Variational Quantum Optimization with Optical Polarization Qubits

The potential of quantum communication is currently limited by noisy hardware. Variational quantum optimization (VQO) methods offer a pathway towards improved quantum network performance by enabling adaptive and hardware-agnostic optimization of system parameters. Utilizing hybrid quantum-classical algorithms, VQO techniques are well-suited to seek optimal hardware settings in the presence of unknown and changing noise, as is often the case in practice. We investigate the applicability of VQO methods for optimizing noisy quantum communication hardware involving polarization-encoded optical qubits. We report our experimental results illustrating that VQO methods can automatedly establish quantum communication protocols, such as two-state discrimination or random access coding, performed on hardware in the presence of polarization and detector noise. Additionally, as VQO algorithms rely on gradient-descent estimation, we also investigate experimental trade-offs between the finite-difference and parameter-shift optimization methods. Our results provide insights into the applicability of VQO methods for increasing the robustness of optical quantum networks against detrimental effects from non-ideal noisy hardware.