In this blog post, we dive into the world of quantum computing applications and explore when expertise in the field is required, and when it isn't. Professor Robert Wille at the Technical University of Munich discusses the steps to realize quantum computing applications and how we can leverage classical computing expertise in the process.
Quantum computing is a rapidly evolving domain, with enormous progress in both hardware and software that has sparked significant interest in academia and industry. From physics and chemistry to finance and optimization, the potential applications of quantum computing are vast. However, utilizing quantum computing applications can be a challenging task that requires expertise in this new computing paradigm. But fear not, as we can still rely on classical computing expertise whenever possible.
Professor Robert Wille breaks down the process of realizing quantum computing applications into four main steps: problem specification, algorithm selection, problem solving, and solution processing. Problem specification and algorithm selection can often be addressed using classical computing expertise, while problem solving requires specialized quantum computing knowledge. This step involves encoding the problem instance as a quantum circuit, executing the resulting circuit on a simulator or an actual quantum computer, and decoding the output histogram to extract the desired result.
The challenge for end-users familiar with classical computing and seeking to utilize quantum computers lies in the paradigm shift required for problem solving with quantum computing applications. While this shift cannot be completely avoided, its ramifications can be reduced by supporting end-users with software and automation methods. As quantum computing continues to advance, bridging the gap between classical and quantum expertise will become increasingly important for unlocking the full potential of this groundbreaking technology.
