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arXiv:2602.18299

Photonic-computing error correction through optical en-/decoder calibrations

Adam Carstensen, Babak Vosoughi Lahijani

Published Feb 20, 2026• 0 citations
arXivHTMLPDF

Abstract

Photonic processors have emerged as an attractive platform for fast and energy-efficient matrix-vector multiplication. However, they are susceptible to error due to their analog nature. Here, we present an error-correction technique that implements a correction offset to the optical en-/decoders of photonic processors. Our proposed method is general-purpose, does not require introducing any additional components to the photonic network, and can address errors stemming from unbalanced losses, 50/50 beamsplitter deviations, digital-to-analog conversion inaccuracies, and any unknown sources. In particular, we show that our method is highly effective in mitigating unbalanced-loss errors, a problem that has not previously been addressed by any error-correction technique. Using this approach, we achieve over 90% error reduction in large triangular meshes, overcoming a key obstacle to highly accurate photonic processors for information processing.

DOI
10.48550/arxiv.2602.18299

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Photonic processors have emerged as an attractive platform for fast and energy-efficient matrix-vector multiplication. However, they are susceptible to error due to their analog nature. Here, we present an error-correction technique that implements a correction offset to the optical en-/decoders of photonic processors. Our proposed method is general-purpose, does not require introducing any additional components to the photonic network, and can address errors stemming from unbalanced losses, 50/50 beamsplitter deviations, digital-to-analog conversion inaccuracies, and any unknown sources. In particular, we show that our method is highly effective in mitigating unbalanced-loss errors, a problem that has not previously been addressed by any error-correction technique. Using this approach, we achieve over 90% error reduction in large triangular meshes, overcoming a key obstacle to highly accurate photonic processors for information processing.
Optical algebraic processors can perform complex calculations in parallel and at high speeds. However, they commonly suffer from a low analog accuracy which hinders their widespread application. Error detection and correction codes provide one technique for improving the accuracy of optical algebraic processors. The use of these codes would allow some of the errors that may occur during a computation to be detected and possibly corrected. This paper describes the results of various computer simulations of optical matrix-vector multipliers employing error-correction codes. It discusses the application of convolutional codes to optical matrix-vector multipliers along with several block codes. Both binary and nonbinary codes are considered. The results indicate that a significant improvement in performance can be obtained when compared with processors not employing error-correction codes. Also, the type of noise, whether signal-independent or signal-dependent noise, has a significant effect on the performance of a matrix-vector multiplier employing an error code. The encoding and decoding operations required for the error codes can be performed optically.
Optical algebraic processors can perform complex calculations in parallel and at high speeds. However, they commonly suffer from a low analog accuracy which hinders their widespread application. Error detection and correction codes provide one technique for improving the accuracy of optical algebraic processors. The use of these codes would allow some of the errors that may occur during a computation to be detected and possibly corrected. This paper describes the results of various computer simulations of optical matrix-vector multipliers employing error-correction codes. It discusses the application of convolutional codes to optical matrix-vector multipliers along with several block codes. Both binary and nonbinary codes are not employing error-correction codes. Also, the type of noise, whether signal-independent or signal-dependent, has a significant effect on the performance of a matrix-vector multiplier employing an error code. The encoding and decoding operations required for the error codes can be performed optically.
Abstract: This study investigates a topological quantum error-correction framework for scalable photonic quantum-computing architectures in which fusion-based entangling primitives are combined with tensor-network optimization. The central problem addressed is the gap between high-fidelity photonic components and the stringent logical-error rates required for fault-tolerant quantum computation. Photonic platforms offer room-temperature operation, manufacturable silicon-photonic process flows, high-bandwidth optical interconnects and natural compatibility with measurement-based computation; however, photon loss, probabilistic fusion and decoder latency impose architectural constraints that cannot be resolved by hardware fidelity alone. The proposed framework therefore treats hardware metrics, code geometry and decoding as a coupled optimization problem rather than as separate engineering layers. The manuscript develops a mathematically grounded model of surface-code and cluster-state error correction under photonic loss and fusion noise, then evaluates tensor-network contraction as a decoder and resource-allocation method. The data section explicitly uses published real-data-based benchmark inputs from contemporary photonic and quantum-error-correction literature, supplemented by transparent, domain-plausible projections where complete open photonic logical-QEC datasets are not yet available. Results show that tensor-network optimization improves the logical-error index relative to matching-based baselines in the modelled photonic regime, while the feasibility boundary remains dominated by loss, fusion infidelity and real-time decoding latency. The contribution is a reproducible evaluation framework that links topological protection, photonic architecture constraints and decoder design for future scalable fault-tolerant systems. Keywords: topological quantum error correction; surface code; photonic quantum computing; fusion-based quantum computation; tensor-network decoding; loss tolerance; scalable quantum architecture; measurement-based quantum computing; fault tolerance; silicon photonics.
Reconfigurable photonic processors suffer performance loss from fabrication tolerances, thermal drift, and other phase- and amplitude-error sources. We introduce an energy-aware calibration routine that simultaneously restores accuracy and minimizes heater power. After output-channel normalization, every Mach-Zehnder interferometer and external phase shifter is retuned by fitting measured transfer matrices to theory, halving the error in a 4 × 4 Hadamard-transform test. Three complementary optimizations-a global phase offset, selection of the lower-voltage branch for each SU(2) rotation, and permutation of matrix rows-substantially reduce total electrical power without degrading fidelity. This precision, power-conscious calibration removes a key obstacle to scalable, high-performance photonic computing systems.
Optical processors can perform complex calculations in parallel at high speeds. They commonly suffer, however, from low accuracy, which currently hinders their widespread use. Various models for the noise in optical processors have been developed.1 In addition, various techniques have been developed to improve their accuracy. One technique involves the use of error-detection and correction codes to improve the accuracy of optical linear algebra processors.2 This allows some of the errors that occur during computation to be detected and possibly corrected. These codes, developed earlier for data transmission and fault tolerance in electronic processors, can thus be used to improve accuracy. Many of the required encoding and decoding operations can be performed optically. Computer simulations of optical matrix-vector multipliers employing various error codes will be presented. They indicate that a significant improvement in accuracy can be obtained when they are compared with processors not employing error codes.
Building fault-tolerant quantum computers with large numbers of logical qubits requires both scalable hardware architectures and error-correcting codes that make efficient use of physical qubits. Photonic interconnects address both of these challenges by allowing the physical qubits to be distributed across multiple processors while providing the non-local connectivity necessary to implement resource-efficient codes such as high-rate quantum low-density parity-check (qLDPC) codes. A key requirement for realising this architecture is the ability to perform stabiliser measurements between remote processors, which has not previously been demonstrated experimentally. Here we report the first experimental demonstration of distributed quantum error detection and correction. We generate entanglement between network qubits in two separate trapped-ion processors and use it to perform remote syndrome measurements on data qubits. We first realise a distributed [[2, 1, 1]] repetition code, detecting phase-flip errors on a logical qubit encoded across the two modules in real time and suppressing logical errors. We then combine these mid-circuit syndrome measurements with real-time feedforward to actively correct arbitrary single-qubit Pauli errors on a distributed Bell state. These results provide an experimental foundation for quantum error correction (QEC) across modular quantum architectures.