Posted in | News | Quantum Physics

Study Suggests Quantum Theory is Near-Optimal in Terms of Predictive Power

Due to non-availability of complete information, majority of the predictions made often go wrong, such as in the case of predicting weather condition.

Associate professor Wolfgang Tittel and his colleagues have published a paper that suggests nature is next to impossible to predict. (photo by Riley Brandt)

However in quantum mechanics, it is not possible to perfectly predict the results of certain experiments in advance even if all the related information is available.

This inability to precisely predict the outcomes of quantum physics experiments has been a long-standing debate that discusses whether quantum mechanics is the optimal solution to predict results.

In a paper, ‘An experimental bound on the maximum predictive power of physical theories,’ published in the Physics Review Letters journal, a team of scientists from the Institute for Quantum Information Science of the University of Calgary, the Eidgenössische Technische Hochschule (ETH) and the Perimeter Institute have suggested that quantum theory is near-optimal with respect to its predictive power. Their study explores measurements on members of optimally entangled photon pairs that are directed into Stern-Gerlach-type apparatus where one out of two possible paths can be taken by each photon.

Dr. Wolfgang Tittel, GDC/AITFIndustrial Research Chair in Quantum Cryptography and Communicationat and Associate Professor at University of Calgary, informed that the study demonstrates that any theory in which minimal unpredictability exists is fated to fail. Quantum theory is a platform that describes how predictable the universe is. Unpredictability in quantum theory is one of its major aspects and is broadly acknowledged. Its appeal is its basic characteristic and a variety of implications. Understanding the accurate pattern of the universe at the big bang may not be adequate to envisage its complete evolution, for instance, in contradiction to classical theory.

Will Soutter

Written by

Will Soutter

Will has a B.Sc. in Chemistry from the University of Durham, and a M.Sc. in Green Chemistry from the University of York. Naturally, Will is our resident Chemistry expert but, a love of science and the internet makes Will the all-rounder of the team. In his spare time Will likes to play the drums, cook and brew cider.

Citations

Please use one of the following formats to cite this article in your essay, paper or report:

  • APA

    Soutter, Will. (2019, February 18). Study Suggests Quantum Theory is Near-Optimal in Terms of Predictive Power. AZoQuantum. Retrieved on November 22, 2024 from https://www.azoquantum.com/News.aspx?newsID=67.

  • MLA

    Soutter, Will. "Study Suggests Quantum Theory is Near-Optimal in Terms of Predictive Power". AZoQuantum. 22 November 2024. <https://www.azoquantum.com/News.aspx?newsID=67>.

  • Chicago

    Soutter, Will. "Study Suggests Quantum Theory is Near-Optimal in Terms of Predictive Power". AZoQuantum. https://www.azoquantum.com/News.aspx?newsID=67. (accessed November 22, 2024).

  • Harvard

    Soutter, Will. 2019. Study Suggests Quantum Theory is Near-Optimal in Terms of Predictive Power. AZoQuantum, viewed 22 November 2024, https://www.azoquantum.com/News.aspx?newsID=67.

Tell Us What You Think

Do you have a review, update or anything you would like to add to this news story?

Leave your feedback
Your comment type
Submit

While we only use edited and approved content for Azthena answers, it may on occasions provide incorrect responses. Please confirm any data provided with the related suppliers or authors. We do not provide medical advice, if you search for medical information you must always consult a medical professional before acting on any information provided.

Your questions, but not your email details will be shared with OpenAI and retained for 30 days in accordance with their privacy principles.

Please do not ask questions that use sensitive or confidential information.

Read the full Terms & Conditions.