---

With rapid progress across platforms for quantum systems, the problem of many-body quantum state reconstruction for noisy quantum states becomes an important challenge. There has been a growing interest in approaching the problem of quantum state reconstruction using generative neural network models. In this talk, I will present the 'attention-based quantum tomography' (AQT), a method of quantum state reconstruction that uses an attention mechanism-based generative network. The results demonstrate not only that AQT outperforms earlier neural-network-based quantum state reconstruction on identical tasks but that AQT can accurately reconstruct the density matrix associated with a noisy quantum state experimentally realized in an IBMQ quantum computer. In addition, I will highlight broader applications of AQT currently being developed.

---

Peter Cha, Cornell
Seminarraum Altbau Theorie
Contact: Simon Trebst