>
2025-09-17 -- Ernest Hancock interviews James Corbett (Corbett Report) MP3&4
Whistleblower EXPOSES How Israel Brainwashes American Christians!
Joe Rogan listens to "How to destroy America"
This "Printed" House Is Stronger Than You Think
Top Developers Increasingly Warn That AI Coding Produces Flaws And Risks
We finally integrated the tiny brains with computers and AI
Stylish Prefab Home Can Be 'Dropped' into Flooded Areas or Anywhere Housing is Needed
Energy Secretary Expects Fusion to Power the World in 8-15 Years
ORNL tackles control challenges of nuclear rocket engines
Tesla Megapack Keynote LIVE - TESLA is Making Transformers !!
Methylene chloride (CH2Cl?) and acetone (C?H?O) create a powerful paint remover...
Engineer Builds His Own X-Ray After Hospital Charges Him $69K
Researchers create 2D nanomaterials with up to nine metals for extreme conditions
Currently, all proposals for the quantum version of machine learning utilize the finite-dimensional substrate of discrete variables. Here we generalize quantum machine learning to the more complex, but still remarkably practical, infinite-dimensional systems. Researchers present the critical subroutines of quantum machine learning algorithms for an all-photonic continuous-variable quantum computer that achieve an exponential speedup compared to their equivalent classical counterparts. Finally, they also map out an experimental implementation which can be used as a blueprint for future photonic demonstrations.
One of the biggest advantages of having a quantum machine learning algorithm for continuous variables is that it can theoretically operate much faster than classical algorithms. Since many science and engineering models involve continuous variables, applying quantum machine learning to these problems could potentially have far-reaching applications.