Mastering the fundamental concepts of learning quantum computing or how to get quantum ready the right way

^{Figure 1: A glimpse into the future: a superconducting quantum computer, enabling breakthrough applications in energy, materials science, and quantum research.}

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In a time when technology is advancing faster than ever, quantum computing emerges as a potential major game-changer. It has the power to solve complicated problems much quicker than traditional computers, potentially transforming entire industries, pushing forward scientific research, and making our data much more secure. Quantum computing could transform how we simulate the way molecules interact or help make better investment choices in finance.

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To capitalize on the advancements in quantum computing technology, leading corporations are striving to become quantum ready. Being quantum ready means that an organization is well-prepared to fully harness the capabilities and benefits of quantum computing. For this very reason, organizations are working towards becoming quantum ready.  

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Being quantum ready means an organization is set up to take full advantage of the progress in quantum computing technology. This includes forming the right partnerships, ensuring early access to hardware and software, and developing the skills and competencies needed. Achieving quantum readiness is essential for enterprises, governments, and individuals to make the most of quantum computing's potential while managing its challenges and risks.

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Steering towards this future, it's evident that a focused effort on education is vital. However, the necessary skills and competencies extend well beyond simply using existing algorithms; it will require deep understanding of both the application area and quantum computing, in order to create the intellectual property for new and novel solutions utilizing this technology.

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Acknowledging the necessity for deep understanding and skill development in quantum computing leads us to question the best strategies for education, especially when the tools and solutions are likely to evolve before quantum computing begins to deliver tangible business value. After all, Quantum computing is a fast-paced field with frequent breakthroughs and advancements.

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Quantum computing frameworks like Qiskit, Cirq, or Forest are critical for developing and running quantum algorithms, but they frequently receive updates or major changes to reflect new discoveries or feedback from the quantum computing community. This constant evolution can challenge learners, who may struggle with applying their knowledge to new contexts if their understanding is too closely tied to a specific framework. Therefore, while learning to use these tools is an important step, the ultimate goal of education in quantum computing should be to equip individuals with the ability to innovate and devise novel solutions that can adapt to and capitalize on the rapidly changing landscape of the field.

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Adapting to change: The importance of underlying ideas in tech education

To explore this further, let’s cast our minds back to the switch from Microsoft Word 2003 to Word 2007 (for those of us who remember). Remember how significant that change felt? Much of that feeling came from the introduction of a new interface, altering the way we interacted with the software. It required us to adapt to an entirely new method of navigating and leveraging the software, shifting our routine workflows and challenging our preconceived notions of how to use a given tool most effectively. This transition highlights a common pitfall: becoming so entrenched in the specifics of how to use a tool that we lose sight of the broader perspective.

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This example is a reminder of the importance of staying flexible and open to change, focusing on the underlying principles that guide our use of technology, rather than getting too attached to one way of doing things. And it is an issue that secretly isn’t limited to everyday software; it also affects the technologies used by developers and researchers – though it sometimes is hard to admit, at least for me. Sometimes, it is just a necessity to dive into into the nitty-gritty of things that might not even be around in one of the next versions. When I was a student, I started out using a machine learning framework called Caffe and was used to specify neural network architectures with a text file. Since then, much has evolved, and now tools like PyTorch, TensorFlow, or Jax dominate the scene, while Caffe itself hasn't been updated in years (though its follow-up, Caffe 2, was incorporated into PyTorch).

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What both examples tell us is that it's really important for anyone to build a strong base in the core principles. And this is especially true for quantum computing. Understanding these basics helps us grasp the key ideas behind different tools and systems, even as they evolve. Focusing on these fundamental concepts means we can adapt more easily to new tools and methods, keeping us flexible and ready for whatever comes next in the fast-moving world of quantum computing.

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Take, for instance, the quest for quantum algorithms that will offer a clear advantage over classical computing. There's a lot of exploration happening around algorithms such as the Quantum Approximate Optimization Algorithm (QAOA). Researchers are experimenting with different versions of QAOA (see here, here or here), trying to find the best ways to solve complex optimization problems more efficiently than classical algorithms. This scenario exemplifies the fluid nature of quantum computing research: the first algorithm to provide a definitive advantage may still be unknown, and the field is full of investigation into various approaches and modifications. Understanding the core principles of quantum computing makes it easier to navigate this exploration, contributing to the development of groundbreaking solutions.

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Turning the learning of fundamental idea into practice

Building on the importance of underlying ideas in technology, it's clear that our approach to learning and skill development must prioritize these fundamental concepts, especially in the realm of quantum computing. Here, the distinction between conceptual knowledge and mere product or version-specific knowledge becomes crucial (see Fig. 2). In the fast-evolving world of quantum tech, where tools and frameworks can become outdated in a matter of months understanding the underlying ideas about qubits and entanglement or also specific algorithmic approaches such as the structure of the quantum approximate optimization algorithm and the way variational quantum circuits leverage ansätze is what will help us long-term.

^{Figure 2: Guiding curriculum development by an emphasis on foundational principles functions as a means towards developing competencies applicable in the long term.}

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In addition, interactivity plays a pivotal role in supporting this focus on concept independence. Small interactive applets can significantly lower the barriers often encountered when first approaching quantum computing. This way, learners can engage directly with the concepts themselves, free from the distractions and complexities that come with navigating unfamiliar tools or syntax. This approach not only makes the foundational ideas more approachable but also enhances understanding by allowing learners to experiment and see the immediate impact of applying theoretical principles.

However, code serves as an important tool for thought during the learning process, allowing to mold and articulate ideas. The challenge is to allow these kinds of experimentation without forcing the learner to think in the building blocks, classes, or primitives of a specific tool. But if we don't spend all our time on one specific framework and instead show how the same concept can be done in different frameworks (see Fig. 3 for an example on how this can look like), we can compare different ways of expressing the same idea. This method helps us understand and get a clearer view of the underlying principles.

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^{Figure 3: Parallel support for multiple frameworks with easy selection at every cod cell fosters deeper understanding of underlying ideas as comparison between them allows to abstract away from concrete representation of a solution.}

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Conclusion

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As we inch closer towards quantum advantage or quantum utility, it's super important to realize that learning about this stuff isn't just a fun thing to do — it's essential. Getting ready for the quantum future means more than just playing around with super cool tech (though it is really fun playing with it). It’s about getting what this huge leap forward in tech could mean for everything from our jobs to how we keep our data safe.

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Educating ourselves about quantum computing is a key element to being prepared. But there's a catch: it's not enough to just know how to use the tools of today, like the different quantum computing programs out there. What really matters, and will matter in the future, is understanding the underlying ideas and principles that quantum computing and its algorithms are built on.

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This is a bit different from other areas of software development, where sometimes just being skilled with the current tools and languages can get you pretty far (or when was the last time you thought about matrix multiplication when specifying a neural network?). In quantum computing however, with a lot of solutions still to be explored, it's like we need to master the underlying ideas, not just following recipes, i.e. replicating existing methods. This deep understanding is what will help us make the most of quantum computing and really push the boundaries of what's possible. And the best part, it is really fun to learn about and engage with quantum computing.

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IQM  Academy offers  you and your organization a comprehensive platform to explore and learn about  quantum computing, a crucial component in achieving quantum readiness. It  features interactive applets, supports various programming frameworks, and  emphasizes the fundamental principles of quantum computing. What's more? You  can begin your journey at no cost. Start with the basics of quantum computing  and advance to more complex subjects.

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