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Looking forward, the team aims to scale these models for larger, more complex datasets and explore further optimizations in quantum state encoding and quantum machine learning architecture design.
The Cornell-led project's paper, "Correlator Convolutional Neural Networks as an Interpretable Architecture for Image-like Quantum Matter Data," published June 23 in Nature Communications.The lead ...
For over a decade, researchers have considered boson sampling—a quantum computing protocol involving light particles—as a key ...
Approaches to quantum-enhanced machine learning. Quantum search In the mid-1990s, computer scientist Lov Grover showed that a future quantum computer can search an unsorted database – such as ...
The appeal of quantum machine learning lies in its potential to tackle problems that classical ML ... Honda Research Institute uses such data compression for image analysis using quantum computers.
Quantum mechanics, which is the study of the behavior of sub-atomic particles, provides a way to enhance the use of machine learning to resolve inherently complex problems around optimization ...
Tacchino and co have created an algorithm that takes a classical vector (like an image) as an input, combines it with a quantum weighting vector, and then produces a 0 or 1 output.
image: This diagram illustrates how the team reduces quantum circuit complexity in machine learning using three encoding methods—variational, genetic, and matrix product state algorithms.
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