There will be an open Zoom talk by Marco Fellous Asiani January 26, 2023 at 5 PM Eastern Standard Time. There are interested participants in Korea/Japan, USA, and EU, so sorry if the time is unusual for your region.
For technical background, see https://arxiv.org/pdf/2209.05469.pdf.
The talk will be 40-45 minutes, but we can keep the Zoom running for at least an hour. You may forward a link to this page to others.
If you are interested in attending, please email email@example.com and ask to be added to the calendar invitation.
MNR: A Metric-Noise-Resource methodology to optimize resource consumption of the full-stack architecture of quantum computers for long-term scalability
Marco Fellous Asiani
In the race to build quantum computers for large problems intractable by classical computing, optimizing their architecture to maximize their scalability potential might become crucial. With this aim in mind, we propose a methodology dubbed Metric-Noise-Resource (MNR), able to quantify and optimize all aspects of the full-stack quantum computer, bringing together concepts from quantum hardware (e.g., physics of the qubits), quantum software (e.g., quantum algorithms and quantum error correction), and enabling technologies (e.g., cryogenics, control electronics, and wiring). By asking to minimize the appropriate cost function under the constraint of implementing a successful computation, one can optimize the entire computing architecture, from quantum hardware, quantum software to enabling technologies. We apply our framework in an idealized model of a full-stack quantum computer based on futuristic superconducting qubits, and we estimate minimum energy bills required to implement error-corrected quantum algorithms. We identify various trade-offs between qubit’s quality, classical electronics consumption, quantum algorithms shape, qubit’s overhead, cryostat temperatures, etc. Considering Shor’s factoring algorithm breaking RSA cryptosystem, we compare the energy our optimized quantum computer would consume to the one of a classical computer solving the same task for various key sizes. There, we exhibit regimes of energy advantage occurring before the computational one, providing another potential motivation for the development of quantum computers. In some regimes of high energy consumption, the holistic “global” approach behind our methodology can reduce consumption by orders of magnitudes compared to “local” optimizations that do not consider the entire computing system. It can be done in surprising manners that couldn’t be guessed with simple estimates. Overall, the framework developed is universal: it can be applied to any quantum computing platform. It could be used to perform benchmarks between quantum computers, drive hardware parts and architectural developments, and finally define clear multidisciplinary roadmaps in the quest for scalability.