Understanding What Quantum Computers Can Do
We've spent a few posts understanding the the building blocks of quantum computers. Now, let's explore what these machines could actually do. Quantum algorithms are like instruction manuals that tell quantum computers how to solve specific problems.
Shor's Algorithm is a famous one (if you want to learn more about it from the creator himself, watch this video). It can break down large numbers into their prime factors much faster than regular computers. While this could affect current encryption methods, we need better quantum computers first to realise this.
Scientists are more excited about quantum chemistry algorithms right now. The Variational Quantum Eigensolver (VQE) helps study how molecules behave. Companies are already using small versions of these algorithms to study new materials and drugs.
Another promising tool is the Quantum Approximate Optimization Algorithm (QAOA). This helps solve complex scheduling and routing problems. Quantum Machine Learning is also gaining attention. These algorithms could speed up artificial intelligence tasks, but there's a catch. Getting data into quantum computers efficiently remains a major challenge that scientists are working to solve.
The field faces two main hurdles right now. First, researchers need to find problems where quantum computers truly work better than classical ones. Second, they need to design algorithms that can run on today's imperfect quantum computers.
As quantum computers improve, the focus has shifted from theory to practical use. Scientists are now working on algorithms that can handle real-world problems despite current hardware limitations. This practical approach, combined with better error correction, will determine which quantum algorithms become useful first.
Today's graphic features Ada Lovelace. Comment below if you know the history of her and algorithms!
𝘘𝘶𝘓𝘦𝘢𝘳𝘯𝘓𝘢𝘣𝘴 𝘪𝘴 𝘴𝘶𝘱𝘱𝘰𝘳𝘵𝘦𝘥 𝘣𝘺 𝘵𝘩𝘦 𝘌𝘐𝘛 𝘋𝘦𝘦𝘱 𝘛𝘦𝘤𝘩 𝘛𝘢𝘭𝘦𝘯𝘵 𝘐𝘯𝘪𝘵𝘪𝘢𝘵𝘪𝘷𝘦 𝘰𝘧 𝘵𝘩𝘦 𝘌𝘶𝘳𝘰𝘱𝘦𝘢𝘯 𝘐𝘯𝘴𝘵𝘪𝘵𝘶𝘵𝘦 𝘰𝘧 𝘐𝘯𝘯𝘰𝘷𝘢𝘵𝘪𝘰𝘯 𝘢𝘯𝘥 𝘛𝘦𝘤𝘩𝘯𝘰𝘭𝘰𝘨𝘺 (𝘌𝘐𝘛)
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