# Dissipation alters modes of information encoding in small quantum reservoirs near criticality

arXiv: https://arxiv.org/abs/2412.18290 · HTML: https://mlsanity.com/paper/2412.18290/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 66 references that are in the map.
- First posted on arXiv 24 Dec 2024.

## Builds on (most cited first, 6 of 6)

- [Quantifying High-order Interdependencies via Multivariate Extensions of the Mutual Information](https://mlsanity.com/paper/1902.11239/) (2019)
- [On fundamental aspects of quantum extreme learning machines](https://mlsanity.com/paper/2312.15124/) (2023)
- [Partial Information Decomposition as a Unified Approach to the Specification of Neural Goal Functions](https://mlsanity.com/paper/1510.00831/) (2015)
- [Harnessing disordered quantum dynamics for machine learning](https://mlsanity.com/paper/1602.08159/) (2016)
- [BROJA-2PID: A robust estimator for bivariate partial information decomposition](https://mlsanity.com/paper/1802.02485/) (2018)
- [Quantum Next Generation Reservoir Computing: An Efficient Quantum Algorithm for Forecasting Quantum Dynamics](https://mlsanity.com/paper/2308.14239/) (2023)

## Built on by (most cited first, 3 of 3)

- [Role of scrambling and noise in temporal information processing with quantum systems](https://mlsanity.com/paper/2505.10080/) (2025)
- [The Role of Entanglement in Quantum Reservoir Computing with Coupled Kerr Nonlinear Oscillators](https://mlsanity.com/paper/2508.11175/) (2025)
- [Random matrix theory of sparse neuronal networks with heterogeneous timescales](https://mlsanity.com/paper/2512.12767/) (2025)

## Abstract (arXiv)

Quantum reservoir computing (QRC) has emerged as a promising paradigm for harnessing near-term quantum devices to tackle temporal machine learning tasks. Yet identifying the mechanisms that underlie enhanced performance remains challenging, particularly in many-body open systems where nonlinear interactions and dissipation intertwine in complex ways. Here, we investigate a minimal model of a driven-dissipative quantum reservoir described by two coupled Kerr-nonlinear oscillators, an experimentally realizable platform that features controllable coupling, intrinsic nonlinearity, and tunable photon loss. Using Partial Information Decomposition (PID), we examine how different dynamical regimes encode input drive signals in terms of redundancy (information shared by each oscillator) and synergy (information accessible only through their joint observation). Our key results show that, near a critical point marking a dynamical bifurcation, the system transitions from predominantly redundant to synergistic encoding. We further demonstrate that synergy amplifies short-term responsiveness, thereby enhancing immediate memory retention, whereas strong dissipation leads to more redundant encoding that supports long-term memory retention. These findings elucidate how the interplay of instability and dissipation shapes information processing in small quantum systems, providing a fine-grained, information-theoretic perspective for analyzing and designing QRC platforms.