Quantum-Classical Hybrid Information Processing via a Single Quantum System
This work addresses the need for flexible hybrid quantum-classical processing in near-term applications, offering a novel method for multitasking but is incremental in building on existing quantum reservoir computing frameworks.
The authors tackled the problem of integrating quantum and classical data processing by proposing a quantum reservoir processor that uses quantum dots to handle both types of inputs, enabling multitasking applications such as quantum tomography and nonlinear equalization of classical channels, with results including closed-loop tomography and preparation of quantum depolarizing channels as a novel technique.
Current technologies in quantum-based communications bring a new integration of quantum data with classical data for hybrid processing. However, the frameworks of these technologies are restricted to a single classical or quantum task, which limits their flexibility in near-term applications. We propose a quantum reservoir processor to harness quantum dynamics in computational tasks requiring both classical and quantum inputs. This analog processor comprises a network of quantum dots in which quantum data is incident to the network and classical data is encoded via a coherent field exciting the network. We perform a multitasking application of quantum tomography and nonlinear equalization of classical channels. Interestingly, the tomography can be performed in a closed-loop manner via the feedback control of classical data. Therefore, if the classical input comes from a dynamical system, embedding this system in a closed loop enables hybrid processing even if access to the external classical input is interrupted. Finally, we demonstrate preparing quantum depolarizing channels as a novel quantum machine learning technique for quantum data processing.