arXiv:2411.03979 · Physical Review X

Harnessing Quantum Backaction for Time-Series Processing

Giacomo Franceschetto, Marcin Płodzień, Maciej Lewenstein, Antonio Acín, Pere Mujal

Published Feb 11, 2026• 1 citation

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.

DOI: 10.1103/j7f9-hfsj

Related papers

General theory of monitored Quantum Reservoir Computing

Oriol Morguí-Sancho, Gonzalo Manzano, Gian Luca Giorgi +1 more

Quantum reservoir computing (QRC) provides a powerful framework for processing temporal data using quantum dynamics, but incorporating measurements into the reservoir remains a fundamental challenge and distinctive feature with respect to classical settings. The induced back-action can vary from a source of disturbance to a computational resource, as measurement deeply modifies the dynamics underlying temporal processing. Existing approaches have treated specific monitoring schemes independently, missing the common physical principles governing online quantum reservoirs. Here we develop a general theory of monitored quantum reservoir computing based on indirect quantum measurements, which unifies projective, weak, partial, and dissipative monitoring protocols within a single operational framework. Measurement back-action can serve as a controllable resource, providing the effective dissipation and non-unital dynamics required for successful QRC, even when the underlying unmonitored evolution is unsuitable. We derive general criteria under which monitored dynamics satisfy the echo-state property, fading memory, and input separability, including a necessary and sufficient condition for emergent strict contractivity. By comparing different monitoring schemes under a common reference dynamics, we show that these protocols are not interchangeable parameterizations to be optimized for peak performance, but rather constitute qualitatively distinct routes to computational capability, each enabled by the interplay between information extraction and measurement-induced disturbance -- a trade-off that can be further shaped through time multiplexing. Our results provide a unified theoretical foundation for online monitored quantum reservoir computing and establish quantum measurement engineering as a systematic approach for designing reservoir architectures across different quantum platforms.

quant-pharXiv:2607.14311Jul 17, 20260 citations

Measurement-enabled online quantum processing with amplitude encoding

Giacomo Franceschetto, Pere Mujal, Rodrigo Martínez-Peña

We introduce a quantum reservoir computing online protocol that realizes amplitude encoding on quantum hardware. Our scheme combines mid-circuit measurement and reset operations to implement the partial-trace dynamics underlying amplitude encoding, while an indirect measurement scheme provides access to reservoir observables without interrupting temporal processing. In contrast to other approaches, our method preserves online operation, avoids input buffering, and keeps the runtime linear in the number of time steps. We present the theoretical formulation of the protocol and a proof-of-principle implementation on quantum hardware, and we evaluate its performance on two standard benchmark tasks. Our results show that the reservoir dynamics can be monitored through both direct measurements of the input qubits and indirect measurements of the memory qubits, enabling observation of the full system while isolating the internal evolution of the reservoir. This work provides a practical route toward scalable hardware implementations of amplitude-encoded quantum reservoir computing and opens the door to systematic experimental studies of complex quantum reservoirs.

quant-pharXiv:2606.18991Jun 18, 20260 citations

Feedback-Driven Quantum Reservoir Computing for Time-Series Analysis

Kaito Kobayashi, Keisuke Fujii, Naoki Yamamoto

Quantum reservoir computing (QRC) is a highly promising computational paradigm that leverages quantum systems as a computational resource for nonlinear information processing. While its application to time-series analysis is eagerly anticipated, prevailing approaches suffer from the collapse of the quantum state upon measurement, resulting in the erasure of temporal input memories. Neither repeated initializations nor weak measurements offer a fundamental solution, as the former escalates the time complexity while the latter restricts the information extraction from the Hilbert space. To address this issue, we propose the feedback-driven QRC framework. This methodology employs projective measurements on all qubits for unrestricted access to the quantum state, with the measurement outcomes subsequently fed back into the reservoir to restore the memory of prior inputs. We demonstrate that our QRC successfully acquires the fading-memory property through the feedback connections, a critical aspect in time-series processing. Notably, analysis of measurement trajectories reveals three distinct phases depending on the feedback strength, with the memory performance maximized at the edge of chaos. We also evaluate the predictive capabilities of our QRC, demonstrating its suitability for forecasting signals originating from quantum spin systems. Published by the American Physical Society 2024

arXiv:2406.15783PRX QuantumNov 14, 202439 citations

Time-series quantum reservoir computing with weak and projective measurements

Pere Mujal, Rodrigo Martínez‐Peña, Gian Luca Giorgi +2 more

Abstract Time-series processing is a major challenge in machine learning with enormous progress in the last years in tasks such as speech recognition and chaotic series prediction. A promising avenue for sequential data analysis is quantum machine learning, with computational models like quantum neural networks and reservoir computing. An open question is how to efficiently include quantum measurement in realistic protocols while retaining the needed processing memory and preserving the quantum advantage offered by large Hilbert spaces. In this work, we propose different measurement protocols and assess their efficiency in terms of resources, through theoretical predictions and numerical analysis. We show that it is possible to exploit the quantumness of the reservoir and to obtain ideal performance both for memory and forecasting tasks with two successful measurement protocols. One repeats part of the experiment after each projective measurement while the other employs weak measurements operating online at the trade-off where information can be extracted accurately and without hindering the needed memory, in spite of back-action effects. Our work establishes the conditions for efficient time-series processing paving the way to its implementation in different quantum technologies.

arXiv:2205.06809npj Quantum InformationFeb 23, 202392 citations

Temporal processing of quantum states with hybrid quantum-classical reservoirs

Mateu Coll-Comas, Gian Luca Giorgi, Roberta Zambrini

A distinctive feature of Quantum Reservoir Computing (QRC) is the ability to directly embed quantum input states into the reservoir dynamics. However, the resulting output is fundamentally linear for a single input state, preventing QRC from naturally computing nonlinear functionals such as purity or entropy. We overcome this limitation with a quantum-classical hybrid architecture combining a qubit quantum reservoir with a classical echo state network (ESN), allowing both nonlinear functional approximation and effective temporal processing. We systematically study performance under two information regimes: full-tomography and partial information (single-axis measurements), with the latter demonstrating that the hybrid system outperforms its standalone components in both linear and nonlinear tasks due to the enhanced information retrieval provided by the quantum reservoir. Building on these results, we apply an online monitoring protocol that explicitly accounts for measurement back-action and finite measurement ensembles, enabling a realistic assessment of performance under experimental conditions. These results establish hybrid quantum-classical reservoir computing (HRC) architectures as a practical and scalable route for enhanced quantum machine learning on near-term qubit hardware.

quant-pharXiv:2606.21327Jun 22, 20260 citations

Quantum reservoir computing with repeated measurements on superconducting devices

Toshiki Yasuda, Yudai Suzuki, Tomoyuki Kubota +8 more

Reservoir computing is a machine learning framework that uses artificial or physical dissipative dynamics to predict time-series data using nonlinearity and memory properties of dynamical systems. Quantum systems are considered promising physical reservoirs, and several quantum reservoir computing (QRC) schemes that exploit intrinsic quantum mechanical properties as computational resources have been proposed. This paper focuses on a particular QRC scheme that was theoretically proposed to use the repeated quantum non-demolition measurement to generate a time series, mainly for the purpose of reducing the total execution time compared to the conventional method without repeated measurement. We experimentally implemented this scheme on IBM superconducting devices and demonstrated that shorter execution time and higher accuracy can be achieved than with a specific conventional method. Then, we study the temporal information processing capacity to quantify the computational capability of the proposed QRC; in particular, we use this quantity to identify the measurement strength that finds the best balance between the amount of available information and the strength of dissipation. An experimental demonstration with a soft robot is also provided, where repeated measurement over 1,000 timesteps was effectively implemented. Finally, we implemented the repeated-measurement QRC scheme using 120 qubits, to test the potential computational power of a large-scale QRC.

arXiv:2310.06706Oct 10, 20237 citations