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Machine learning algorithm helps unravel the physics underlying quantum systems

Machine learning algorithm helps unravel the physics underlying quantum systems
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Researchers Develop Machine Learning Algorithm to Help Further Understand Quantum Systems

Zooming in on a wafer of D-Wave Quantum Computers
Bayers Factors
In the paper entitled
Learning models of quantum systems from experiments, published in Nature Physics, quantum mechanics from QET Labs of Bristol are describing an algorithm that overcomes such challenges by functioning as an independent agent, through the use of machine learning to oppose or contradict engineer Hamiltonian models.
Essentially, the research team developed a new protocol for the formulation and validation of approximate models for quantum systems of interest.
As indicated in their study, the researchers algorithm is working independently, designing and carrying out experiments on the aimed quantum system, with the resulting data being fed back into the algorithm. ....

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Machine Learning Algorithm Helps Unravel the Physics Underlying Quantum Systems

Machine Learning Algorithm Helps Unravel the Physics Underlying Quantum Systems
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Bristol Qetlabs , Sebastian Knauer , Anthony Laing , Brian Flynn , Andreas Gentile , University Quantum Engineering Technology Labs Qetlabs , University Of Vienna Faculty Physics , Qu Co , Professor In Bristol School Of Physics , University Of Bristol Qetlabs , Quantum Engineering Centre For Doctoral Training , Quantum Engineering Technology Labs , Nature Physics , Quantum Engineering Centre , Associate Professor , செபாஸ்டியன் நவர் , அந்தோணி முட்டையிடும் , பிரையன் ஃப்லிந் , கூ இணை , குவாண்டம் பொறியியல் தொழில்நுட்பம் ஆய்வகங்கள் , இயற்கை இயற்பியல் , இணை ப்ரொஃபெஸர் ,

Machine learning algorithm helps unravel physics underlying quantum systems

The nitrogen vacancy centre set-up, that was used for the first experimental demonstration of QMLA.
Some authors of the paper: Rafaelle Santagati (top left); Andreas Gentile (top right); Sebastian Knauer (bottom left); Brian Flynn (bottom right).
Scientists from the University’s Quantum Engineering Technology Labs (QETLabs) have developed an algorithm that provides valuable insights into the physics underlying quantum systems – paving the way for significant advances in quantum computation and sensing, and potentially turning a new page in scientific investigation.
In physics, systems of particles and their evolution are described by mathematical models, requiring the successful interplay of theoretical arguments and experimental verification. Even more complex is the description of systems of particles interacting with each other at the quantum mechanical level, which is often done using a Hamiltonian model. The process of formulating Hamiltonian models from obser ....

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Machine learning algorithm helps unravel the physics underlying quantum systems

Machine learning algorithm helps unravel the physics underlying quantum systems
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Bristol Qetlabs , Sebastian Knauer , Anthony Laing , Brian Flynn , Andreas Gentile , University Of Vienna Faculty Physics , Professor In Bristol School Of Physics , Qu Co , University Of Bristol Qetlabs , Quantum Engineering Centre For Doctoral Training , Quantum Engineering Technology Labs , Nature Physics , Quantum Engineering Centre , Associate Professor , செபாஸ்டியன் நவர் , அந்தோணி முட்டையிடும் , பிரையன் ஃப்லிந் , கூ இணை , குவாண்டம் பொறியியல் தொழில்நுட்பம் ஆய்வகங்கள் , இயற்கை இயற்பியல் , இணை ப்ரொஃபெஸர் ,