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In his seminal paper, 'Simulating physics with computers', Richard Feynman argued that very complex problems would require a new computational paradigm to be solved. Curiously, he evoked the many-body problem as an example. In an era driven by data, there is a strong urge to tackle problems of enormous complexity. This is the case of problems described by a very large number of variables and countless possible configurations, tasks too difficult even for the most powerful classical processors and supercomputers. These practical needs, combined with advances in our ability to manipulate atomic systems, have enabled the remarkable developments in quantum computing we have witnessed over the two past decades. Who would have imagined such rapid progress? To make the much-hyped promise of quantum advantage (aka supremacy) a reality, there is still a great deal of work to be done. Not only does the hardware needs to be developed, but also the software has to be engineered. Limitations in current quantum architectures -- specifically scalability and noise -- continue to hinder practical applications with true advantage. This raises an important question: how can we combine the power of classical and quantum computing to make the most of both approaches?
If you have already read the other research pages, you might be wondering when and how I became interested in quantum computing. Well, for me, the answer is quite straightforward. Imagine that you have a background in numerical methods for many-body systems. You know these methods are powerful, but you are aware of their limitations in a variety of scenarios. At some point of your research, you find yourself super interested in problems where these limitations emerge and you feel disheartened. Then, you suddenly witness a new powerful machine pop up, together with fresh and promising algorithms. Both machine and algorithms were conceived precisely to tackle the class of complex systems you are eager to study. Sounds natural, right?
Someone might then ask: 'Why didn't you pick quantum computing as your main field during your graduate training? It might sound like you are jumping into in the hype now ...' . Well, when I started my PhD, I was already living in the condensed matter world, and the many-body problem was tattooed in my heart. At the time, the applicability of quantum computing - especially to the many-body problem - felt futuristic and far away. Over the last decade, many new and extremely challenging classical simulations popped up in front of me. Meanwhile, quantum simulation evolved from analogue to digital, and quantum computing became a tangible reality. Would any computational physicist resist the temptation to learn about that? This is the story of a girl who cried a river and drowned the whole world 😂. Luckily, there was the quantum ark. I am currently learning how to swim - classically as well 🏊🏼♀️ (true story) - to be saved by it.
Someone might then ask: 'Why didn't you pick quantum computing as your main field during your graduate training? It might sound like you are jumping into in the hype now ...' . Well, when I started my PhD, I was already living in the condensed matter world, and the many-body problem was tattooed in my heart. At the time, the applicability of quantum computing - especially to the many-body problem - felt futuristic and far away. Over the last decade, many new and extremely challenging classical simulations popped up in front of me. Meanwhile, quantum simulation evolved from analogue to digital, and quantum computing became a tangible reality. Would any computational physicist resist the temptation to learn about that? This is the story of a girl who cried a river and drowned the whole world 😂. Luckily, there was the quantum ark. I am currently learning how to swim - classically as well 🏊🏼♀️ (true story) - to be saved by it.