Optimizing Quantum Photonic Integrated Circuits using Differentiable Tensor Networks
arXiv:2509.11861
The paper proposes a gradient‑based optimization framework that uses differentiable tensor networks to design quantum photonic integrated circuits with nonlinear unitary gates and stochastic loss components, demonstrating applications in quantum state preparation and phase sensing.
Abstract
Recent reports of large photonic nonlinearities in integrated photonic devices, using the strong excitonic light-matter coupling in semiconductors, necessitate a tailored design framework for quantum processing in the limit of low photon occupation. We present a gradient-based optimization method for quantum photonic integrated circuits, which are composed of nonlinear unitary coupling gates and stochastic, nonunitary components for sampling the photonic losses. As core of our method, differentiable tensor-networks are leveraged, which are accurate in the regime of low photonic occupation and modest intermode entanglement. After characterizing the circuit gate architecture with field simulations of GaAs-based samples, we demonstrate the applicability of our method by optimizing quantum photonic circuits for two key use cases: integrated designs for quantum optical state preparation and tailored optimal readout for quantum phase sensing.