The data reuploading trick was originally proposed for quantum computing to achieve the universal approximation property. In this paper, we introduce data reuploading to realize universal non-quantum photonic computing with practical photonic integrated circuits (PICs). We aim to comprehensively discuss the various advantages and implementation considerations of this approach. Our framework can eliminate the need of quantum squeezed lights, photon counters, and nonlinear photonics, which have been essential for enabling photonic neural networks in conventional configurations. Additionally, we explore ways to minimize the optical components by combining multiple functionalities into a single phase shifter, showing competitive performance when compared to using the same number of phase shifters, all without employing any nonlinear photonic devices. Considering these characteristics, our investigation into the use of PICs for data reuploading presents a novel architectural approach to realize photonic neural networks. This approach embodies unique features that distinctly set it apart from traditional photonic neural networks.
The data reuploading trick was originally proposed for quantum computing to achieve the universal approximation property. In this paper, we introduce data reuploading to realize universal non-quantum photonic computing with practical photonic integrated circuits (PICs). We aim to comprehensively discuss the various advantages and implementation considerations of this approach. Our framework can eliminate the need of quantum squeezed lights, photon counters, and nonlinear photonics, which have been essential for enabling photonic neural networks in conventional configurations. Additionally, we explore ways to minimize the optical components by combining multiple functionalities into a single phase shifter, showing competitive performance when compared to using the same number of phase shifters, all without employing any nonlinear photonic devices. Considering these characteristics, our investigation into the use of PICs for data reuploading presents a novel architectural approach to realize photonic neural networks. This approach embodies unique features that distinctly set it apart from traditional photonic neural networks.
The data reuploading trick, originally proposed for universal quantum computing, enables the universal approximation property. We recently extended this concept to achieve universal non-quantum photonic computing using practical photonic integrated circuits (PICs) composed solely of 50:50 beam splitters and phase shifters, eliminating the need for nonlinear photonic devices. In this approach, input data are repeatedly embedded as rotation angles. In this presentation, we explore strategies to enhance the performance of this method by increasing the number of layers and optical channels (lanes) in various configurations. For a classical two-dimensional, four-class classification problem of wavy lines, we evaluated a two-mode class-embedding circuit with output, a four-mode configuration, and four stacking methods of two-mode circuits with average pooling, alongside a baseline configuration using projection in a complex domain. The first three configurations demonstrated excellent accuracy. Additionally, we investigated the effect of shifting the order of input data layer by layer, which significantly improved performance in certain applications. These findings highlight a novel architectural approach to photonic neural networks enabled by data reuploading in PICs. This approach offers unique features that set it apart from traditional photonic
Reconfigurable photonic integrated circuits (PICs) for quantum optical information processing are now routinely made in foundries with hundreds or thousands of components. Such PICs rely on linear optics, meaning that all photon-photon interactions are probabilistic, and hence that complex protocols that, for example, produce entanglement or graph states do so at low rates. Adding optical nonlinearities to the circuits provides an activation function analogous to that of classical neural networks, enabling deterministic photon-photon protocols in ideal circuits. Here, we discuss the next step: quantum photonic neural networks made from more realistic – i.e. imperfect – components. We show that these are capable of near-deterministic entanglement generation, when imperfections such as losses, imperfect routing and sub-optimal nonlinearities are balanced against network size. These results provide a design for real quantum photonic neural networks and demonstrate that they may play important roles in emerging quantum technologies.
In a single qubit system, a universal quantum classifier can be realized using the data reuploading technique. In this study, we propose a new quantum classifier applying this technique to bosonic systems and successfully demonstrate it using a silicon-based photonic integrated circuit. We established a theory of quantum machine learning algorithm applicable to bosonic systems and implemented a programmable optical circuit combined with an interferometer. Learning and classification using part of the implemented optical quantum circuit with uncorrelated two photons resulted in a classification with a success probability of 94±0.8% in the proof of principle experiment. As this method can be applied to an arbitrary two-mode N-photon system, further development of optical quantum classifiers, such as extensions to quantum entangled and multiphoton states, is expected in the future.
Semiconductor quantum dots (QDs) are a type of solid-state quantum emitter that can act as a near-ideal quantum light-matter interface when integrated with high-quality nanophotonic systems. Though QDs have typically been used to create state-of-the-art, on-demand single photon sources, here we widen the perspective on QDs, showing how to design quantum photonic integrated circuits based on both linear and nonlinear QD phase shifters. Specifically, we find that linear QD phase shifters can be used to realize cryogenically-compatible, fast, low-loss, and high-fidelity reconfigurable linear circuits. When paired with QDs that mediate interactions between photonic qubits, generating nonlinear phase shifts, deterministic quantum photonic logic gates can be achieved. Thus, our work paves the way for the realization of on-chip, cryogenically-compatible linear and nonlinear quantum photonic circuits, including quantum photonic neural networks, which can form the foundation for scalable and efficient quantum photonic technologies.
We introduce the architecture and timing algorithm to realize a time-bin-encoded quantum photonic neural network (QPNN): a reconfigurable nonlinear photonic circuit inspired by the brain and trained to process quantum information. Unlike the typical spatially-encoded QPNN, time-encoded networks require the same number of photonic elements (e.g. phase shifters or switches) regardless of their size or depth. Here, we present a model of such a network and show how to include imperfections such as losses, routing errors and most notably distinguishable photons. As an example, we train the QPNN to realize a controlled-NOT gate, based on a hypothetical ideal Kerr nonlinearity. We then extend our model to a realistic two-photon nonlinearity due to scattering from a single, semiconductor quantum dot coupled to a photonic waveguide. We show that, using this realistic nonlinearity, the QPNN can be trained to act as a Bell-state analyzer which operates with a fidelity of 0.96 and at a rate only limited by losses. We further show that time gating can raise this fidelity to over 0.99, while still maintaining an efficiency exceeding 0.9. Overall, this work lays a framework for the first QPNN encoded in time, and provides a clear path to the scaling of these networks.
Integrated photonic chips leverage the recent developments in integrated circuit technology, along with the control and manipulation of light signals, to realize the integration of multiple optical components onto a single chip. By exploiting the power of light, integrated photonic chips offer numerous advantages over traditional optical and electronic systems, including miniaturization, high-speed data processing and improved energy efficiency. In this review, we survey the current status of quantum computation, optical neural networks and the realization of some algorithms on integrated optical chips.