Scalable machine learning-assisted clear-box characterization for optimally controlled photonic circuits

• URL: http://arxiv.org/abs/2310.15349v2
• Date: Thu, 22 Feb 2024 09:43:38 GMT
• Title: Scalable machine learning-assisted clear-box characterization for optimally controlled photonic circuits
• Authors: Andreas Fyrillas, Olivier Faure, Nicolas Maring, Jean Senellart, Nadia Belabas
• Abstract summary: We introduce a scalable and innovative method to characterize photonic chips through an iterative machine learning-assisted procedure. Our method is based on a clear-box approach that harnesses a fully modeled virtual replica of the photonic chip to characterize. We validate our characterization and imperfection mitigation methods on a 12-mode Clements-interferometer equipped with 126 phase shifters.
• Score: 1.0187122752343796
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Photonic integrated circuits offer a compact and stable platform for generating, manipulating, and detecting light. They are instrumental for classical and quantum applications. Imperfections stemming from fabrication constraints, tolerances and operation wavelength impose limitations on the accuracy and thus utility of current photonic integrated devices. Mitigating these imperfections typically necessitates a model of the underlying physical structure and the estimation of parameters that are challenging to access. Direct solutions are currently lacking for mesh configurations extending beyond trivial cases. We introduce a scalable and innovative method to characterize photonic chips through an iterative machine learning-assisted procedure. Our method is based on a clear-box approach that harnesses a fully modeled virtual replica of the photonic chip to characterize. The process is sample-efficient and can be carried out with a continuous-wave laser and powermeters. The model estimates individual passive phases, crosstalk, beamsplitter reflectivity values and relative input/output losses. Building upon the accurate characterization results, we mitigate imperfections to enable enhanced control over the device. We validate our characterization and imperfection mitigation methods on a 12-mode Clements-interferometer equipped with 126 phase shifters, achieving beyond state-of-the-art chip control with an average 99.77 % amplitude fidelity on 100 implemented Haar-random unitary matrices.

Related papers

• A Hybrid Approach to Mitigate Errors in Linear Photonic Bell-State Measurement for Quantum Interconnects [0.0]
  We introduce a novel hybrid detection scheme for Bell-state measurement. We derive explicit fidelities for quantum teleportation and entanglement swapping processes. This work provides a new tool for linear optics schemes, with applications to quantum state engineering and quantum interconnects.
  arXiv  Detail & Related papers  (2024-06-14T18:00:00Z)
• Characterization of multi-mode linear optical networks [0.0]
  We formulate efficient procedures for the characterization of optical circuits in the presence of imperfections. We show the viability of this approach in an experimentally relevant scenario, defined by a tunable integrated photonic circuit. Our findings can find application in a wide range of optical setups, based both on bulk and integrated configurations.
  arXiv  Detail & Related papers  (2023-04-13T13:09:14Z)
• Experimental realization of deterministic and selective photon addition in a bosonic mode assisted by an ancillary qubit [50.591267188664666]
  Bosonic quantum error correcting codes are primarily designed to protect against single-photon loss. Error correction requires a recovery operation that maps the error states -- which have opposite parity -- back onto the code states. Here, we realize a collection of photon-number-selective, simultaneous photon addition operations on a bosonic mode.
  arXiv  Detail & Related papers  (2022-12-22T23:32:21Z)
• Retrieving space-dependent polarization transformations via near-optimal quantum process tomography [55.41644538483948]
  We investigate the application of genetic and machine learning approaches to tomographic problems. We find that the neural network-based scheme provides a significant speed-up, that may be critical in applications requiring a characterization in real-time. We expect these results to lay the groundwork for the optimization of tomographic approaches in more general quantum processes.
  arXiv  Detail & Related papers  (2022-10-27T11:37:14Z)
• 'Sawfish' Photonic Crystal Cavity for Near-Unity Emitter-to-Fiber Interfacing in Quantum Network Applications [0.0]
  We develop a waveguide-integrated 'Sawfish' photonic crystal cavity and use finite element simulations to demonstrate that our system transfers, with 97.4% efficiency, the zero-phonon line emission of a negatively-charged tin vacancy center in diamond adiabatically to a single-mode fiber. Our corrugation-based design proves robust under state-of-the-art nanofabrication parameters, maintaining an emitter-to-fiber coupling efficiency of 88.6%.
  arXiv  Detail & Related papers  (2022-10-10T14:00:39Z)
• Topologically Protecting Squeezed Light on a Photonic Chip [58.71663911863411]
  Integrated photonics offers an elegant way to increase the nonlinearity by confining light strictly inside the waveguide. We experimentally demonstrate the topologically protected nonlinear process of spontaneous four-wave mixing enabling the generation of squeezed light on a silica chip.
  arXiv  Detail & Related papers  (2021-06-14T13:39:46Z)
• Zero-power calibration of photonic circuits at cryogenic temperatures [0.0]
  We present a way to reversibly fine-tune the optical properties of individual waveguide structures using solidified xenon. This work paves the way towards the integration of compact, reconfigurable photonic circuits alongside superconducting detectors, devices, or otherwise.
  arXiv  Detail & Related papers  (2021-05-11T00:22:41Z)
• Rapid characterisation of linear-optical networks via PhaseLift [51.03305009278831]
  Integrated photonics offers great phase-stability and can rely on the large scale manufacturability provided by the semiconductor industry. New devices, based on such optical circuits, hold the promise of faster and energy-efficient computations in machine learning applications. We present a novel technique to reconstruct the transfer matrix of linear optical networks.
  arXiv  Detail & Related papers  (2020-10-01T16:04:22Z)
• Inverse-designed photon extractors for optically addressable defect qubits [48.7576911714538]
  Inverse-design optimization of photonic devices enables unprecedented flexibility in tailoring critical parameters of a spin-photon interface. Inverse-designed devices will enable realization of scalable arrays of single-photon emitters, rapid characterization of new quantum emitters, sensing and efficient heralded entanglement schemes.
  arXiv  Detail & Related papers  (2020-07-24T04:30:14Z)
• Near-ideal spontaneous photon sources in silicon quantum photonics [55.41644538483948]
  Integrated photonics is a robust platform for quantum information processing. Sources of single photons that are highly indistinguishable and pure, that are either near-deterministic or heralded with high efficiency, have been elusive. Here, we demonstrate on-chip photon sources that simultaneously meet each of these requirements.
  arXiv  Detail & Related papers  (2020-05-19T16:46:44Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.