Multi-task learning with quantum reservoir computing

With the rapid development of experimental technology, a large degree of controllability has been available in a broad spectrum of noise-intermediate-scale quantum (NISQ) devices. To take full advantage of the computational power of those NISQ devices, we study the potential quantum advantage of quantum reservoir computing and the learning power of specially designed quantum reservoirs. Here we construct the classical and quantum reservoirs and contrast their computational capacities to demonstrate the potential quantum advantage. Furthermore, we study the learning power of the designed quantum reservoir. We find the designed quantum reservoir can simultaneously learn different tasks: a synthetic oscillatory network of transcriptional regulators, chaotic motifs in gene regulatory networks, and fractional-order Chua's circuit with a memristor. Except for those artificial systems, we also investigate the real-world problem, the exchange rate prediction. The quantum reservoir has acquired outstanding performance on the above tasks.