Robust and scalable integrated optical unitary processors based on all-to-all-coupled interferometers

Ryota Tanomura, Rui Tang, Takuo Tanemura +1 more

Yadgirkar, Rohan

Universal multiport interferometers based on Mach–Zehnder meshes are becoming a core building block for photonic neural-network accelerators and photonic quantum processors. This thesis presents the design, calibration, and control of such a programmable photonic integrated circuit (PIC). A fully automated software and hardware platform is developed to characterize and control the tunable elements of the PIC, enabling robust implementation of arbitrary linear optical transformations as well as target unitary operations. Using the calibrated device model, the control framework programs the PIC to realize specific unitaries and evaluates their performance through fidelity metrics computed from measured transfer matrices. By covering the complete stack from chip design to automated calibration and high-level unitary programming, this work establishes a scalable framework for design and operation of programmable photonic processors.

Ryota Tanomura, Rui Tang, Takuo Tanemura +1 more

Optical unitary converters (OUC) that enable reconfigurable unitary transformation between the input and output optical bases has gained increased interest for various applications. We compare different architectures of integrated OUC in terms of fabrication tolerance and demonstrate that all-to-all-coupled interferometer significantly enhances the robustness.

Christophe Couteau

Stefano Paesani

Pablo Martínez-Carrasco Romero, Andrés Macho, José Roberto Rausell‐Campo +2 more

Programmable integrated photonics (PIP) has emerged as a powerful on-chip platform for optical signal processing and computing, enabling the implementation of reconfigurable N$\times$N unitary matrix transformations through meshes of tunable interferometers, which realize 2$\times$2 unitary matrices. However, the energy consumption associated with phase-shifter actuation is becoming a major limitation to the scalability of PIP platforms. Here, we introduce a geometric framework for energy optimization in PIP circuits by exploiting the representation of 2$\times$2 unitary matrices as concatenations of basic Euler rotations on the Bloch sphere. We show that equivalent implementations of the same N$\times$N unitary matrix (N $\geq$ 2) can exhibit markedly different energy costs depending on the rotation trajectories on the Bloch sphere implemented by each interferometer. Leveraging this insight, we identify minimum-energy configurations by systematically selecting the shortest rotation trajectories. We experimentally and numerically validate the proposed approach in diverse silicon PIP architectures, including feedforward and multipurpose hexagonal meshes, neural network accelerators, and photonic quantum-gate implementations. These results establish a general route toward more energy-efficient large-scale PIP processors for classical and quantum signal processing and computing applications.