Skip to main content

Get Started

Want to learn more about Quantum Machine Learning? You've come to the right place. This guide will help you understand the basics of QML, the current state of quantum computing, and how you can get started with QML. No prior knowledge of quantum mechanics is required, but a little familiarity with machine learning concepts will be helpful.

What exactly is quantum machine learning?​

What exactly is Quantum Machine Learning (QML)? Imagine taking the power of your favorite AI/ML models – their ability to learn, adapt, and solve complex problems – and amplifying them a thousandfold. That's the essence of Quantum Machine Learning (QML).

But instead of relying on the traditional bits and bytes of classical computers, QML harnesses the bizarre and powerful laws of quantum mechanics. Think qubits instead of bits, superposition instead of binary states, and entanglement – a spooky connection between qubits that allows them to share information instantly, no matter the distance.

By leveraging these quantum superpowers, QML can tackle problems that would bring even the mightiest supercomputers to their knees. It can:

  • Unravel the mysteries of complex molecules: Imagine designing new drugs or materials with unprecedented accuracy and speed.
  • Optimize financial markets: Uncover hidden patterns and make predictions that would leave Wall Street quants gasping.
  • Crack unbreakable codes: Develop next-generation cryptography that's immune to even the most sophisticated hacking attempts.

...and much more.

What are we lagging behind in quantum computing?​

You might think that even though you have read several blogs and news articles about the ongoing development of quantum computers by companies like Google and IBM, we haven't yet seen any real-world applications. Why is that?

The answer lies in two key areas: quantum hardware and software. On the hardware side, the main challenges are quantum error correction and quantum noise. These issues create instability in the qubits, which limits the computations they can perform effectively. As for software, the primary hurdles are controlled entanglement and efficient quantum gates. While we have theoretical solutions for these problems, creating practical implementations that work reliably on large-scale quantum computers remains a significant challenge.