Squeezing Operator Research Articles

Multi-task OCTA image segmentation with innovative dimension compression

Optical Coherence Tomography Angiography (OCTA) plays a crucial role in the early detection and continuous monitoring of ocular diseases, which relies on accurate multi-tissue segmentation of retinal images. Existing OCTA segmentation methods typically focus on single-task designs that do not fully utilize the information of volume data in these images. To bridge this gap, our study introduces H2C-Net, a novel network architecture engineered for simultaneous and precise segmentation of various retinal structures, including capillaries, arteries, veins, and the fovea avascular zone (FAZ). At its core, H2C-Net consists of a plug-and-play Height-Channel Module (H2C) and an Enhanced U-shaped Network (GPC-Net). The H2C module cleverly converts the height information of the OCTA volume data into channel information through the Squeeze operation, realizes the lossless dimensionality reduction from 3D to 2D, and provides the "Soft layering" information by unidirectional pooling. Meanwhile, in order to guide the network to focus on channels for training, U-Net is enhanced with group normalization, channel attention mechanism, and Parametric Rectified Linear Unit (PReLU), which reduces the dependence on batch size and enhances the network's ability to extract salient features. Extensive experiments on two subsets of the publicly available OCTA-500 dataset have shown that H2C-Net outperforms existing state-of-the-art methods. It achieves average Intersection over Union (IoU) scores of 82.84 % and 88.48 %, marking improvements of 0.81 % and 1.59 %, respectively. Similarly, the average Dice scores are elevated to 90.40 % and 93.76 %, exceeding previous benchmarks by 0.42 % and 0.94 %. The proposed H2C-Net exhibits excellent performance in OCTA image segmentation, providing an efficient and accurate multi-task segmentation solution in ophthalmic diagnostics. The code is publicly available at: https://github.com/IAAI-SIT/H2C-Net.

Click-through rate prediction model based on graph networks and feature squeeze-and-excitation mechanism

PurposePredicting a user’s click-through rate on an advertisement or item often uses deep learning methods to mine hidden information in data features, which can provide users with more accurate personalized recommendations. However, existing works usually ignore the problem that the drift of user interests may lead to the generation of new features when they compute feature interactions. Based on this, this paper aims to design a model to address this issue.Design/methodology/approachFirst, the authors use graph neural networks to model users’ interest relationships, using the existing user features as the node features of the graph neural networks. Second, through the squeeze-and-excitation network mechanism, the user features and item features are subjected to squeeze operation and excitation operation, respectively, and the importance of the features is adaptively adjusted by learning the channel weights of the features. Finally, the feature space is divided into multiple subspaces to allocate features to different models, which can improve the performance of the model.FindingsThe authors conduct experiments on two real-world data sets, and the results show that the model can effectively improve the prediction accuracy of advertisement or item click events.Originality/valueIn the study, the authors propose graph network and feature squeeze-and-excitation model for click-through rate prediction, which is used to dynamically learn the importance of features. The results indicate the effectiveness of the model.

Instantaneous Frequency Extraction for Nonstationary Signals via a Squeezing Operator with a Fixed-Point Iteration Method

Instantaneous Frequency Extraction for Nonstationary Signals via a Squeezing Operator with a Fixed-Point Iteration Method

Universal control of a bosonic mode via drive-activated native cubic interactions

Linear bosonic modes offer a hardware-efficient alternative for quantum information processing but require access to some nonlinearity for universal control. The lack of nonlinearity in photonics has led to encoded measurement-based quantum computing, which relies on linear operations but requires access to resourceful (’nonlinear’) quantum states, such as cubic phase states. In contrast, superconducting microwave circuits offer engineerable nonlinearities but suffer from static Kerr nonlinearity. Here, we demonstrate universal control of a bosonic mode composed of a superconducting nonlinear asymmetric inductive element (SNAIL) resonator, enabled by native nonlinearities in the SNAIL element. We suppress static nonlinearities by operating the SNAIL in the vicinity of its Kerr-free point and dynamically activate nonlinearities up to third order by fast flux pulses. We experimentally realize a universal set of generalized squeezing operations, as well as the cubic phase gate, and exploit them to deterministically prepare a cubic phase state in 60 ns. Our results initiate the experimental field of polynomial quantum computing, in the continuous-variables notion originally introduced by Lloyd and Braunstein.

From Wigner hyperbolic rotation to fractional squeezing transformation

Based on the usual Wigner-Weyl transformation theory we find that the Wigner hyperbolic rotation in phase space will map onto fractional squeezing operator in Hilbert space. The merit of Weyl ordering and the coherent state representation of Fresnel operator is used in our derivation.

Quantum Switch as a Thermodynamic Resource in the Context of Passive States.

In recent years, many works have explored possible advantages of indefinite causal order, with the main focus on its controlled implementation known as quantum switch. In this paper, we tackle advantages in quantum thermodynamics, studying whether quantum switch is capable of activating a passive state, either alone or with extra resources (active control state) and/or operations (measurement of the control system). By disproving the first possibility and confirming the second one, we show that quantum switch is not a thermodynamic resource in the discussed context, though it can facilitate work extraction given external resources. We discuss our findings by considering specific examples: a qubit system subject to rotations around the x and y axes in the Bloch sphere, as well as general unitaries from the U(2) group; and the system as a quantum harmonic oscillator with displacement operators, as well as with a combination of displacement and squeeze operators.

Risk-based analysis of squeeze cementing operations

The success rate of a squeeze cementing operation is generally low due to complexities in the range of potential leakage pathways and enormous uncertainty in characterizing these pathways. This work aims first to develop a physically based numerical model of the key part of the squeeze cementing operation, i.e. invasion of cement slurry into a realistic microannuli geometry under operational conditions. Secondly, it aims to account for uncertainties by using statistical tools combined with the deterministic model to deliver probabilistic information regarding outcomes. This paper develops this novel risk-based approach. The first objective of this work is to build upon our previous model, which focused on a single-perforation injection scenario. We reconstruct a multi-perforation injection scenario by collocating radial (single-perforation) invasion flows. We use this model to investigate the invasion of a viscoplastic fluid, representing a cement slurry, into a randomized varying width microannulus channel. The second objective is to investigate the effect of relevant parameters on leakage reduction. This is broken into two parts. First, how effectively the squeeze operation fills the microannulus around a perforation, explored through metrics that quantify penetration/filling. Second, what effect the penetration/filling has on the leakage of the entire well, i.e. given that the squeeze operation is local. We compare the effect of different perforation patterns and rheological parameters on penetration/filling metrics and on the reduction of leakage. We generate a probability distribution of leakage rates before and after the operation. In this way can estimate both the mean reduction in leakage for different perforation patterns, and associated confidence intervals. Such predictions have not been made before. Our results demonstrate the inherent uncertainty of a squeeze operation, which arises from the geometrical complexity. Practically, this suggests that higher perforation density and lower yield stress slurry will lead to higher success rates, as is intuitive. However, the spread of uncertainty in outcomes is not reduced much by such practices, meaning that one can still be unlucky in perforating the wrong part of the annulus.

TCJA-SNN: Temporal-Channel Joint Attention for Spiking Neural Networks.

Spiking neural networks (SNNs) are attracting widespread interest due to their biological plausibility, energy efficiency, and powerful spatiotemporal information representation ability. Given the critical role of attention mechanisms in enhancing neural network performance, the integration of SNNs and attention mechanisms exhibits tremendous potential to deliver energy-efficient and high-performance computing paradigms. In this article, we present a novel temporal-channel joint attention mechanism for SNNs, referred to as TCJA-SNN. The proposed TCJA-SNN framework can effectively assess the significance of spike sequence from both spatial and temporal dimensions. More specifically, our essential technical contribution lies on: 1) we employ the squeeze operation to compress the spike stream into an average matrix. Then, we leverage two local attention mechanisms based on efficient 1-D convolutions to facilitate comprehensive feature extraction at the temporal and channel levels independently and 2) we introduce the cross-convolutional fusion (CCF) layer as a novel approach to model the interdependencies between the temporal and channel scopes. This layer effectively breaks the independence of these two dimensions and enables the interaction between features. Experimental results demonstrate that the proposed TCJA-SNN outperforms the state-of-the-art (SOTA) on all standard static and neuromorphic datasets, including Fashion-MNIST, CIFAR10, CIFAR100, CIFAR10-DVS, N-Caltech 101, and DVS128 Gesture. Furthermore, we effectively apply the TCJA-SNN framework to image generation tasks by leveraging a variation autoencoder. To the best of our knowledge, this study is the first instance where the SNN-attention mechanism has been employed for high-level classification and low-level generation tasks. Our implementation codes are available at https://github.com/ridgerchu/TCJA.

A Fourier transform differentiator operator

By utilizing the Fourier transform, we present a practical method for evaluating a function of a derivative applied to any other arbitrary function of a single complex variable. As an illustration of this approach, we compute the actions of the displacement and squeeze operators on an arbitrary function, as well as the propagation of paraxial fields, Airy and number states, without the need for algebraic quantum operator techniques or the Fresnel integral.

Mitigating Quantum Decoherence in Force Sensors by Internal Squeezing.

The most efficient approach to laser interferometric force sensing to date uses monochromatic carrier light with its signal sideband spectrum in a squeezed vacuum state. Quantum decoherence, i.e., mixing with an ordinary vacuum state due to optical losses, is the main sensitivity limit. In this Letter, we present both theoretical and experimental evidence that quantum decoherence in high-precision laser interferometric force sensors enhanced with optical cavities and squeezed light injection can be mitigated by a quantum squeeze operation inside the sensor's cavity. Our experiment shows an enhanced measurement sensitivity that is independent of the optical readout loss in a wide range. Our results pave the way for quantum improvements in scenarios where high decoherence previously precluded the use of squeezed light. Our results hold significant potential for advancing the field of quantum sensors and enabling new experimental approaches in high-precision measurement technology.

Vibrational response functions for multidimensional electronic spectroscopy: From Duschinsky rotations to multimode squeezed coherent states.

Multidimensional spectroscopy unveils the interplay of nuclear and electronic dynamics, which characterizes the ultrafast dynamics of various molecular and solid-state systems. In a class of models widely used for the simulation of such dynamics, field-induced transitions between electronic states result in linear transformations (Duschinsky rotations) between the normal coordinates of the vibrational modes. Here, we present an approach for the calculation of the response functions, based on the explicit derivation of the vibrational state. This can be shown to coincide with a multimode squeezed coherent state, whose expression we derive within a quantum-optical formalism, and specifically by the sequential application to the initial state of rotation, displacement, and squeeze operators. The proposed approach potentially simplifies the numerical derivation of the response functions, avoiding the time integration of the Schrödinger equation, the Hamiltonian diagonalization, and the sum over infinite vibronic pathways. In addition, it quantitatively substantiates in the considered models the intuitive interpretation of the response functions in terms of the vibrational wave packet dynamics.

A Lightweight Block With Information Flow Enhancement for Convolutional Neural Networks

Convolutional neural networks (CNNs) have demonstrated excellent capability in various visual recognition tasks but impose an excessive computational burden. The latter problem is commonly solved by utilizing lightweight sparse networks. However, such networks have a limited receptive field in a few layers, and the majority of these networks face a severe information barrage due to their sparse structures. Spurred by these deficiencies, this work proposes a Squeeze Convolution block with Information Flow Enhancement (SCIFE), comprising a Divide-and-Squeeze Convolution and an Information Flow Enhancement scheme. The former module constructs a multi-layer structure through multiple squeeze operations to increase the receptive field and reduce computation. The latter replaces the affine transformation with the point convolution and dynamically adjusts the activation function’s threshold, enhancing information flow in both channels and layers. Moreover, we reveal that the original affine transformation may harm the network’s generalization capability. To overcome this issue, we utilize a point convolution with a zero initial mean. SCIFE can serve as a plug-and-play replacement for vanilla convolution blocks in mainstream CNNs, while extensive experimental results demonstrate that CNNs equipped with SCIFE compress benchmark structures without sacrificing performance, outperforming their competitors.

Squeezing Stationary Distributions of Stochastic Chemical Reaction Systems

Stochastic modeling of chemical reaction systems based on master equations has been an indispensable tool in physical sciences. In the long-time limit, the properties of these systems are characterized by stationary distributions of chemical master equations. In this paper, we describe a novel method for computing stationary distributions analytically, based on a parallel formalism between stochastic chemical reaction systems and second quantization. Anderson, Craciun, and Kurtz showed that, when the rate equation for a reaction network admits a complex-balanced steady-state solution, the corresponding stochastic reaction system has a stationary distribution of a product form of Poisson distributions. In a formulation of stochastic reaction systems using the language of second quantization initiated by Doi, product-form Poisson distributions correspond to coherent states. Pursuing this analogy further, we study the counterpart of squeezed states in stochastic reaction systems. Under the action of a squeeze operator, the time-evolution operator of the chemical master equation is transformed, and the resulting system describes a different reaction network, which does not admit a complex-balanced steady state. A squeezed coherent state gives the stationary distribution of the transformed network, for which analytic expression is obtained.

Squeezing the free scalar ground state

Consider two free Hamiltonians for the same scalar field with two different masses. We find a squeeze operator which maps the ground state of one to the other. The operator is described in both the Dirac and also the Schrödinger wave functional formalisms for quantum field theory. We conjecture that this construction can be generalized to obtain operators which map between distinct topological sectors in the same theory.

A Novel Rolling Bearing Fault Diagnosis Method Based on BLS and CNN with Attention Mechanism

In actual industrial application scenarios, noise pollution makes it difficult to extract fault features accurately via conventional methods. A novel method for rolling bearing fault diagnosis combining a convolutional neural network (CNN), an attention mechanism for a squeeze-and-excitation network (SENet) module and a broad learning system (BLS) is proposed (SECNN–BLS). The one-dimensional bearing vibration signal is processed by using multiple short-time Fourier transforms (STFT); the two-dimensional image in the time-frequency domain is used as the model input. In the CNN fault feature extraction process, SENet is introduced to replace the CNN convolution layer, and global information is obtained through the squeeze operation. Excitation operation captures the importance of image channels, assigns weights adaptively to improve the attention on important information and eliminates the interference of irrelevant features without increasing the spatial and temporal complexity. The weighted feature representation is then transferred to the BLS input. The BLS has the characteristics of a flat structure and ridge regression to quickly solve the weights; as a fault classifier, it can save more computing resources and improve the accuracy of the fault classification. The SECNN–BLS fault diagnosis has achieved more than 98% accuracy on the Society for Machinery Failure Prevention Technology (MFPT) dataset. We also demonstrate the excellent performance of SECNN–BLS in a noisy environment.

Coherent state representation of squeezing operator and its applications

In a previous paper Zhan et al. (2022) proposed the coherent state representation for linear operator, which shows a beautiful theoretical value. Inspired by the work, we further extend it to the nonlinear case, including single- and two-mode squeezing process. Based on squeezing operators’ transformation relation, we introduce their coherent state representations and derive their normal ordering forms by using the technique of integration within product of operator. As applications, we examine some operators’s normal ordering forms and show the entanglement property of prepared quantum states via the Schmidt decomposition. In particular, we further consider two cascaded two-mode squeezing operators, in which a generalized SU(1,1) interferometer is involved. In addition, the application of two-mode squeezing operator followed by photon-subtraction to generate non-Gaussian entangled states in quantum teleportation is discussed. Our results will be useful in preparation of quantum states and quantum metrology.

Image-Caption Model Based on Fusion Feature

The encoder–decoder framework is the main frame of image captioning. The convolutional neural network (CNN) is usually used to extract grid-level features of the image, and the graph convolutional neural network (GCN) is used to extract the image’s region-level features. Grid-level features are poor in semantic information, such as the relationship and location of objects, while regional features lack fine-grained information about images. To address this problem, this paper proposes a fusion-features-based image-captioning model, which includes the fusion feature encoder and LSTM decoder. The fusion-feature encoder is divided into grid-level feature encoder and region-level feature encoder. The grid-level feature encoder is a convoluted neural network embedded in squeeze and excitation operations so that the model can focus on features that are highly correlated to the title. The region-level encoder employs node-embedding matrices to enable models to understand different node types and gain richer semantics. Then the features are weighted together by an attention mechanism to guide the decoder LSTM to generate an image caption. Our model was trained and tested in the MS COCO2014 dataset with the experimental evaluation standard Bleu-4 score and CIDEr score of 0.399 and 1.311, respectively. The experimental results indicate that the model can describe the image in detail.

Experimental Preparation and Manipulation of Squeezed Cat States via an All‐Optical In‐Line Squeezer

AbstractThe squeezed cat state, an essential quantum resource, can be used for quantum error correction and slowing decoherence of the optical cat state. However, preparing a squeezed cat state with high generation rate, and effectively manipulating it, remain challenging. In this work, a high‐performance all‐optical in‐line squeezer is developed to prepare a squeezed cat state and manipulate the phase of the quadrature squeezing. This scheme has the advantages that the phase of the quadrature squeezing of the squeezed cat state can be manipulated by changing the working condition of the squeezer, and that a higher generation rate can be achieved via the deterministic squeezing operation of the in‐line squeezer. The generation rate of squeezed cat states reaches 2 kHz, the same as that of the initial cat state. The all‐optical in‐line squeezer proposed here removes the requirements of electro‐optic and opto‐electric conversions necessary for an off‐line squeezer, thus enabling high‐bandwidth squeezing operations on non‐Gaussian states. These results provide an efficient method to prepare and manipulate optical squeezed cat states, which makes a step closer to their applications in all‐optical quantum information processing.

Operator complexity for quantum scalar fields and cosmological perturbations

We calculate the operator complexity for the displacement, squeeze and rotation operators of a quantum harmonic oscillator, assuming equal computational cost for the corresponding fundamental gates. The complexity of the time-dependent displacement operator is constant, equal to the magnitude of the coherent state parameter, while the complexity of unitary evolution by a generic quadratic Hamiltonian is proportional to the amount of squeezing and is sensitive to the time-dependent phase of the unitary operator. We apply these results to study the complexity of a free massive scalar field, finding that the complexity has a period of rapid linear growth followed by a saturation determined by the UV cutoff and the number of spatial dimensions. We also study the complexity of the unitary evolution of quantum cosmological perturbations in de Sitter space, which can be written as time-dependent squeezing and rotation operators on individual Fourier mode pairs. The complexity of a single mode pair at late times grows linearly with the number of $e$-folds, while the complexity at early times oscillates rapidly due to the sensitivity of operator complexity to the phase of unitary time evolution. Integrating over all modes, the total complexity of cosmological perturbations scales as the square root of the (exponentially) growing volume of de Sitter space, suggesting that inflation leads to an explosive growth in complexity of the Universe.

Thangka Yidam classification based on DenseNet and SENet

Thangka Yidam classification is of great importance for understanding the image content and experiencing the Tibetan living culture. However, few samples, complex composition, and diverse Thangka images give the present methods a challenge for Thangka Yidam classification. To this end, we propose an improved method for Thangka Yidam classification based on DenseNet and SENet, which improves the transmission and reuse of feature information. First, a weighted pooling function is designed in the squeeze operation of SENet to alleviate the loss of feature information. Second, we further embed the improved SENet between dense blocks and transition layers of DenseNet to learn the weight correlation between feature channels. Finally, we adopt the Leaky ReLU function to optimize the network in DenseNet. The experimental results demonstrate that the accuracy of our method on the constructed Thangka dataset is increased by 5.54% to 95.89% compared with DenseNet model, and the accuracy on the Cifar-10 dataset is increased by 2.23% to 92.46%. Moreover, the experiments on Thangka dataset also show the significant improvements of our approach over the state-of-the-art methods, which proves the generalization ability of our method.