Reservoir computing (RC), a neural network designed for temporal data, enables efficient computation with low-cost training and direct physical implementation. Recently, quantum RC has opened possibilities for conventional RC and introduced other ideas to tackle open problems in quantum physics and advance quantum technologies. Despite its promise, it faces challenges, including physical realization, output readout, and measurement-induced backaction. Here, we propose to implement quantum RC through quantum transport in mesoscopic electronic systems. Our approach possesses several advantages: compatibility with existing device-fabrication techniques, ease of output measurement, and robustness against measurement backaction. Leveraging universal conductance fluctuations, we numerically demonstrate two benchmark tasks, spoken-digit recognition and time-series forecasting, to validate our proposal. This work establishes another pathway for implementing on-chip quantum RC via quantum transport and expands the mesoscopic physics applications.

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• Analog computation
• Mesoscopics
• Physics of computation
• Quantum fluctuations & noise
• Quantum interference effects
• Quantum transport
• Artificial neural networks
• S-matrix method in transport