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  • Open Access

Harnessing Quantum Backaction for Time-Series Processing

Giacomo Franceschetto1,*, Marcin Płodzień1,‡, Maciej Lewenstein1,2, Antonio Acín1,2, and Pere Mujal1,†

  • *Contact author: giacomo.franceschetto@icfo.eu
  • †Contact author: pere.mujal@alumni.icfo.eu
  • ‡Present address: Qilimanjaro Quantum Tech, Carrer de Veneçuela 74, 08019 Barcelona, Spain
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Phys. Rev. X 16, 021002 – Published 2 April, 2026

DOI: https://doi.org/10.1103/j7f9-hfsj

Abstract

Quantum measurements affect the state of the observed systems via backaction. While projective measurements extract maximal classical information, they drastically alter the system. In contrast, indirect measurements balance information extraction with the degree of disturbance. Considering the prevalent use of projective measurements in quantum computing and communication protocols, the potential benefits of indirect measurements in these fields remain largely unexplored. In this work, we show that measurement backaction, a purely quantum effect, can be harnessed within the quantum reservoir computing framework to enhance temporal data processing. Incorporating indirect measurements leads to improved execution-time scaling and overall performance. Our results reveal that carefully optimizing the measurement strength can significantly improve the quantum reservoir computing algorithm performance. Furthermore, our approach demonstrates improved memory performance when compared with state-of-the-art classical-feedback protocols. This work provides a comprehensive and practical recipe to promote the implementation of indirect-measurement-based protocols in quantum reservoir computing. Moreover, our findings motivate further exploration of experimental protocols that leverage the backaction effects of indirect measurements.

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Popular Summary

Measurements on quantum systems inherently influence their state. This effect, known as quantum backaction, may act as a disturbance, potentially erasing the fragile features that make quantum physics powerful. Instead, we show that this uniquely quantum effect can be harnessed as a computational resource in quantum reservoir computing, a machine-learning approach for time-dependent data. Indirect measurements, in contrast to conventional projective measurements, allow us to balance how much information we gain against how much disturbance we introduce. Remarkably, they also reshape the system’s dynamics with a controlled backaction that strengthens memory and improves the prediction of complex signals. Moreover, the computational performance enabled by this controlled reshaping, present in our protocol, cannot be matched by strategies that merely avoid measurement effects or attempt to correct them with classical resources. More broadly, this shift in perspective demonstrates that backaction due to quantum measurements, long viewed as a limitation, can instead become a practical and powerful resource for near-term quantum algorithms.

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Phys. Rev. X 16, 021002– Published 2 April, 2026
  • Received 18 December 2024
  • Revised 12 December 2025
  • Accepted 11 February 2026
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