Bio
Jens Eisert is …
Abstract
Quantum computers promise the efficient solution of some highly structured computational problems that are classically intractable. While for many years they have been primarily objects of theoretical study, only recently have efforts to build intermediate-scale quantum computers taken off. This creates an interesting state of affairs, but at the same time, it begs the question of what such devices are, practically speaking, good for. In this talk, we will present some encouraging as well as—emphasizing the latter—discouraging insights into near-term quantum computing. We will discuss rigorous quantum advantages in paradigmatic problems [1,2] and explore the use of quantum computers in machine learning [3,4] and optimization [5]. The second part of the talk will focus on the significant limitations that arise. We will emphasize identifying limitations to quantum error mitigation for shallow quantum circuits in the worst case [6]. Interestingly, it may depend on the nuances of non-unital quantum noise to what extent quantum computing without error correction may be feasible [7]. We will also provide efficient classical algorithms for instances of quantum algorithms, hence “de-quantizing” them [7-9]. The talk will conclude with the note that quantum simulation remains, to date, one of the most promising applications of near-term quantum devices [10,11].
[1] Rev. Mod. Phys. 95, 035001 (2023). [2] arXiv:2307.14424, Nature Comm. (2024). [3] Nature Comm. 15, 434 (2024). [4] Nature Comm. 15, 2277 (2024). [5] Science Adv. 10, eadj5170 (2024). [6] arXiv:2210.11505, Nature Phys. (2024). [7] arXiv:2403.13927 (2024). [8] arXiv:2309.11647 (2023). [9] Phys. Rev. Lett. 131, 100803 (2023). [10] Nature Comm. 14, 3895 (2023). [11] arXiv:2108.08319, Nature Comm. (2024).