Optimization and Noise Analysis of the Quantum Algorithm for Solving One-Dimensional Poisson Equation

Solving differential equations is one of the most promising applications of quantum computing. Recently we proposed an efficient quantum algorithm for solving one-dimensional Poisson equation avoiding the need to perform quantum arithmetic or Hamiltonian simulation. In this letter, we further develop this algorithm to make it closer to the real application on the noisy intermediate-scale quantum (NISQ) devices. To this end, we first develop a new way of performing the sine transformation, and based on it the algorithm is optimized by reducing the depth of the circuit from n2 to n. Then, we analyze the effect of common noise existing in the real quantum devices on our algorithm using the IBM Qiskit toolkit. We find that the phase damping noise has little effect on our algorithm, while the bit flip noise has the greatest impact. In addition, threshold errors of the quantum gates are obtained to make the fidelity of the circuit output being greater than 90%. The results of noise analysis will provide a good guidance for the subsequent work of error mitigation and error correction for our algorithm. The noise-analysis method developed in this work can be used for other algorithms to be executed on the NISQ devices.Comment: 20 pages, 9 figure

Quantum-inspired Complex Convolutional Neural Networks

Quantum-inspired neural network is one of the interesting researches at the junction of the two fields of quantum computing and deep learning. Several models of quantum-inspired neurons with real parameters have been proposed, which are mainly used for three-layer feedforward neural networks. In this work, we improve the quantum-inspired neurons by exploiting the complex-valued weights which have richer representational capacity and better non-linearity. We then extend the method of implementing the quantum-inspired neurons to the convolutional operations, and naturally draw the models of quantum-inspired convolutional neural networks (QICNNs) capable of processing high-dimensional data. Five specific structures of QICNNs are discussed which are different in the way of implementing the convolutional and fully connected layers. The performance of classification accuracy of the five QICNNs are tested on the MNIST and CIFAR-10 datasets. The results show that the QICNNs can perform better in classification accuracy on MNIST dataset than the classical CNN. More learning tasks that our QICNN can outperform the classical counterparts will be found.Comment: 12pages, 6 figure

Black-Box Quantum State Preparation with Inverse Coefficients

Black-box quantum state preparation is a fundamental building block for many higher-level quantum algorithms, which is applied to transduce the data from computational basis into amplitude. Here we present a new algorithm for performing black-box state preparation with inverse coefficients based on the technique of inequality test. This algorithm can be used as a subroutine to perform the controlled rotation stage of the Harrow-Hassidim-Lloyd (HHL) algorithm and the associated matrix inversion algorithms with exceedingly low cost. Furthermore, we extend this approach to address the general black-box state preparation problem where the transduced coefficient is a general non-linear function. The present algorithm greatly relieves the need to do arithmetic and the error is only resulted from the truncated error of binary string. It is expected that our algorithm will find wide usage both in the NISQ and fault-tolerant quantum algorithms.Comment: 11 pages, 3 figure

Hybrid quantum-classical convolutional neural network for phytoplankton classification

The taxonomic composition and abundance of phytoplankton have a direct impact on marine ecosystem dynamics and global environment change. Phytoplankton classification is crucial for phytoplankton analysis, but it is challenging due to their large quantity and small size. Machine learning is the primary method for automatically performing phytoplankton image classification. As large-scale research on marine phytoplankton generates overwhelming amounts of data, more powerful computational resources are required for the success of machine learning methods. Recently, quantum machine learning has emerged as a potential solution for large-scale data processing by harnessing the exponentially computational power of quantum computers. Here, for the first time, we demonstrate the feasibility of using quantum deep neural networks for phytoplankton classification. Hybrid quantum-classical convolutional and residual neural networks are developed based on the classical architectures. These models strike a balance between the limited function of current quantum devices and the large size of phytoplankton images, making it possible to perform phytoplankton classification on near-term quantum computers. Our quantum models demonstrate superior performance compared to their classical counterparts, exhibiting faster convergence, higher classification accuracy and lower accuracy fluctuation. The present quantum models are versatile and can be applied to various tasks of image classification in the field of marine science

Effect of Schooling Behavior on Upstream Migration of Juvenile Grass Carp and Silver Carp

The construction of dams impedes energy exchange and material circulation in rivers, and the operation of hydropower stations negatively impacts the function of river ecosystems. Countries across the globe have implemented various fish protection countermeasures and conducted many associated hydraulic and ecological studies to mitigate the impact of hydropower development on fish survival, maintain the abundance and diversity of fish populations, and restore riverine and lacustrine fish migration routes and habitats. Remarkably, wild fish generally migrate in groups, whereas most contemporary studies on fish passage facilities focus on individual fish. Hence, the behavioral characteristics of fish schools are worth investigating. Given that schooling behavior is a pervasive feature of fish communities and plays an essential role in dealing with potential risks, improving self-adaptation, expanding resilience, and enhancing population sustainability, this study concentrated on investigating its effect on upstream fish migration and decoding the internal mechanism. We conducted an experiment targeting grass carp (Ctenopharyngodon idella) and silver carp (Hypophthalmichthys molitrix), two economically important freshwater fish species in China, on a non-uniform flow ground with low turbulence. The experiment quantified the impact of fish schooling on their ability to overcome flow barriers by examining the ascending sustainability and swimming performance of five-fish groups and one-fish groups at three flow velocity levels (0.25–0.50 m/s, 0.30–0.60 m/s, and 0.35–0.70 m/s) utilizing a novel index system. The new index system employed nondimensionalized ascending sustainability, first-attempt endurance, and first-attempt ascending energy consumption to indicate the fish's persistent ascending ability, ascending efficiency, and ascending energy cost, respectively. Finally, the ascending trajectory of fish was investigated simultaneously to determine the distribution of trajectory and the distribution of the hydrodynamic force factor at the trajectory points. It could be concluded that (1) the influence of schooling behavior on ascending fish behavior was related to the ascending sustainability of individual fish. Schooling behavior significantly increased the ascending sustainability while producing no specific impact on the ascending efficiency at low-level individual ascending sustainability. Inversely, schooling behavior significantly decreased the ascending efficiency while having no particular effect on high-level individual ascending sustainability. Moreover, the contribution of schooling behavior to the ascending sustainability of grass carp varied with the flow velocity, as the ascending sustainability of the five-fish groups was significantly higher (P = 0.030) and lower (P = 0.048) than the one-fish group at the velocity levels of 0.30–0.60 m/s and 0.35–0.70 m/s, respectively. In contrast, schooling behavior holistically improved the upward swimming of silver carp, significantly increasing the ascending sustainability of the five-fish groups at velocity levels of 0.30–0.60 m/s (P = 0.004) and 0.35–0.70 m/s (P < 0.001). (2) The endurance of the first attempt in the juvenile grass carp group was significantly higher than that of the single fish at the velocity levels of 0.25–0.50 m/s (P < 0.001) and 0.35–0.70 m/s (P = 0.005), while the endurance of the first attempt in the juvenile silver carp group was significantly higher than that of the single fish at the velocity levels of 0.25–0.50 m/s (P < 0.001) and 0.30–0.60 m/s (P = 0.005). The endurance of the first attempt in juvenile silver carp decreased significantly in schools (P < 0.001) solely at the velocity level of 0.35–0.70 m/s. In addition, the flow velocity generally increased the first-attempt endurance and cumulative energy consumption of individual and grouped fish. However, since the burst-coast swimming mode forced on juvenile silver carp in high-velocity conditions significantly improved ascending efficiency, the endurance of the first attempt initially increased and then decreased with the flow velocity. (3) Schooling behavior enabled grass carp to swim with less energy and significantly lowered their energy cost at the velocity level of 0.25–0.50 m/s (P < 0.001), whereas it augmented the accumulated energy consumption of five-fish groups in silver carp and significantly increased their energy cost at the velocity level of 0.25–0.50 m/s (P = 0.050). (4) Collectively, juvenile silver carp could find an ideal ascending trajectory more rapidly than juvenile grass carp. The ascending trajectory of grass carp tended to concentrate at first and then disperse with increasing velocity, whereas the trajectory of silver carp tended to concentrate with increasing velocity. In brief, schooling is an unsubstituted behavior in the upstream migration of fish migrants, motivating and inhibiting the fish's upstream movement performance with its primary effect on locomotion in terms of energy consumption, visual response, and the ability to overcome flow barriers. The findings could improve the design of fish protection measures and provide specific recommendations for the operation of fish passage facilities. For example, when silver carp migrate through a fishway, the additional light source can adequately promote schooling behavior and improve their capacity to overcome flow barriers. When grass carp migrate through a fish passage, the additional light source in the rest pond of the fishway can effectively promote schooling behavior, thereby preventing the return of fish owing to a lack of ascending sustainability. Considering that the hydrodynamic environment of natural waters and the size of fish groups are highly complex and variable, the schooling behavior characteristics of fish groups in different water flow environments should be investigated in future research to enrich the database for the planning and implementation of fish protection engineering measures

DataSheet_1_Hybrid quantum-classical convolutional neural network for phytoplankton classification.docx

The taxonomic composition and abundance of phytoplankton have a direct impact on marine ecosystem dynamics and global environment change. Phytoplankton classification is crucial for phytoplankton analysis, but it is challenging due to their large quantity and small size. Machine learning is the primary method for automatically performing phytoplankton image classification. As large-scale research on marine phytoplankton generates overwhelming amounts of data, more powerful computational resources are required for the success of machine learning methods. Recently, quantum machine learning has emerged as a potential solution for large-scale data processing by harnessing the exponentially computational power of quantum computers. Here, for the first time, we demonstrate the feasibility of using quantum deep neural networks for phytoplankton classification. Hybrid quantum-classical convolutional and residual neural networks are developed based on the classical architectures. These models strike a balance between the limited function of current quantum devices and the large size of phytoplankton images, making it possible to perform phytoplankton classification on near-term quantum computers. Our quantum models demonstrate superior performance compared to their classical counterparts, exhibiting faster convergence, higher classification accuracy and lower accuracy fluctuation. The present quantum models are versatile and can be applied to various tasks of image classification in the field of marine science.</p

Achieving ideal transistor characteristics in conjugated polymer semiconductors.

Organic thin-film transistors (OTFTs) with ideal behavior are highly desired, because nonideal devices may overestimate the intrinsic property and yield inferior performance in applications. In reality, most polymer OTFTs reported in the literature do not exhibit ideal characteristics. Supported by a structure-property relationship study of several low-disorder conjugated polymers, here, we present an empirical selection rule for polymer candidates for textbook-like OTFTs with high reliability factors (100% for ideal transistors). The successful candidates should have low energetic disorder along their backbones and form thin films with spatially uniform energetic landscapes. We demonstrate that these requirements are satisfied in the semicrystalline polymer PffBT4T-2DT, which exhibits a reliability factor (~100%) that is exceptionally high for polymer devices, rendering it an ideal candidate for OTFT applications. Our findings broaden the selection of polymer semiconductors with textbook-like OTFT characteristics and would shed light upon the molecular design criteria for next-generation polymer semiconductors.EPSRC, ER