Nanotechnology Now

Our NanoNews Digest Sponsors
Heifer International

Wikipedia Affiliate Button

Home > Press > Quantum algorithm could help AI think faster: Researchers in Singapore, Switzerland and the UK present a quantum speed-up for machine learning

Abstract:
One of the ways that computers 'think' is by analysing relationships within large sets of data. An international team has shown that quantum computers can do one such analysis faster than classical computers, for a wider array of data types than was previously expected.

Quantum algorithm could help AI think faster: Researchers in Singapore, Switzerland and the UK present a quantum speed-up for machine learning

Singapore | Posted on February 2nd, 2018

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 Zhikuan Zhao, corresponding author on the work.

That's exactly what he's offering, in joint work with colleague Anupam Prakash at the Centre for Quantum Technologies, National University of Singapore, and collaborator Leonard Wossnig, then at ETH Zurich and the University of Oxford. Zhao is a PhD student 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 Zhao. 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.

Zhao, Prakash and Wossnig 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. Zhao 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. Zhao 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."

###

Acknowledgments:

The authors acknowledge support from Singapore's Ministry of Education and National Research Foundation. This material is based on research funded in part by the Singapore National Research Foundation under NRF Grants No. NRF-NRFF2013-01 and No. NRF-NRFF2013-13.

####

For more information, please click here

Contacts:
Jenny Hogan


Researcher Contact:

Zhikuan Zhao
PhD Student, Singapore University of Technology and Design
Centre for Quantum Technologies, National University of Singapore

Copyright © Centre for Quantum Technologies, National University of Singapore

If you have a comment, please Contact us.

Issuers of news releases, not 7th Wave, Inc. or Nanotechnology Now, are solely responsible for the accuracy of the content.

Bookmark:
Delicious Digg Newsvine Google Yahoo Reddit Magnoliacom Furl Facebook

Related Links

Reference:

Related News Press

News and information

New method to reduce uranium concentration in contaminated water March 18th, 2019

Review of the recent advances of 2D nanomaterials in Lit-ion batteries March 15th, 2019

Converting biomass by applying mechanical force Nanoscientists discover new mechanism to cleave cellulose effectively and in an environmentally friendly way March 15th, 2019

Exotic “second sound” phenomenon observed in pencil lead: At relatively balmy temperatures, heat behaves like sound when moving through graphite, study reports March 15th, 2019

Possible Futures

New method to reduce uranium concentration in contaminated water March 18th, 2019

Review of the recent advances of 2D nanomaterials in Lit-ion batteries March 15th, 2019

Converting biomass by applying mechanical force Nanoscientists discover new mechanism to cleave cellulose effectively and in an environmentally friendly way March 15th, 2019

Exotic “second sound” phenomenon observed in pencil lead: At relatively balmy temperatures, heat behaves like sound when moving through graphite, study reports March 15th, 2019

Quantum Computing

Researchers reverse the flow of time on IBM's quantum computer March 14th, 2019

Researchers move closer to practical photonic quantum computing: New method fills critical need to measure large-scale quantum correlation of single photons February 28th, 2019

Media invited to open meeting on the future of quantum technology held at RIT Jan. 23-25: Leaders from NASA, NSF, NIST and Sandia National Laboratory to attend January 11th, 2019

Spintronics 'miracle material' put to the test: Physicists build devices using mineral perovskite January 11th, 2019

Discoveries

New method to reduce uranium concentration in contaminated water March 18th, 2019

Review of the recent advances of 2D nanomaterials in Lit-ion batteries March 15th, 2019

Converting biomass by applying mechanical force Nanoscientists discover new mechanism to cleave cellulose effectively and in an environmentally friendly way March 15th, 2019

Quantum sensing method measures minuscule magnetic fields: MIT researchers find a new way to make nanoscale measurements of fields in more than one dimension March 15th, 2019

Announcements

New method to reduce uranium concentration in contaminated water March 18th, 2019

Review of the recent advances of 2D nanomaterials in Lit-ion batteries March 15th, 2019

Converting biomass by applying mechanical force Nanoscientists discover new mechanism to cleave cellulose effectively and in an environmentally friendly way March 15th, 2019

Exotic “second sound” phenomenon observed in pencil lead: At relatively balmy temperatures, heat behaves like sound when moving through graphite, study reports March 15th, 2019

Interviews/Book Reviews/Essays/Reports/Podcasts/Journals/White papers

New method to reduce uranium concentration in contaminated water March 18th, 2019

Review of the recent advances of 2D nanomaterials in Lit-ion batteries March 15th, 2019

Converting biomass by applying mechanical force Nanoscientists discover new mechanism to cleave cellulose effectively and in an environmentally friendly way March 15th, 2019

Quantum sensing method measures minuscule magnetic fields: MIT researchers find a new way to make nanoscale measurements of fields in more than one dimension March 15th, 2019

Artificial Intelligence

CEA-Leti & Stanford Target Edge-AI Apps with Breakthrough Memory Cell: Paper at ISSCC 2019 Presents Proof-of-Concept Multi-Bit Chip That Overcomes NVM’s Read/Write, Latency and Integration Challenges February 20th, 2019

Using artificial intelligence to engineer materials' properties: New system of 'strain engineering' can change a material's optical, electrical, and thermal properties February 11th, 2019

CEA-Leti Extends 300mm Line and Adds Avenues for Developing Disruptive Technologies: Execution Relies on CEA-Leti’s Fully Implemented Technology With Module-Level Innovations & Devices and Their Architectures December 3rd, 2018

Cea-Leti and imec Launch Strategic Partnership to Develop AI and Quantum Computing November 23rd, 2018

NanoNews-Digest
The latest news from around the world, FREE



  Premium Products
NanoNews-Custom
Only the news you want to read!
 Learn More
NanoStrategies
Full-service, expert consulting
 Learn More











ASP
Nanotechnology Now Featured Books




NNN

The Hunger Project