Reservoir computing (RC) is a neuromorphic machine learning paradigm that is ideal for temporal signal processing and is suitable for analog implementations in physical substrates, including photonic devices. RC has been extended to quantum systems due to the enhanced capabilities provided by an enlarged Hilbert space. In that regard, quantum reservoir computing (QRC) has the advantage of avoiding barren plateaus during training. Photonic architectures have already been studied for QRC applications. In our research, we propose a scalable quantum photonic platform for QRC that is suitable for solving temporal tasks. The physical substrate of our reservoir is an optical pulse, which recirculates through an optical cavity with losses, thus creating a quantum memory. The dissipation device (a beam-splitter) also allows the injection of external information and the weak monitoring of the reservoir. Our work focuses on the ability to process classical signals in real time by creating a physical ensemble of identical pulses inside a fiber and the noise robustness of our architecture by tuning the squeezing produced inside the optical cavity.
Photonic quantum technologies are noteworthy candidates in the achievement of quantum advantage for quantum information processing. Moreover, their capabilities for fast signal processing have attracted the interest of researchers in the field of quantum reservoir computing (QRC). In our research, we propose a scalable quantum photonic platform for QRC suitable for solving temporal tasks. In our platform, an optical pulse recirculating through an optical cavity creates a quantum memory, thus not needing external classical storage. A classical signal is sequentially encoded in the quantum field fluctuations of external optical pulses, which interact with the cavity pulse using a beam-splitter (BS). A nonlinear crystal is placed inside the cavity to generate non-trivial dynamics and create a quantum network of entangled modes. A homodyne detector is placed at one of the output paths of the BS for sequential data collecting. Our work focuses on the ability to process classical signals in real time and the noise robustness of our architecture.
Quantum reservoir computing is an unconventional computing approach that exploits the quantumness of physical systems used as reservoirs to process information, combined with an easy training strategy. An overview is presented about a range of possibilities including quantum inputs, quantum physical substrates and quantum tasks. Recently, the framework of quantum reservoir computing has been proposed using Gaussian quantum states that can be realized e.g. in linear quantum optical systems. The universality and versatility of the system makes it particularly interesting for optical implementations. In particular, full potential of the proposed model can be reached even by encoding into quantum fluctuations, such as squeezed vacuum, instead of classical intense fields or thermal fluctuations. Some examples of the performance of this linear quantum reservoir in temporal tasks are reported.
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