Speaker
Description
We introduce a reinforcement learning algorithm designed to identify the fixed points of a given quantum operation. The method iteratively constructs the unitary transformation that maps the computational basis onto the basis of fixed points through a reward-penalty scheme based on quantum measurements. In cases where the operation corresponds to a Hamiltonian evolution, this task reduces to determining the Hamiltonian eigenstates. The algorithm is first benchmarked on random Hamiltonians acting on two and three qubits and then applied to many-body systems of up to six qubits, including the transverse-field Ising model and the all-to-all pairing Hamiltonian. In both cases, the algorithm is demonstrated to perform successfully; in the pairing model, it can also reveal hidden symmetries, which can be exploited to restrict learning to specific symmetry sectors. Finally, we discuss the possibility of post-selecting high-fidelity states even when full convergence has not been reached.
References
[1] M. L. Olivera-Atencio, J. Casado- Pascual and D. Lacroix, Exploring fixed points and eigenstates of quantum systems with reinforcement learning, arXiv:2511.17491v1 [quant-ph] (2025) [2] F. Albarr´an-Arriagada, J. C. Retamal, E. Solano, and L. Lamata, Reinforcement learning for semi-autonomous approximate quantum eigensolver, Mach. Learn.: Sci. Technol. 1, 015002 (2020).
| Sessions | Quantum Machine Learning: |
|---|---|
| Invited | No |