Versatile Quantum Machine Learning with an Ultra-low Power Photonic Quantum Reservoir Computer

Abstract

Integrated photonic microprocessors provide high-bandwidth, massively parallel linear computation, but realizing nonlinear feature maps and temporal memory remain key challenges for machine learning. Conventional approaches rely on active tuning and additional nonlinear elements, increasing architectural complexity and power overhead. Here we demonstrate an integrated photonic quantum reservoir computer that achieves nonlinear mapping, fading memory, and task versatility without active tuning of the reservoir core. The same chip supports accurate static classification, dynamic prediction, and stable autonomous forecasting, establishing broad utility across both classification and temporal inference tasks. Competitive performance is retained in the zero-bias state, where all on-chip phase shifters are unpowered, eliminating active control and reducing computational power consumption to zero. This passive operation highlights a scalable route to multifunctional machine-learning hardware, where large-scale photonic quantum processors can be repurposed as reservoirs without reconfiguring their internal optical networks. By combining quantum-state encoding, multimode interferometric mixing, and photon-statistical readout, this architecture provides a physically grounded paradigm for low-power, large-scale quantum reservoir computing.

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BibTeX RIS

BibTeX

@misc{wang2026versatile,
  title = {{Versatile Quantum Machine Learning with an Ultra-low Power Photonic Quantum Reservoir Computer}},
  author = {Wei Wang and Zan Tang and Menglong Fang and Daiqin Su and Mile Gu and Jayne Thompson and Lip Ket Chin and Hong Cai and Leong-Chuan Kwek and Ai-Qun Liu},
  howpublished = {arXiv},
  year = {2026},
  month = sep,
  doi = {10.48550/arxiv.2609.24381},
  eprint = {2609.24381},
  archivePrefix = {arXiv},
  url = {https://arxiv.org/abs/2609.24381},
}

RIS

TY  - UNPB
TI  - Versatile Quantum Machine Learning with an Ultra-low Power Photonic Quantum Reservoir Computer
AU  - Wei Wang
AU  - Zan Tang
AU  - Menglong Fang
AU  - Daiqin Su
AU  - Mile Gu
AU  - Jayne Thompson
AU  - Lip Ket Chin
AU  - Hong Cai
AU  - Leong-Chuan Kwek
AU  - Ai-Qun Liu
PY  - 2026
DA  - 2026/09/21
PB  - arXiv
DO  - 10.48550/arxiv.2609.24381
UR  - https://arxiv.org/abs/2609.24381
AB  - Integrated photonic microprocessors provide high-bandwidth, massively parallel linear computation, but realizing nonlinear feature maps and temporal memory remain key challenges for machine learning. Conventional approaches rely on active tuning and additional nonlinear elements, increasing architectural complexity and power overhead. Here we demonstrate an integrated photonic quantum reservoir computer that achieves nonlinear mapping, fading memory, and task versatility without active tuning of the reservoir core. The same chip supports accurate static classification, dynamic prediction, and stable autonomous forecasting, establishing broad utility across both classification and temporal inference tasks. Competitive performance is retained in the zero-bias state, where all on-chip phase shifters are unpowered, eliminating active control and reducing computational power consumption to zero. This passive operation highlights a scalable route to multifunctional machine-learning hardware, where large-scale photonic quantum processors can be repurposed as reservoirs without reconfiguring their internal optical networks. By combining quantum-state encoding, multimode interferometric mixing, and photon-statistical readout, this architecture provides a physically grounded paradigm for low-power, large-scale quantum reservoir computing.
ER  -

Abstract

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