What are the essential skills and tools for quantum computing beginners? (2024)

You actually don't need a solid grasp of the basics. You need a solid grasp of the limited subset of the basics that is actually applicable to QC. And you don't even need to know all that just to get started. There's knowledge you need early, but there's also knowledge you can acquire over time and be just fine.

I highly recommend brushing up through the MIT OCW lectures on Linear Algebra (course code: 18.06) delivered by Gilbert Strang. They are classic! There's also another course of lectures with code 18.065 that align more toward Matrix Methods in Data Science, Signal Processing, etc (for anybody wanting to poke into ML). As for quantum physics, I highly recommend... MIT OCW again! Lectures by Alan Adams and Barton Zwiebach on course 8.04 (Quantum Mechanics I, II, and III) are a classic! Happy Learning! 😊

For beginners in quantum computing, essential mathematics includes linear algebra for understanding quantum gates and qubit states, calculus for grasping quantum algorithms' mathematical foundations, and probability theory for comprehending quantum measurement outcomes. Basic physics knowledge encompasses quantum mechanics principles like superposition, entanglement, and quantum states' probabilistic nature. Familiarity with complex numbers and vector spaces is crucial for understanding quantum concepts and operations.

For beginners, it is advisable to have a foundational understanding of linear algebra, including vectors, matrices, and their basic operations (multiplication, transposition, inverse). This knowledge can be gained during the introductory journey into quantum computing. Similarly, familiarity with essential mathematical concepts such as probability (distributions, percentages) and complex numbers (representations, operations) is beneficial.In terms of physics, fundamental concepts of quantum mechanics, particularly superposition, entanglement, and wave functions, are important. These concepts can be easily reinforced through online resources, and many introductory quantum computing courses cover them comprehensively.

It fully depends on which level you want to engage in; if you're interested in being a quantum computer scientist I don't recommend starting without deep expertise and understanding of math and physics! But (when the field) is mature enough, engaging with less understanding of the basics, to develop applications on available well-established algorithms will be possible!

Knowledge of python and hopefully julia should be highly valuable skill to have. Depending who you ask and their preferences, qiskit or cirq are the tools to go. I like the openess of qiskit and the independent contributions. However, worth noting that soon these wont be needed. Eventually being knowledgable with just python or c++ or julia with new advanced compilation techniques will allow us to run a lot of quantum algorithms (even if they cannot do useful stuff) in a much easier fashion. So if you want to be a leader in the field, learn about classical and quantum compilation and transpilation.

Feel free to jump right in and get started. Finding the right tools can take time, because the existing ones are evolving while new ones are being introduced. Fortunately, most of the skills you will acquire should be transferable. It's like driving a car; you still know how to drive, so you just need to learn what features the new one has and how they work.

For now, python is important, again if you come from the data science field this will be easier than other fields. But you already have Matlab for instance and other languages. And I belive this will spread soon and have other programming languages.

Basically, you need to know Python as a programming language, as most of the quantum libraries are in Python. When we're talking about quantum computing libraries, Qiskit, Cirq and Q# are the most popular libraries. The New Qiskit version 1. x has more than 100 Qubits capable of doing lots of work. But I prefer to try all of those libraries because they have pros and cons accordingly.

Python; Q#, Qiskit. Learn these three and you be intermediate to expert level in Quantum Computing languages and frameworks.

In my experience, a good demystified understanding of Quantum Information Theory is key to understanding the basics of Quantum Computing. The math can be overwhelming if one doesn't understand how Quantum computing differs from classical approach, and a number of resources which approach concepts like entanglement in plain English with illustrative examples help clarify the subject before the math takes over.

In order to understand the intuition behind advanced quantum algorithms, it is important to visualise how quantum superposition and entanglement work. Superposition is the property which allows qubits to hold two values at the same time, while entanglement is the property which allows particles to be correlated. These are of fundamental importance in the quantum world as every advanced concept and algorithm uses them.

Quantum information is a new way to look at a topic, not just a way to increase the efficiency of the system! But that being said we should remember that the hardware required to deliver the promises of quantum information and "entanglement" are nowhere as close to delivering applications in the real world.

No sure what to add here, this is all part of the foundations required to understand how quantum computation works. In my response above I explained that soon enough this won't really be needed since it will all be taken care semi-automatically, at least for the vast number of practicioners. In the same way you dont need to know how interior point methods work when you solve linear programs on excel. So, unless people have inherent interest in quantum physics and computing by itself, eventually these notions will not be required by the vast majority of users.

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This is one of the phenomenon that intrigues me the most about Quantum Information Science (QIS). In quantum physics we have [superposition] and [entanglement] – this phenomenon applied to processing information, managed by quantum algorithms is computing on the level of nature itself. We are limited with classical bits -- with qubits, we have [information processed] in [many states] -- superpostioned . The time it would take a classical computer to factor [several states] of a new drug being developed with coherence and decoherance could be a function of 1,000 years vs 1 day as QC becomes fault-tolerent and reliable as a primary. This is a [natural] phenomenon like nature itself. Fascinating.

Do NOT start with Shor or Grover. Understand and play hands-on with the basic features, like generating a superposition smiley, or teleporting. Maybe start first with fiddling around with simple gates. Explore different explanations how teleporting works. There is the hard mathematical way and the graphically easy way. Also understand what does not work with quantum computing, and understand also how to find out if something can generate added value with quantum compared to non-quantum solutions.

Start by getting your hands dirty with Qiskit or the library you choose just to get more about how combining different circuits would work. For Quantum Algorithms, IBM Quantum Learning Platform has a very structured course titled "Fundamentals of quantum algorithms" that goes step by step through the content with both video and written explanations.

A good algorithm to start understanding the potential of quantum computing is the Deutsch-Jozsa. Despite its limited practical applicability, it will introduce the concept of oracle that would be useful for understanding the Grover algorithm and the cost functions used in QAOA for example. The paradigm shift may require more time to understand how to use the potential of quantum mechanics to solve problems in different ways. That is why it is essential to start with basic algorithms even if you are fluent in classical programming.

Quantum variational algorithms represent a promising approach, particularly suitable for near-term quantum devices known as Noisy Intermediate-Scale Quantum (NISQ) computers. Variational Quantum Eigensolver (VQE), designed to find the lowest energy eigenstates of a Hamiltonian, which is crucial in chemistry and material simulations. These algorithms leverage a hybrid quantum-classical framework, where a quantum computer is used to prepare states and measure observables, while a classical optimizer adjusts quantum circuit parameters to minimize a cost function. This synergy allows for the efficient utilization of current quantum hardware's capabilities, despite its limitations.

Similarly, in the mid term future you wont need to know about "quantum" algorithms the same way people dont know about "classical" algorithms when they run deep learning models now or when they run some solvers on excell or such. But if you want to start, well, there is not really a golden rule if you got to start with fault tolerant style or heuristic algorithms. Either is fine, in my opinion, as long as the physics behind is understood. This is the key part. Maybe on more topic is to be able to compare them to classical counterparts and especially in terms of asymptotic scaling. If you quantum algorithm scales worse, maybe better to forget it.

Sign up and use the resources of quantum simulators such as: IBM Quantum Experience; Rigetti Quantum Computing (forrest); or Microsoft Azure Quantum.Set up these quantum virtual machines -- the simulators / hardware / framework are all here to build and learn.

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To get the most value out of your use of quantum hardware and simulators, you will also need to know some classical machine learning, especially some of the unsupervised techniques.Quantum computing is noisy and error prone; you will need to have a way of understanding what the data is telling you when the results aren't what to expect and you need to find out why. Machine learning can be of inestimable use, and some accompanying data visualization techniques will go a long way in terms of helping you explain what you've found to your stakeholders.

I'd say this is different from the rest of the article. I don't think you need to go too far in this. This isn't essential and probably not great for beginner, for the most part as you start out you will be using the hardware and simulators offered by the maintainers of your programming language or framework. This will greatly limit your choices. There are ways around this, for example I believe there are a handful of Qiskit plugins that allow you to use another vendor's computers. To be concerned about hardware is beyond the essentials.

Quantum simulators are a good start to learning and making tests in quantum. You can use your machine without waiting for a job to run on real hardware. Once you have your code right you can try on real hardware to see the results.

Also explore hybrid models (e.g. variational models where you combine classical machine learning with quantum circuits). You do not need hardware, because depending on what you try to achieve, there is already a fully error corrected way to do QC using GPUs. On the other hand, you can compare the theoretical reults with executions on real hardware. You might want to test to build a so-called GHZ state and measure it over, say 10 qubits - theoretically and on a real machine to get the feeling of where we stand and why we need error correction.

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If you're specifically interested in Quantum Machine Learning (QML), I highly recommend exploring Qiskit or PennyLane. Both of these platforms provide a wealth of resources, including examples, tutorials, and extensive repositories, making them ideal for delving into QML. Additionally, to deepen your understanding, I suggest reading "Machine Learning with Quantum Computers" by Schuld and Petruccione (Second Edition). This book is a valuable resource that offers comprehensive insights into the intersection of machine learning and quantum computing. It’s an excellent guide for anyone looking to expand their knowledge in this cutting-edge area.

Qiskit textbook is good. Nielsen and Chuang is great. For the serious just go with classics like Classical and Quantum Computation by Kitaev et. al. while also good to know Watrus' lecture notes, Childs' work on quantum walks and for something lighter go with Aaronson's Quantum Computing Since Democritus. Avoid any books "written" by quantum startup CEOs with background in food delivery apps and evangelists of any kind who just smell dollards around. Also avoid blog posts and Linkedin posts by quantum snake oil professions (who are in abudance).

IBM's Qiskit textbook is becoming industry standard for this field.Its important to have a foundational understanding of 1) Quantum Mechanics; 2) Computer Science; 3) Applied Mathematics (linear algebra at a minimum).The best book to learn this from known, as the [bible] because it contains the foundational knowledge you [NEED] is: "Quantum Computation and Quantum Information" by Micheal Nielsen and Isaac Chuang. This book has more citations than any other in the field of physics because it covers Quantum Information Science (QIS) as the 3-pillars of knowledge -- in the framework that holds it up.

IBM Quantum Platform provides access to quantum computers, documentation, resources (courses and tutorials) to learn Qiskit and quantum computation, and an interactive tool for building circuits (Quantum Composer).

Many free online learning materials, courses, simulators, and cloud labs are now available from providers like IBM Quantum, AWS Braket, and Google Quantum AI to learn interactively at your own pace. Joining quantum computing online communities like QWorld, Quantum Computing India, OneQuantum, Womanium, etc also provides support.

Quantum Computation and Quantum Information by Michael Nielsen and Isaac Chuang, is by far my favourite literature recommendation for beginners. This book, published over two decades ago, is still one of the best books out there to learn quantum! Online courses and content are an incredible way to get started, but if you are ready to start jumping into a more academic battle, this is your way in.

Build your network in the quantum computing community. Even though the quantum computing industry is global, it's tight knit and it's easy to find leaders to follow so you can learn from them and follow in their footsteps in your own journey. From a local standpoint, look for quantum groups to join on Meetup. Globally, look for international groups to connect with, like the Quantum Strategy Institute.

There are many excellent courses for beginners on Youtube. IBM has done a particularly good job with their Qiskit Summer Schools which assume though a math background and some coding background. The climb is steeper without math or coding skills however for nontechnical exposure Youtube is an amazing source of content where one can learn at a high level a lot of interesting aspects of quantum computing.

As someone who started exploring quantum computing 2 years ago, my most crucial takeaways are staying curious, consistent and connected. In a field such as quantum which is constantly changing, curiosity drives you to explore everything from fundamental linear algebra to new innovations - the resources in this article are the perfect place to start. Staying consistent can be difficult when encountering a steep learning curve, but persistence and appreciating your journey really helps. Most importantly, I really value the supportiveness of the quantum community. It's a place where you can share ideas, learn new insights and collaborate with others who have the same passion! These 3 Cs will really help you navigate your quantum journey.

For sales professionals in quantum computing, it's essential to combine a basic understanding of quantum mechanics and tools like Qiskit with strong sales skills. They need to articulate complex technologies into clear value propositions, focusing on relationship building and strategic thinking. Sales teams must effectively communicate how quantum computing can solve business problems, reduce costs, and offer competitive advantages. Key skills also include customer education and staying updated on industry trends to connect quantum innovations with practical business applications.