Quantum computing in machine learning

Quantum Computers for Machine Learning seems weird and tough. But it can be a remarkable innovation in optimizing the problems and changing the pattern of data security to the reduction of power

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Patrick Rebentrost came up with the notion that machine learning and quantum mechanics are fundamentally about manipulating …

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Quantum Machine Learning. We now know that quantum computers have the potential to boost the performance of machine learning systems, and may eventually power efforts in fields from drug discovery to fraud detection. We're doing foundational research in quantum ML to power tomorrow’s smart quantum algorithms.


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Frequently Asked Questions

How can quantum computing be useful for machine learning?

While machine learning algorithms are used to compute immense quantities of data, quantum machine learning utilizes qubits and quantum operations or specialized quantum systems to improve computational speed and data storage done by algorithms in a program.

Is quantum machine learning a good career?

The exact answer to your question depends on what you consider to be a good career. Quantum machine learning would be an interesting field to work in so if that’s important to you, check! It’s a new career path. It’s not even that, it’s a new idea for a career path.

What are the prerequisites to understanding quantum computing?

  • (Just) enough quantum mechanics to understand quantum computation.
  • (Just) enough theoretical computer science to understand quantum computation.
  • Quantum algorithms.
  • The Deutsch-Jozsa algorithm
  • Simon's algorithm
  • The prime factorization algorithm
  • Grover's search algorithm
  • More quantum mechanics (density matrices, quantum operatios)

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What are the uses of quantum computing?

What are the possible applications of quantum computing in the future?

  • Optimization: Many optimization problems are searching for a global minimal point solution. ...
  • Machine Learning / Big data: ML and deep learning researchers are seeking for efficient ways to train and test models using large data set. ...
  • Simulation: Simulation is a useful tool to anticipate possible errors and take action. ...

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