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Hardware-Efficient Universal Linear Transformations for Optical Modes in the Synthetic Time Dimension

Jasvith Raj Basani1,2,*, Chaohan Cui1,3,*,†, Jack Postlewaite1, Edo Waks1,2, and Saikat Guha1,3

  • *These authors contributed equally.
  • †Contact author: chaohan@umd.edu
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PRX Quantum 7, 020305 – Published 8 April, 2026

DOI: https://doi.org/10.1103/yvcj-d7pb

Abstract

Recent progress in photonic information processing has spurred strong demand in scalable and reconfigurable photonic circuitry. Conventional spatially meshed multiport interferometers require a number of components growing quadratically with the system size, posing a fundamental scaling challenge ahead. Here, we introduce a hardware-efficient synthetic time-domain photonic processor that achieves at least an exponential reduction in hardware component count for implementing arbitrary linear transformations. The processor’s dynamic connectivity allows systematic pruning, minimizing optical loss while preserving all-to-all connectivity. We benchmark our architecture on the task of boosted Bell-state measurements—a protocol essential for linear optical quantum computation, and show that it exceeds thresholds for universal cluster-state quantum computation under realistic hardware constraints. We link the device performance to the geometry of multiphoton transport, showing that localization effects from redundant, imperfect hardware may enhance robustness to coherent errors. Our design establishes a practical pathway toward near-term, scalable, and reconfigurable photonic processors in the synthetic time dimension.

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Popular Summary

By leveraging the time dimension, researchers have designed a photonic processor that dramatically reduces hardware complexity. Unlike conventional spatially multiplexed optical circuits that grow unwieldy as they scale, the synthetic time-domain design achieves an exponential reduction in components while retaining full connectivity. The team demonstrated its power by surpassing key thresholds for quantum computing tasks, even under realistic hardware imperfections. This breakthrough opens a practical pathway toward scalable, reconfigurable, and robust photonic quantum processors for future quantum technologies.

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PRX Quantum 7, 020305– Published 8 April, 2026
  • Received 26 August 2025
  • Revised 4 January 2026
  • Accepted 4 March 2026
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