Universal logical quantum photonic neural network processor via cavity-assisted interactions
Encoding quantum information within bosonic modes offers a promising direction for hardware-efficient and fault-tolerant quantum information processing. However, achieving high-fidelity universal control over bosonic encodings using native photonic hardware remains a significant challenge. We establish a quantum control framework to prepare and perform universal logical operations on arbitrary multimode multi-photon states using a quantum photonic neural network. Central to our approach is the optical nonlinearity, which is realized through strong light-matter interaction with a three-level Λ atomic system. The dynamics of this passive interaction are asymptotically confined to the single-mode subspace, enabling the construction of deterministic entangling gates and overcoming limitations faced by many nonlinear optical mechanisms. Using this nonlinearity as the element-wise activation function, we show that the proposed architecture is able to deterministically prepare a wide array of multimode multi-photon states, including essential resource states. We demonstrate universal code-agnostic control of bosonic encodings by preparing and performing logical operations on symmetry-protected error-correcting codes. Our architecture is not constrained by symmetries imposed by evolution under a system Hamiltonian such as purely χ(2) and χ(3) processes, and is naturally suited to implement non-transversal gates on photonic logical qubits. Additionally, we propose an error-correction scheme based on non-demolition measurements that is facilitated by the optical nonlinearity as a building block. Our results pave the way for near-term quantum photonic processors that enable error-corrected quantum computation, and can be achieved using present-day integrated photonic hardware.
Authors
- Jasvith Raj Basani (ORCID: https://orcid.org/0000-0001-8453-8104)
- Edo Waks A (ORCID: https://orcid.org/0000-0003-0299-0784)
- Murphy Yuezhen Niu
Institutions
- Google (United States) (US)
- University of California, Santa Barbara (US)
- Joint Center for Quantum Information and Computer Science (US)
- Joint Quantum Institute (US)
- University of Maryland, College Park (US)
Publication Details
- Journal
- npj Quantum Information
- Published
- 2025-08-20
- DOI
- https://doi.org/10.1038/s41534-025-01096-9
- Citations
- 2
- Primary Topic
- Neural Networks and Reservoir Computing
- Type
- article
- Field-Weighted Citation Impact
- 4.96
Funders
- Defense Advanced Research Projects Agency
- Graduate School, University of Maryland