feature is correlated with another by ‘inverting’ the matrix. This information can then be used to extrapolate into the future.
“There is a lot of computation involved in analysing the matrix. When it gets beyond say 10,000 by 10,000 entries, it becomes hard for classical computers,” explains Jansen. This is because the number of computational steps goes up rapidly with the number of elements in the matrix: every doubling of the matrix size increases the length of the calculation eight-fold.
The 2009 algorithm could cope better with bigger matrices, but only if the data in them is what’s known as ‘sparse’. In these cases, there are limited relationships among the elements, which is often not true of real-world data.
Jansen, Anupam and Leonard present a new algorithm that is faster than both the classical and the previous quantum versions, without restrictions on the kind of data it works for.
As a rough guide, for a 10,000 square matrix, the classical algorithm would take on the order of a trillion computational steps, the first quantum algorithm some 10,000s of steps and the new quantum algorithm just 100s of steps. The algorithm relies on a technique known as quantum singular value estimation.
There have been a few proof-of-principle demonstrations of the earlier quantum linear system algorithm on small-scale quantum computers. Jansen and his colleagues hope to work with an experimental group to run a proof-of-principle demonstration of their algorithm, too. They also want to do a full analysis of the effort required to implement the algorithm, checking what overhead costs there may be.
To show a real quantum advantage over the classical algorithms will need bigger quantum computers. Jansen estimates that “We’re maybe looking at three to five years in the future when we can actually use the hardware built by the experimentalists to do meaningful quantum computation with application in artificial intelligence.”
Learn more: Quantum algorithm could help AI think faster
The Latest on: Machine learning
[google_news title=”” keyword=”machine learning” num_posts=”10″ blurb_length=”0″ show_thumb=”left”]
via Google News
The Latest on: Machine learning
- Saudi Arabia Photonic Sensor Market Power of Data Visualization Techniques for Communicating Research Findingson May 9, 2024 at 12:14 am
Download Free Sample of This Strategic Report with Industry Analysis @ https://reportocean.com/industry-verticals/sample-request?report_id=SA1361 The report covers ...
- A smart neckband for tracking dietary intakeon May 8, 2024 at 9:35 am
A smart neckband allows wearers to monitor their dietary intake. Automatically monitoring food and fluid intake can be useful when managing conditions including diabetes and obesity, or when ...
- Machine Learning Neckband Tracks Food and Hydration Intakeon May 8, 2024 at 6:27 am
Chi Hwan Lee and his colleagues developed a smart neckband that tracks eating and drinking habits. This machine-learning-powered device uses sensors to distinguish between these actions and similar ...
- New Grid-EYE – 90° from Panasonic increases field of vision for Machine Learning based IR sensingon May 8, 2024 at 6:25 am
Panasonic Industry has launched a new member of its popular Grid-EYE sensor family featuring a 90° lens delivering a wider field of view (FoV) and reducing the number of sensors required to cover a ...
- Machine Learning & Predictive Analytics: A Game-Changer In The Fight Against Climate Changeon May 5, 2024 at 8:30 am
The power of ML extends beyond monitoring. Predictive analytics can forecast future emissions based on historical data and current trends ...
- Use AI And Machine Learning To Capitalize On Smart Building Dataon May 3, 2024 at 4:30 am
The same data-driven machine learning principles that power GenAI, when applied to building IoT data, can help organizations optimize well-being and productivity.
- Nanotubes, nanoparticles and antibodies detect tiny amounts of fentanylon May 2, 2024 at 1:22 pm
A research team at the University of Pittsburgh led by Alexander Star, a chemistry professor in the Kenneth P. Dietrich School of Arts and Sciences, has developed a fentanyl sensor that is six orders ...
- Researchers use machine learning to create a fabric-based touch sensoron April 17, 2024 at 12:06 pm
A new study from NC State University combines three-dimensional embroidery techniques with machine learning to create a fabric-based sensor that can control electronic devices through touch.
- Thermal Camera Plus Machine Learning Reads Passwords Off Keyboard Keyson May 3, 2023 at 6:53 pm
Researchers at the University of Glasgow show how machine learning can pull details from ... and that small amount of heat can be spotted by a thermal sensor. We’ve seen this basic approach ...
via Bing News