arXiv subjects

Hongxiang Guo

Publications and source records attributed to Hongxiang Guo.

6 recordsLinked to original sources

arXiv2026

Non-volatile Programmable Photonic Integrated Circuits using Mechanically Latched MEMS: A System-Level Scheme Enabling Power-Connection-Free Operation Without Performance Compromise

Programmable photonic integrated circuits (PPICs) offer a versatile platform for implementing diverse optical functions on a generic hardware mesh. However, the scalability of PPICs faces critical power consumption barriers. Therefore, we propose a novel non-volatile PPIC architecture utilizing MEMS with mechanical latching, enabling stable passive operation without any power connection once configured. To ensure practical applicability, we present a system-level solution including both this hardware innovation and an accompanying automatic error-resilient configuration algorithm. The algorithm compensates for the lack of continuous tunability inherent in the non-volatile hardware design, thereby enabling such new operational paradigm without compromising performance, and also ensuring robustness against fabrication errors. Functional simulations were performed to validate the proposed scheme by configuring five distinct functionalities of varying complexity, including a Mach-Zehnder interferometer (MZI), a MZI lattice filter, a ring resonator (ORR), a double ORR ring-loaded MZI, and a triple ORR coupled resonator waveguide filter. The results demonstrate that our non-volatile scheme achieves performance equivalent to conventional PPICs. Robustness analysis was also conducted, and the results demonstrated that our scheme exhibits strong robustness against various fabrication errors. Furthermore, we explored the trade-off between the hardware design complexity of such non-volatile scheme and its performance. This study establishes a viable pathway to a new generation of power-connection-free PPICs, providing a practical and scalable solution for future photonic systems.

physics.optics↗

arXiv2024

Characterization of Recirculating Waveguide Meshes Based on an Optimization Method with a Parameter Space Reduction Technology

Fabrication imperfections must be considered during configuration to ensure that the setup is suitable for the actual fabricated programmable photonic integrated circuits (PPICs). Therefore, characterization of imperfections is crucial but difficult, especially for PPICs made from recirculating waveguide meshes. The flexibility required by these meshes demands a more complex topology and compact TBU structure, complicating the characterization. In this paper, we propose a characterization method applicable to recirculating waveguide meshes based on an optimization approach, along with a step-by-step procedure to reduce the parameter space of optimization, allowing for characterizing imperfect parameters of each individual component within the waveguide mesh. To the best of our knowledge, this method can greatly broaden the range of characterized parameters compared to currently reported methods. In order to verify the effectiveness of our method, we used the characterized parameters to build a multi-frequency model of a mesh with fabrication errors and successfully demonstrated accurate prediction of its behavior. Furthermore, we applied our method on implementations of 6 different kind of FIR/IRR filters, to further prove the effectiveness of our method in configuring applications on meshes with fabrication errors. At last, our method was carried out under various scenarios considering beam splitter splitting ratio variance, inaccurate measurements of mesh and imprecise TBU insertion loss characterization, to demonstrate its strong robustness under various practical scenarios.

physics.optics↗

arXiv2023

Integrated photonics modular arithmetic processor

Integrated photonics computing has emerged as a promising approach to overcome the limitations of electronic processors in the post-Moore era, capitalizing on the superiority of photonic systems. However, present integrated photonics computing systems face challenges in achieving high-precision calculations, consequently limiting their potential applications, and their heavy reliance on analog-to-digital (AD) and digital-to-analog (DA) conversion interfaces undermines their performance. Here we propose an innovative photonic computing architecture featuring scalable calculation precision and a novel photonic conversion interface. By leveraging Residue Number System (RNS) theory, the high-precision calculation is decomposed into multiple low-precision modular arithmetic operations executed through optical phase manipulation. Those operations directly interact with the digital system via our proposed optical digital-to-phase converter (ODPC) and phase-to-digital converter (OPDC). Through experimental demonstrations, we showcase a calculation precision of 9 bits and verify the feasibility of the ODPC/OPDC photonic interface. This approach paves the path towards liberating photonic computing from the constraints imposed by limited precision and AD/DA converters.

physics.optics↗

arXiv2022

Novel Architecture of Parameterized Quantum Circuit for Graph Convolutional Network

Recently, the implementation of quantum neural networks is based on noisy intermediate-scale quantum (NISQ) devices. Parameterized quantum circuit (PQC) is such the method, and its current design just can handle linear data classification. However, data in the real world often shows a topological structure. In the machine learning field, the classical graph convolutional layer (GCL)-based graph convolutional network (GCN) can well handle the topological data. Inspired by the architecture of a classical GCN, in this paper, to expand the function of the PQC, we design a novel PQC architecture to realize a quantum GCN (QGCN). More specifically, we first implement an adjacent matrix based on linear combination unitary and a weight matrix in a quantum GCL, and then by stacking multiple GCLs, we obtain the QGCN. In addition, we first achieve gradients decent on quantum circuit following the parameter-shift rule for a GCL and then for the QGCN. We evaluate the performance of the QGCN by conducting a node classification task on Cora dataset with topological data. The numerical simulation result shows that QGCN has the same performance as its classical counterpart, the GCN, in contrast, requires less tunable parameters. Compared to a traditional PQC, we also verify that deploying an extra adjacent matrix can significantly improve the classification performance for quantum topological data.

quant-ph↗

arXiv2020

Quantum Spike Neural Network

Utilizing quantum computers to deploy artificial neural networks (ANNs) will bring the potential of significant advancements in both speed and scale. In this paper, we propose a kind of quantum spike neural networks (SNNs) as well as comprehensively evaluate and give a detailed mathematical proof for the quantum SNNs, including its successful probability, calculation accuracy, and algorithm complexity. The proof shows the quantum SNNs' computational complexity that is log-polynomial in the data dimension. Furthermore, we provide a method to improve quantum SNNs' minimum successful probability to nearly 100%. Finally, we present the good performance of quantum SNNs for solving pattern recognition from the real-world.

quant-ph↗

arXiv2019

An Optimized Quantum Maximum or Minimum Searching Algorithm and its Circuits

Finding a maximum or minimum is a fundamental building block in many mathematical models. Compared with classical algorithms, Durr, Hoyer's quantum algorithm (DHA) achieves quadratic speed. However, its key step, the quantum exponential searching algorithm (QESA), which is based on Grover algorithm, is not a sure-success algorithm. Meanwhile, quantum circuits encounter the gate decomposition problem due to variation of the scale of data. In this paper, we propose an optimized quantum algorithm for searching maximum and minimum, based on DHA and the optimal quantum exact search algorithm. Furthermore, we provide the corresponding quantum circuits, together with three equivalent simplifications. In circumstances when we can exactly estimate the ratio of the number of solutions M and the searched space N, our method can improve the successful probability close to 100%. Furthermore, compared with DHA, our algorithm shows an advantage in complexity with large databases and in the gate complexity of constructing oracles. Experiments have been executed on an IBM superconducting processor with two qubits, and a practical problem of finding the minimum from Titanic passengers' age was numerically simulated. Both showed that our optimized maximum or minimum performs more efficiently compared with DHA. Our algorithm can serve as an important subroutine in various quantum algorithms which involves searching maximum or minimum.

quant-ph↗