<ul><li>Quantum reservoir computing (QRC) is a promising paradigm for utilizing near-term quantum devices in temporal machine learning tasks.</li><li>A study investigates a minimal model of a driven-dissipative quantum reservoir consisting of two coupled Kerr-nonlinear oscillators.</li><li>Using Partial Information Decomposition (PID), the researchers analyze how different dynamical regimes encode input drive signals.</li><li>The results reveal a transition from redundant to synergistic encoding near a critical point, with synergy enhancing short-term responsiveness and dissipation supporting long-term memory retention.</li></ul>

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

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