• HOME
  • NEWS
  • EXPLORE
    • CAREER
      • Companies
      • Jobs
    • EVENTS
    • iGEM
      • News
      • Team
    • PHOTOS
    • VIDEO
    • WIKI
  • BLOG
  • COMMUNITY
    • FACEBOOK
    • INSTAGRAM
    • TWITTER
Tuesday, October 14, 2025
BIOENGINEER.ORG
No Result
View All Result
  • Login
  • HOME
  • NEWS
  • EXPLORE
    • CAREER
      • Companies
      • Jobs
        • Lecturer
        • PhD Studentship
        • Postdoc
        • Research Assistant
    • EVENTS
    • iGEM
      • News
      • Team
    • PHOTOS
    • VIDEO
    • WIKI
  • BLOG
  • COMMUNITY
    • FACEBOOK
    • INSTAGRAM
    • TWITTER
  • HOME
  • NEWS
  • EXPLORE
    • CAREER
      • Companies
      • Jobs
        • Lecturer
        • PhD Studentship
        • Postdoc
        • Research Assistant
    • EVENTS
    • iGEM
      • News
      • Team
    • PHOTOS
    • VIDEO
    • WIKI
  • BLOG
  • COMMUNITY
    • FACEBOOK
    • INSTAGRAM
    • TWITTER
No Result
View All Result
Bioengineer.org
No Result
View All Result
Home NEWS Science News

Mapping the edge of reality

Bioengineer by Bioengineer
April 28, 2017
in Science News
Reading Time: 2 mins read
0
Share on FacebookShare on TwitterShare on LinkedinShare on RedditShare on Telegram

Australian and German researchers have collaborated to develop a genetic algorithm to confirm the rejection of classical notions of causality.

Dr Alberto Peruzzo from RMIT University in Melbourne said: "Bell's theorem excludes classical concepts of causality and is now a cornerstone of modern physics.

"But despite the fundamental importance of this theorem, only recently was the first 'loophole-free' experiment reported which convincingly verified that we must reject classical notions of causality.

"Given the importance of this data, an international collaboration between Australian and German institutions has developed a new method of analysis to robustly quantify such conclusions."

The team's approach was to use genetic programming, a powerful machine learning technique, to automatically find the closest classical models for the data.

Together, the team applied machine learning to find the closest classical explanations of experimental data, allowing them to map out many dimensions of the departure from classical that quantum correlations exhibit.

Dr Chris Ferrie, from the University of Technology Sydney, said: "We've light-heartedly called the region mapped out by the algorithm the 'edge of reality,' referring to the common terminology 'local realism' for a model of physics satisfying Einstein's relativity.

"The algorithm works by building causal models through simulated evolution imitating natural selection – genetic programming.

"The algorithm generates a population of 'fit' individual causal models which trade off closeness to quantum theory with the minimisation of causal influences between relativistically disconnected variables."

The team used photons, single particles of light, to generate the quantum correlations that cannot be explained using classical mechanics.

Quantum photonics has enabled a wide range of new technologies from quantum computation to quantum key distribution.

The photons were prepared in various states possessing quantum entanglement, the phenomenon which fuels many of the advantages in quantum technology. The data collected was then used by the genetic algorithm to find a model that best matches the observed correlations.

These models then quantify the region of models which are ruled out by nature itself.

###

The team includes theoretical physicists and computer scientists from the ARC Centre for Engineered Quantum Systems (EQuS) at the University of Sydney, the Centre for Quantum Software and Information at the University of Technology Sydney and the Institute for Theoretical Physics at the University of Cologne as well as the experimental group at RMIT University's Quantum Photonics Laboratory.

The research, "Explaining quantum correlations through evolution of causal models", has been published in Physical Review A and can be accessed online.

The DOI is https://doi.org/10.1103/PhysRevA.95.042120

For interviews: Dr Chris Ferrie, [email protected], or Dr Alberto Peruzzo, [email protected] or +61 410 790 860.

For general media enquiries: David Glanz, +61 3 9925 2807 or +61 438 547 723 or [email protected].

Media Contact

Dr. Chris Ferrie
[email protected]
@RMIT

http://www.rmit.edu.au

############

Story Source: Materials provided by Scienmag

Share12Tweet7Share2ShareShareShare1

Related Posts

Magnetoelastic Sensor Reveals Fatigue Levels Accurately

Magnetoelastic Sensor Reveals Fatigue Levels Accurately

October 14, 2025

Exploring Non-canonical Thioesterases in Peptide Biosynthesis

October 14, 2025

Gabriella Miller Kids First Data Resource Center Launches Variant Workbench

October 14, 2025

Enhancing Clinicians’ Views on Urinary Continence Care

October 14, 2025
Please login to join discussion

POPULAR NEWS

  • Sperm MicroRNAs: Crucial Mediators of Paternal Exercise Capacity Transmission

    1238 shares
    Share 494 Tweet 309
  • New Study Reveals the Science Behind Exercise and Weight Loss

    104 shares
    Share 42 Tweet 26
  • New Study Indicates Children’s Risk of Long COVID Could Double Following a Second Infection – The Lancet Infectious Diseases

    101 shares
    Share 40 Tweet 25
  • Revolutionizing Optimization: Deep Learning for Complex Systems

    92 shares
    Share 37 Tweet 23

About

We bring you the latest biotechnology news from best research centers and universities around the world. Check our website.

Follow us

Recent News

Magnetoelastic Sensor Reveals Fatigue Levels Accurately

Exploring Non-canonical Thioesterases in Peptide Biosynthesis

Gabriella Miller Kids First Data Resource Center Launches Variant Workbench

Subscribe to Blog via Email

Enter your email address to subscribe to this blog and receive notifications of new posts by email.

Join 65 other subscribers
  • Contact Us

Bioengineer.org © Copyright 2023 All Rights Reserved.

Welcome Back!

Login to your account below

Forgotten Password?

Retrieve your password

Please enter your username or email address to reset your password.

Log In
No Result
View All Result
  • Homepages
    • Home Page 1
    • Home Page 2
  • News
  • National
  • Business
  • Health
  • Lifestyle
  • Science

Bioengineer.org © Copyright 2023 All Rights Reserved.