DEV Community: imtarajones

What I learned in Quantum Computing this year (as a Junior Engineer)

imtarajones — Mon, 23 Dec 2024 22:25:34 +0000

As a junior software engineer who found a role working in quantum computing, 2024 has been a really interesting year. I wanted to share this post as an example for others just coming into this industry as it can seem like everyone is an expert and knows exactly what they are doing. Which isn't really the case. My journey isn't too special, but going through college I thought I would end up either in finance as a quant, or at some giant software company in California (you know the ones), so going from a theoretical understanding to a hands-on development role in such an important new industry as quantum computing has been a surprise. And a lot of hard work. And an opportunity that I won't be wasting to continue to grow and learn and be able to help others do so too.

Julia was a welcome surprise

The quantum computing community's embrace of Julia caught my attention early this year. While Python remains dominant, Julia's quantum packages like Yao.jl and QuantumOptics.jl offered surprisingly elegant solutions for quantum circuit design. The language's multiple dispatch system proved particularly useful for handling different quantum gate implementations. However, the learning curve was steep - coming from Python, I spent countless evenings trying to better understand Julia's type system. The learning path on Julia's own site is really good though.

Evolution with Qiskit

My relationship with Qiskit has changed a lot since the first tutorials back before the 1.0 update. I'm still finding a lot of broken resources because of that update but at least now I don't see it just as a black box for circuit construction. I've had to learn how to use its pulse-level programming capabilities for work (although "be aware of" is probably more accurate then being an expert in how to do this day to day). This deeper understanding helped me understand what my team are doing when they optimize our error mitigation strategies, particularly when dealing with cross-talk on IBM's devices. The transition from Circuit to Primitive-based workflows in Qiskit took adjustment, but ultimately led to more maintainable code.

Going from simulation to reality with more hardware access

Outside of my day job I got to access more IonQ and Quantinuum hardware through Amazon Braket and Microsoft Azure Quantum. One of my mentors who was a product manager for a quantum company pushed me to try all the various quantum onboarsding guides I could find and it was a great idea. I worried that it might feel like a lot of abstracted walk throughs but it forced me to try new systems I wouldn't have otherwise used. For example the contrast between superconducting and trapped-ion systems became tangible rather than theoretical. I learned the hard way that algorithms performing well in simulation often require substantial modification for real hardware. And cross-platform benchmarking became a regular part of my workflow, teaching me to think more critically about qubit connectivity and gate fidelities.

Classiq and open source communities

Another nudge from the mentor angle was to explore all the different open source projects. I was really impressed by Classiq's algorithm library and their various workshops and hackathons and outreach efforts make it easier to get involved and learn by doing. It also opened my eyes to intermediate representations in quantum circuit synthesis. Their approach to automated circuit optimization challenged my understanding of quantum compilation. While I initially struggled with their abstraction layers, the ability to generate hardware-aware circuits across different backends proved invaluable for our projects. I also got to jump into some new open source communities like the Unitary Fund, which while I haven't been particularly noisy as a part of it, I appreciate it exists and I can dip in and out and see what everyone is talking about. I hope to get more involved in 2025.

Azure Quantum Training

Microsoft's Azure Quantum training proved unexpectedly valuable. I could roll this under the categories above but this was a real surprise for me as someone who doesn't use any Microsoft tools otherwise. Which I know some older friends find amazing as they've all come through the previous generation where Microsoft was dominant. Beyond the platform-specific knowledge, I gained practical experience with Q# and quantum intermediate representation (QIR). The structured approach to error correction and the exploration of the ideas of topological qubits gave me a stronger foundation in quantum error correction principles. Also a really smooth set of documentation and user guides.

Growing female representation

One of the most encouraging developments this year has been connecting with more women in quantum computing. I don't come from science academia so I'm used to there being a lot less women in software engineering, so this is a happy surprise. I see great inspiration everywhere, like the Qubit by Qubit team, or all of Anastasia's videos, or even Hannah Fry's excellent video documentary for Bloomberg recently. Plus heaps of inspiring peers and colleagues who I will spare the public links! But thank you to all of them and everyone who makes it easy to just get involved and get to work.
Looking Forward

As I reflect on this year's journey, I'm struck by how rapidly the field evolves even while we all complain that it's taking so long. The gap between theoretical proposals and practical implementation continues to narrow, though significant engineering challenges remain. For junior engineers entering the field, my advice would be to maintain strong foundations in both classical and quantum algorithms while staying adaptable to new tools and approaches. And be prepared to work on a big problem for a long time. The rewards in the meantime are worth it!

The programming languages I learned in my Quantum Computing job

imtarajones — Fri, 15 Mar 2024 05:35:22 +0000

If you want to feel like you will never stop learning then join a Deep Tech startup. I did. And I haven’t had a day that didn’t feel like I was learning more each day then entire months in my college degree. It’s an amazing feeling and a lot of hard work.

As a junior developer I learned the langugaes and tools of the things I found interesting and could work on for fun. Although I’m not sure that I would call Javascript and an infinite amount of frameworks anything remotely resembling “fun”. It wasn’t until I joined a company that has quantum computing projects that I really took this side of things seriously, and tackled a few “new to me” coding languages.

Turns out this was a good idea. Because quantum computing is an interesting use case for some of the Machine Learning roles out there, and it might be lucrative in the future. This is exactly what I’ve been working on lately! So if that’s interesting to you too, here’s what I recommend you learn, what you might expect to learn, and a summary of what I had to learn. All rolled in one.

Python

Python is probably the most popular coding language used in quantum computing. It’s a versatile language that’s pretty easy to learn and has a wide range of applications in this area. You will quickly come to find that Python is used in many quantum computing frameworks, including Qiskit, Cirq, and Q#.

Python’s popularity in quantum computing is due in part to a mix of simplicity and readability. It’s also an excellent language for data analysis and visualization, which are crucial skills in quantum computing. This is all helped immensely where Python has a lot of libraries and tools that make it easy to work with data, scientific workloads, and as a result, a lot of quantum computing frameworks.

Here’s what I used to learn Python

Codecademy: Interactive lessons and practice problems. - https://www.codecademy.com/learn/learn-python-3
LearnPython.org: A comprehensive tutorial site. https://www.learnpython.org/
“Automate the Boring Stuff with Python”: A practical, project-based book. https://automatetheboringstuff.com/

Julia

Julia is another popular coding language used in quantum computing. I wasn’t familiar with this until I started my job, and it seems to be a relatively new language that’s designed to be fast and efficient in the fields of science and analysis. Julia is particularly useful for quantum computing because it can handle complex numerical computations quickly and accurately.

Julia has a number of advantages over Python. It’s designed to be faster and more efficient than Python (although I can’t speak to this yet given the project work I use it on), but this in theory makes it ideal for large-scale quantum computing applications. Julia also has a number of libraries and tools that make it easy to work with quantum computing and other scientific frameworks.

Here’s what I used to learn Julia

JuliaAcademy: A dedicated learning platform with a range of Julia courses on various topics. https://juliaacademy.com/
ThinkJulia.jl: An adaptation of the book “Think Python”, this resource uses a similar approach to teach programming fundamentals with Julia. https://juliapackages.com/p/thinkjulia
JuliaLang.org “Getting Started” Guide: The official Julia documentation provides a concise introduction to the language and syntax. https://julialang.org/learning/
MIT Computational Thinking with Julia and Pluto.jl: Uses interactive Pluto notebooks for a highly engaging learning experience. https://computationalthinking.mit.edu/Spring21/

C++

Okay don’t go running away screaming. It’s not as intense as it sounds. We all know that C++ is a powerful programming language and it’s no surprise that it’s also pretty commonly used in quantum computing. It’s particularly useful for developing quantum computing frameworks and libraries. C++ is a high-performance language that’s well-suited for applications that require low-level memory management and high-speed computation. This might not be what you work on day to day, but it’s always a good skill to have.

C++ is used in many quantum computing frameworks, including Qiskit and Cirq. It’s also used in a number of quantum computing libraries, such as the Quantum Toolkit and the Quantum Computing Toolkit. I’m sure there’s many others out there as well, but these are just the ones that I’ve used.

I’m not going to lie, C++ is a more challenging language to learn than Python or Julia, but it offers a number of advantages for quantum computing applications. It’s particularly useful for developing high-performance quantum computing algorithms that require low-latency computations. Or in my case, for being able to understand what my senior team leaders who are working on these use cases are doing, which in turn makes the quantum workloads that I’m contributing make more sense, and give more room to adapt and align with that “lower level” work.

Here’s what I used to learn C++

LearnCpp.com: A comprehensive site with tutorials covering everything from basic syntax to advanced concepts. https://www.learncpp.com/
Codecademy: Offers beginner-friendly interactive lessons on C++ basics. https://www.codecademy.com/catalog/language/cpp
HackerRank: Solve coding challenges and compete with others while honing your C++ skills. https://www.hackerrank.com/
Effective Modern C++: Considered essential reading for mastering modern C++ practices. https://www.oreilly.com/library/view/effective-modern-c/9781491908419/

And that’s just a start…

Working in Deep Tech feels like a balance between excitedly pursuing the unknown, and a stress of not knowing enough to be able to effectively do so. Thankfully it’s mostly the first feeling, and we have so many resources now to help us in our learning journey.

Hopefully this helps others who are looking to make careers either in Deep Tech or quantum computing in particular, and even if you are a master of Python, Julia, and C++ you’ve always got Java and MATLAB and others to master too. The journey is never over. But today is a good day to start!

The top 10 news and blogs for quantum computing

imtarajones — Sun, 25 Jun 2023 20:53:02 +0000

Quantum computing is exciting… but it’s also very hard. And not just that, it’s also changing and moving and evolving at a really fast pace. Keeping up with all the announcements and new releases and entire platform changes is half of the fun. If you caught my other posts you can see I’m learning in the open here, so keeping with that spirit, here’s my collection of what I think are the most popular technology blogs about Quantum Computing. If you have any other suggestions, let me know!

The Quantum Insider: This one is as close to the TechCrunch of quantum computing as it seems anyone has gotten. It’s got a lot of breaking news, press releases, and some good hot takes and editorial content. I haven’t tried their “Intelligence Platform” yet (who has the money for this as an individual?) but I hope to check it out sometime.

Quantum Untangled: This is a Substack written by Ryan Morrison and it’s not just informative but he seems to put effort into talking with the people and teams behind the press releases. Great to read a real journalist in this space.

Quantum journal: This is a non-profit and open access peer-reviewed journal that provides high visibility for quality research on quantum science and related fields. Can be a bit heavy being focused on papers but still a super useful resources.

Gizmodo | Quantum Computers: Gizmodo is a big tech blog but their Quantum Computing section is pretty good. No idea why. But happy that it is, and not just the usual press releases and such.

Quantum Computing Report: This blog provides a wide range of resources including a list of software, systems, cloud access points, and applications related to quantum computing. The popup ads are a bit annoying and its not the best design, but there’s good stuff there.

Qiskit blog on Medium: It wouldn’t be a quantum computing roundup without IBM’s community project getting namechecked. I wonder if they will always be the dominant one, but for now they are, and the community is super friendly and helpful.

Microsoft Quantum Blog: Microsoft’s quantum computing blog can be a little heavy on the self serving side of things, but they’ve been active and there’s a lot going on. Plus it’s good that it’s not just IBM all the time!

InsideHPC | Quantum Computing: This blog covers news and analysis on the Quantum Computing industry/market. One of the ugliest blogs you will ever read on the topic but there’s useful content tucked in amongst the shockingly old fashioned CMS.

QuTech Blog: QuTech, a collaboration between TU Delft and TNO (Netherlands Organisation for Applied Scientific Research), shares progress in their research in quantum technology on this blog. Needs updating but some good stuff in there.

Quantum AI (Google Quantum): This blog is from Google’s Quantum AI team, where they share updates on their latest quantum computing research. It’s a shiny website and takes a little effort to dig into the good stuff, but that’s Google these days I guess. Fancy.

I’m sure there are a LOT more but these are the ones I use for now. I hope that we see more and more independent journalists focusing on deep tech like this, so let me know if you see any.

Open source as a path to learning Quantum Computing

imtarajones — Sun, 11 Jun 2023 03:45:45 +0000

It sounds really intense doesn't it? Quantum computing! The funny thing about it is that it doesn't have to be. We learn programming without having to learn what a CPU does, so it's interesting that we think we need to be physicists to learn to contribute to this new industry technology.

I wrote in "A Beginner's Guide to Starting Your Journey in Quantum Computing" that there are some fundamentals to learn. But we don't have to be experts in them. We just need to know what they are, and have some experience in some of them. Like anything technical, the opportunities come not from individual mastery, but the progress in and of a community.

This applies even more-so in the quantum computing world, where the maturing industry needs more and more developers and designers and marketing and business and UX and devops and everything in-between. The paradox to date has been that the technical teams need more general talent but can't find any that has exposure to the culture and context of quantum computing. And the general talent can't find the opportunities to break into those teams. Which is where open source communities and open projects come in. Even the smallest level of participation in one can create a ladder to climb into a rewarding, incredible, and about-to-change-the-world kind of career.

So here are ten open-source software projects in the field of quantum computing that you can explore and try:

1. Qiskit

Qiskit is developed by IBM and is a popular open-source framework for quantum computing. It provides a comprehensive set of tools, libraries, and simulators to design and run quantum circuits, execute experiments on real quantum hardware, and develop quantum algorithms. Qiskit also offers a friendly community and extensive documentation to support beginners. It's a bit weird that it seems to try hard to "not be IBM" but it's just IBM. But the content and community are enormous and it's a default one to learn.

2. Cirq

Cirq is developed by Google, and is an open-source framework for quantum computing that focuses on writing quantum circuits and running them on simulators and quantum processors. It offers a Python-based interface and supports a variety of quantum devices. Cirq's documentation and examples provide a solid starting point for quantum programming. IMHO this side of things is best for those with an interest in AI given this is almost certainly the focus Google has.

3. Qristal SDK

Qristal SDK is made by Quantum Brilliance and is an open-source software development kit (SDK) designed for quantum computing research and education. It provides a full stack to compile, simulate and deploy a variety of quantum applications on virtual or hardware quantum computing devices. Quantum Brilliance is focusing on room-temperature quantum accelerators and works with existing HPC installations. It's pretty new and team is small but friendly, which makes it ideal for people wanting closer involvement (and opportunities) with the team.

4. Forest

Forest, developed by Rigetti Computing, is another open-source software development kit (SDK) for quantum computing. It includes PyQuil, a Python-based quantum programming language, and offers access to Rigetti's quantum cloud services. Forest provides a comprehensive toolkit for quantum algorithm development and experimentation. The Rigetti community has ebbed and flowed a little (they were in the news in early 2023 about financial woes and layoffs) but is still very much a leading brand.

5. Xanadu PennyLane

PennyLane is an open-source software framework developed by Xanadu for quantum machine learning and quantum optimization. It enables the integration of quantum computing with popular machine learning libraries, such as TensorFlow and PyTorch. PennyLane allows you to simulate and run quantum circuits on various backends.

6. OpenQASM

OpenQASM is an open-source quantum assembly language developed by IBM as part of the Qiskit framework. It allows users to write quantum circuits using a text-based representation. OpenQASM provides a standardized format for describing quantum algorithms and is compatible with various quantum programming frameworks.

7. QuTiP :

QuTiP or "Quantum Toolbox in Python" is an open-source Python library for simulating and manipulating quantum systems. It offers a range of functionality for quantum dynamics simulations, quantum state and operator manipulation, and quantum information theory. QuTiP is widely used in the quantum physics and quantum information research community.

8. Strawberry Fields

Strawberry Fields is an open-source quantum software platform developed by Xanadu for simulating and executing photonic quantum computations. It provides tools for designing, simulating, and optimizing quantum circuits with continuous-variable (CV) quantum systems. Strawberry Fields is particularly focused on quantum information processing using photonic quantum hardware.

9. pyQuil

pyQuil is an open-source Python library developed by Rigetti Computing. It enables users to write and execute quantum programs using the Quil language, which is a quantum instruction set designed for near-term quantum devices. pyQuil provides access to Rigetti's quantum cloud services and offers a range of utilities for quantum algorithm development.

10. Quantum Development Kit

The Quantum Development Kit (QDK) is an open-source framework developed by Microsoft for programming quantum computers. It includes Q#, a high-level programming language designed specifically for quantum computing. The QDK provides simulators, libraries, and tools to develop and debug quantum algorithms. It also offers integration with classical languages like C# and Python.

Getting involved is a community thing

These projects offer diverse functionalities, programming interfaces, and community support. Exploring these open-source projects will provide you with hands-on experience, access to quantum simulators, and, in some cases, direct access to real quantum hardware. Remember to check their documentation, tutorials, and code examples to get started and be sure to reach out to their developer relations or product management teams, as they are always open to conversations and very encouraging of anyone getting involved with their projects. In my next article I will show how to get started and first steps to take together.

A Beginner's Guide to Starting Your Journey in Quantum Computing

imtarajones — Sat, 10 Jun 2023 22:33:53 +0000

Quantum computing is a wild and wonderful industry that's slowly moving to take its place at the forefront of technological advancement. The potential of it is to offer immense potential for solving complex problems and revolutionizing various industries. If you're intrigued by the possibilities of quantum computing and eager to embark on a learning journey, this guide will provide you with a solid starting point. Let's explore the essential steps to begin your exploration of quantum computing.

1. Understanding the Basics

Before diving into quantum computing, it's crucial to establish a foundation in the underlying principles of quantum mechanics. Familiarize yourself with concepts such as superposition, qubits, entanglement, and quantum gates. Online resources, textbooks, and introductory courses are excellent starting points to gain a theoretical understanding of these concepts.

2. Mathematics and Linear Algebra

Quantum computing involves a significant amount of mathematics, particularly linear algebra. A solid grasp of concepts like vectors, matrices, and complex numbers will greatly assist in understanding quantum algorithms and operations. Online tutorials, textbooks, and interactive courses can aid in developing your mathematical skills for quantum computing.

3. Quantum Programming Languages

To interact with quantum computers and simulate quantum algorithms, you'll need to learn quantum programming languages. Popular languages include Qiskit, Cirq, and PyQuil. Each language provides a framework to write quantum circuits, execute simulations, and run experiments on real quantum hardware. Dive into the documentation, tutorials, and code examples provided by these frameworks to familiarize yourself with their syntax and capabilities.

4. Online Learning Resources

The internet is a treasure trove of learning resources for quantum computing. Explore online platforms such as IBM Quantum Experience, Microsoft Quantum Development Kit, and the Quantum Computing Playground. These platforms offer interactive tutorials, coding exercises, and access to cloud-based quantum computers, enabling hands-on experience with real quantum systems.

5. Quantum Computing Communities

Engaging with quantum computing communities is invaluable for learning and staying updated on the latest developments. Join online forums, discussion boards, and social media groups dedicated to quantum computing. Participate in conversations, ask questions, and collaborate with like-minded individuals. The quantum community is welcoming and eager to assist beginners on their learning journey.

6. Quantum Algorithms and Applications

Delve into studying quantum algorithms and their applications. Start with foundational algorithms like Grover's algorithm and Shor's algorithm. Explore their implications in solving problems such as database search and prime factorization. Investigate quantum machine learning, quantum chemistry, and optimization problems to understand the wide-ranging potential of quantum computing.

7. Hands-on Experiments

To solidify your understanding, aim for hands-on experience. Leverage quantum simulators provided by quantum programming frameworks to experiment with quantum circuits and algorithms. As you progress, consider accessing real quantum hardware through cloud-based quantum computing platforms. Actively experimenting and running quantum programs will enhance your learning and comprehension of quantum computing.

Conclusion

Embarking on the journey of learning quantum computing may seem daunting, but with perseverance and dedication, you can acquire the knowledge and skills necessary to explore this transformative field.

The more official advice will tell you to "build a strong foundation in quantum mechanics and develop your mathematical proficiency", but this doesn't mean there's not a role for you sooner rather than that. It's not just for the experts now. With some understanding of the common programming languages in quantum computing (such as Python), you can easily engage with the quantum computing community, study quantum algorithms, and conduct hands-on experiments with some of the cloud-based and open source tools.

My advice is to embrace the continuous learning process and stay curious, as quantum computing is a rapidly evolving field with immense possibilities waiting to be explored. As we will see in my next post where I will recommend some great open source projects to help you get started!