Meet the Zapata Intern Class of 2022
Meet the Zapata Intern Class of 2022
Interns at Zapata actively advance the field of quantum computing with breakthrough research and engineering.
Zapata is home to some of the world’s leading minds in quantum science and engineering. But it is also an incubator for the next generation of quantum leaders.
Interns at Zapata actively advance the field of quantum computing with breakthrough research and engineering. In the past, Zapata interns have developed quantum algorithms that bring us closer to quantum advantage and added new capabilities to Orquestra®, Zapata’s enterprise quantum computing platform. Our interns have gone on to become postdocs in renowned research groups, lecturers at prestigious universities, and leaders within Zapata. It’s fair to say that Zapata would not be the organization it is without the contributions of our interns.
This past summer, our intern class was nothing short of exceptional. Below is a review of their many achievements.
On the Quantum Software team, interns played a key role in developing an integration with Nvidia’s GPU-based quantum simulator, cuQuantum, to accelerate the simulation of quantum circuits on Orquestra. “The cuQuantum integration was one of the most rewarding projects that I have done in my life,” said intern Boniface Yogendran, also known as Yogi. “The quantum software team gave me the confidence to take on this project and provided the right guidance to achieve it. The best part of this project was to see my work presented at Q2B Tokyo!”
Yogi and Laura Gao also worked on a research project exploring the cost function landscape for the Quantum Approximate Optimization Algorithm (QAOA), which will help us solve optimization problems more efficiently. “We hope this work will help researchers better understand the nature of this algorithm and provide them with some useful techniques for evaluating how to approach solving optimization problems with QAOA,” said Technical Lead Michał Stęchły, who supervised the interns on the project. The team plans to publish the results of this work on arXiv in October this year.
The cuQuantum integration was one of the most rewarding projects that I have done in my life. The quantum software team gave me the confidence to take on this project and provided the right guidance to achieve it. The best part of this project was to see my work presented at Q2B Tokyo!
Boniface “Yogi” Yogendran, Quantum Software Intern
Another intern, Qiyuan Hu, made an important contribution in developing a set of benchmarking tools for Orquestra. “Qiyuan laid the groundwork for our benchmarking suite which is already garnering attention throughout Zapata,” said Quantum Software Engineer Athena Caesura, who supervised his work. “His diligent and enthusiastic work style made setting up benchmarks for QAOA and Error Correction a breeze!”
Interns on the Quantum Software team contribute wherever needed, which can sometimes mean working on many unrelated projects throughout their internship. Ahmed Darwish and Juan Florez are two such cases. “During their internships, Ahmed and Juan helped our team a lot and their contributions shouldn’t go unnoticed,” said Michał. “Ahmed helped us design new data structures for handling quantum operators, got rid of plenty of technical debt and improved the quality of our codebase overall. As for Juan, he worked on building features, fixing bugs, improving docs. Things like these might look minor, but they make the Orquestra user experience much smoother when you add them up.”
Alex Juda, a Technical Lead who worked closely with Juan, added that “Juan’s biggest contribution was an experimental feature that covered multiple levels of our stack. This required preparing a design document and coordinating across multiple teams.” This kind of cross-disciplinary work is common at Zapata, where scientific research, software engineering, and business problem-solving routinely intersect.
The Quantum Hardware team had two interns this summer, Oliver Maupin and George Umbrarescu. “The interns propelled the field by helping develop tools that allow us to better use NISQ devices in a simple and understandable way,” said Amara Katabarwa, who leads the Quantum Hardware team and was once a Zapata intern himself. “We can now easily implement error mitigation strategies in a principled way and better understand how real devices work.”
For his part, Oliver worked on integrating Robust Amplitude Estimation (RAE) into Orquestra to create The Orquestra Estimators Suite. RAE is a key algorithm developed by Zapata that reduces circuit runtimes and shortens the path to quantum advantage on near-term devices. Oliver also wrote the first chapter of the Estimators Suite manual and integrated unit tests for RAE.
Not to be outdone, George helped with the release of an alpha version of Orqmit, Zapata’s first error mitigation suite. This included writing code for an error mitigation technique called Dynamical Decoupling (DD) into the suite. As part of this work, George designed a digital simulator to test how DD performs in the context of mitigating dominant coherent error, also known as ZZ errors, on superconducting devices. “As far as I know, his simulator is the first to digitally simulate DD while also simultaneously simulating ZZ errors,” said Amara. “There are other analog simulators, but this makes them hard to use in the context of quantum circuits where we have digital instructions.”
The interns propelled the field by helping develop tools that allow us to better use NISQ devices in a simple and understandable way. We can now easily implement error mitigation strategies in a principled way and better understand how real devices work.
Amara Katabarwa, Quantum Hardware Team Lead
The Quantum AI team has the deepest intern bench at Zapata — and for good reason. As we argued in a recent blog post, AI/ML (more specifically, generative modeling) will likely be one of the first places where we see quantum advantage. This summer, the Quantum AI interns made broad contributions to groundbreaking research (or in Danny Samuel’s case, writing user guides) on quantum machine learning (QML) that is now – or will soon be – available to customers in the QML Suite, Zapata’s new toolset for building quantum machine learning applications on Orquestra. The QML Suite contains several quantum and quantum-inspired generative models that customers can deploy in Orquestra to optimize areas of their business.
Building models for the QML Suite reflects a unique quality of a Zapata internship that is hard to find at other research internships: the ability to apply research to real world problems. “While I love doing research for the sake of curiosity,” noted intern Javier Lopez Piqueres, “it admittedly feels more rewarding when the outcome has a direct impact on ‘real’ problems. I am hoping that the research carried out during my internship will be useful to the Professional Services team!”
While I love doing research for the sake of curiosity, it admittedly feels more rewarding when the outcome has a direct impact on ‘real’ problems.
Javier Lopez Piqueres, Quantum AI Intern
Together with their mentors, each intern on the Quantum AI team was challenged to pick a specific research project to focus on that would ultimately support the company’s wider initiatives. Interns would then give regular presentations on their research to the team, where they would face pointed questions from their peers. This hands-on experience helped interns develop their abilities not only in research but also in communicating and defending that research, valuable skills for any researcher.
These research projects were not trivial; in every case they expanded the frontiers of QML. Mohamed Hibat-Allah, for example, contributed to breakthrough research demonstrating generalization capabilities in a quantum generative model known as a quantum circuit born machine (QCBM). Meanwhile, Atithi Acharya worked on innovative research demonstrating synergy between quantum circuits and quantum-inspired tensor networks, which shortcuts the path to quantum advantage.
This is just the tip of the iceberg of the research contributed by the interns and reflects only the research that has been published so far. Most of the interns on the team got an extension and are still actively working on their research projects, so there will be more to come by the end of the year.
In the coming months, research conducted by interns will be published on topics ranging from boosting the optimization capabilities of tensor network-based generative models (Alexander Meiburg, Javier Lopez Piqueres) to thorough comparisons of classical state-of-the-art, hybrid quantum-classical, quantum-inspired and fully quantum models (Brian Chen, Roberto Campos Ortiz, Mohamed Hibat-Allah). Others studied the trainability of QML models and the limitations and opportunities for their use on real-world quantum devices (Dhruv Devulapalli, Atithi Acharya, Danial Motlagh).
On the Quantum Algorithms Research team, all of the intern projects revolved around the question of “how should we use early fault-tolerant quantum computers?”
“Thinking back to May, we had a relatively foggy idea of what quantum computing might look like on early fault-tolerant quantum computers,” said Lead Quantum Algorithms Research Scientist and Co-Founder Peter Johnson. “This summer, the quantum algorithms interns have introduced us to many concepts that the field will come to make use of. These include signal-to-noise ratio, filter kernels, algorithm efficiency, and many others. We anticipate that such concepts will gradually permeate into the field and stimulate advances that are needed to make the technology work.”
Much of the work will be shared through upcoming research papers. These include analyzing state preparation methods (Katerina Gratsea), developing robust quantum algorithms (Yiqing Zhou), building tools to assess noise impact on quantum algorithms (Qiyao Liang), and writing software for compiling quantum circuits (Zack Bansingh).
This summer, the quantum algorithms interns have introduced us to many concepts that the field will come to make use of.
Peter Johnson, Lead Quantum Algorithms Research Scientist and Co-Founder
One paper has already been posted on the arXiv. Interns Shuchen Zhu and Ruizhe Zhang both made major contributions to the development of low-depth algorithms for ground state energy estimation. In essence, this work dramatically reduces the resources required to run quantum chemistry applications on early fault-tolerant quantum computers. To read more about the significance of this work, check out our recent blog post and see the full paper here.
“Analyzing the performance of quantum algorithms lets us look into the future of the industry. However, it takes a lot of effort and clever thinking,” said Peter. “One must simultaneously wield concepts from linear algebra, statistics, calculus, physics, and more. One of the biggest intern contributions this summer were undertaking the rigorous analysis of several quantum algorithms. These analyses, while looking like excerpts from a graduate-level math text book, provide a very strong foundation to stand on when making bold claims about the future of quantum computing.”
No intern spotlight would be complete without mentioning Ethan Hansen, or as we call him, the Quantum Podfather. As the host of the Quantum Pod, Zapata’s own quantum computing podcast, he’s interviewed industry heavyweights such as Bob Sorenson in addition to Zapata’s own leading scientists and engineers — and always with impeccable production quality. Seriously, if you’ve never listened, throw on an episode for an engaging deep dive on the biggest trends shaping quantum computing.
Ethan has been an intern since 2020 when he was still in high school. Since then, he’s been an indispensable contributor across the teams, from writing documentation for Orquestra, to writing detailed and insightful blog posts on the marketing team. The boy genius isn’t old enough to order a drink yet, but he was already managing his own intern this past summer: Sasha Cocquyt.
Sasha started at Zapata in July and made an impressive contribution in just a few short weeks. Her most impactful deliverable was her work writing user guides for the QML Suite together with Quantum AI intern Danny Samuel. “Sasha is one of those rare bright minds that captures the essence of an idea quickly and readily,” said Ethan. “She came to us with virtually no background knowledge (other than what she had built up herself running a quantum club and online courses) and within a week had mastered our GitHub flow, integrated herself in multiple teams, and started writing excellent explainers of our models and how to use them. Her work is invaluable to our goal of taking the most advanced, hardest science and making it usable for as wide of an audience as possible.”
Last but certainly not least, anybody involved in sales or professional services can tell you about the tireless work ethic of intern Sarah Bai. “In her short time at Zapata, Sarah quickly became a valued member of the Zapata BD team,” said VP of Customer Solutions Steven Stern. “I want to thank her for being an asset to our organization, and our customer network. During her time with us, we enjoyed her positive demeanor, stellar support at QTech Boston, her excellent work on our use case library rollout, and her assistance with projects involving market research. We miss her already and can’t wait to see what she’ll accomplish after graduation next year!”
Sharing our understanding of the current state of AI and quantum.