Design of a novel hybrid quantum deep neural network in INEQR images classification

We redesign the parameterized quantum circuit in the quantum deep neural network, construct a three-layer structure as the hidden layer, and then use classical optimization algorithms to train the parameterized quantum circuit, thereby propose a novel hybrid quantum deep neural network(HQDNN) used for image classification. After bilinear interpolation reduces the original image to a suitable size, an improved novel enhanced quantum representation(INEQR) is used to encode it into quantum states as the input of the HQDNN. Multi-layer parameterized quantum circuits are used as the main structure to implement feature extraction and classification. The output results of parameterized quantum circuits are converted into classical data through quantum measurements and then optimized on a classical computer. To verify the performance of the HQDNN, we conduct binary classification and three classification experiments on the MNIST(Modified National Institute of Standards and Technology) data set. In the first binary classification, the accuracy of 0 and 4 exceeds98%. Then we compare the performance of three classification with other algorithms, the results on two datasets show that the classification accuracy is higher than that of quantum deep neural network and general quantum convolutional neural network.

HQNN-SFOP:Hybrid Quantum Neural Networks with Signal Feature Overlay Projection for Drone Detection Using Radar Return Signals-A Simulation

With the wide application of drone technology,there is an increasing demand for the detection of radar return signals from drones.Existing detection methods mainly rely on time-frequency domain feature extraction and classical machine learning algorithms for image recognition.This method suffers from the problem of large dimensionality of image features,which leads to large input data size and noise affecting learning.Therefore,this paper proposes to extract signal time-domain statistical features for radar return signals from drones and reduce the feature dimension from 512×4 to 16 dimensions.However,the downscaled feature data makes the accuracy of traditional machine learning algorithms decrease,so we propose a new hybrid quantum neural network with signal feature overlay projection(HQNN-SFOP),which reduces the dimensionality of the signal by extracting the statistical features in the time domain of the signal,introduces the signal feature overlay projection to enhance the expression ability of quantum computation on the signal features,and introduces the quantum circuits to improve the neural network’s ability to obtain the inline relationship of features,thus improving the accuracy and migration generalization ability of drone detection.In order to validate the effectiveness of the proposed method,we experimented with the method using the MM model that combines the real parameters of five commercial drones and random drones parameters to generate data to simulate a realistic environment.The results show that the method based on statistical features in the time domain of the signal is able to extract features at smaller scales and obtain higher accuracy on a dataset with an SNR of 10 dB.On the time-domain feature data set,HQNNSFOP obtains the highest accuracy compared to other conventional methods.In addition,HQNN-SFOP has good migration generalization ability on five commercial drones and random drones data at different SNR conditions.Our method verifies the feasibility and effectiveness of signal detection methods based on quantum computation and experimentally demonstrates that the advantages of quantum computation for information processing are still valid in the field of signal processing,it provides a highly efficient method for the drone detection using radar return signals.

Influence of Quantum Information Technology on International Security

Humanity is currently undergoing the fourth industrial revolution,characterized by advancements in artificial intelligence,clean energy,quantum information technology,virtual reality,and biotechnology.This technological revolution is poised to have a profound impact on the world.Quantum information technology encompasses both quantum computing and the transmission of quantum information.This article aims to integrate quantum information technology with international security concerns,exploring its implications for international security and envisioning its groundbreaking significance.

Mechanism of Universal Quantum Computation in the Brain

In this paper, the authors extend [1] and provide more details of how the brain may act like a quantum computer. In particular, positing the difference between voltages on two axons as the environment for ions undergoing spatial superposition, we argue that evolution in the presence of metric perturbations will differ from that in the absence of these waves. This differential state evolution will then encode the information being processed by the tract due to the interaction of the quantum state of the ions at the nodes with the “controlling’ potential. Upon decoherence, which is equal to a measurement, the final spatial state of the ions is decided and it also gets reset by the next impulse initiation time. Under synchronization, several tracts undergo such processes in synchrony and therefore the picture of a quantum computing circuit is complete. Under this model, based on the number of axons in the corpus callosum alone, we estimate that upwards of 50 million quantum states might be prepared and evolved every second in this white matter tract, far greater processing than any present quantum computer can accomplish.

Quantum Computing Based Neural Networks for Anomaly Classification in Real-Time Surveillance Videos

For intelligent surveillance videos,anomaly detection is extremely important.Deep learning algorithms have been popular for evaluating realtime surveillance recordings,like traffic accidents,and criminal or unlawful incidents such as suicide attempts.Nevertheless,Deep learning methods for classification,like convolutional neural networks,necessitate a lot of computing power.Quantum computing is a branch of technology that solves abnormal and complex problems using quantum mechanics.As a result,the focus of this research is on developing a hybrid quantum computing model which is based on deep learning.This research develops a Quantum Computing-based Convolutional Neural Network(QC-CNN)to extract features and classify anomalies from surveillance footage.A Quantum-based Circuit,such as the real amplitude circuit,is utilized to improve the performance of the model.As far as my research,this is the first work to employ quantum deep learning techniques to classify anomalous events in video surveillance applications.There are 13 anomalies classified from the UCF-crime dataset.Based on experimental results,the proposed model is capable of efficiently classifying data concerning confusion matrix,Receiver Operating Characteristic(ROC),accuracy,Area Under Curve(AUC),precision,recall as well as F1-score.The proposed QC-CNN has attained the best accuracy of 95.65 percent which is 5.37%greater when compared to other existing models.To measure the efficiency of the proposed work,QC-CNN is also evaluated with classical and quantum models.

Leveraging Quantum Computing for the Ising Model to Simulate Two Real Systems: Magnetic Materials and Biological Neural Networks (BNNs)

Quantum computing is a field with increasing relevance as quantum hardware improves and more applications of quantum computing are discovered. In this paper, we demonstrate the feasibility of modeling Ising Model Hamiltonians on the IBM quantum computer. We developed quantum circuits to simulate these systems more efficiently for both closed and open boundary Ising models, with and without perturbations. We tested these various geometries of systems in both 1-D and 2-D space to mimic two real systems: magnetic materials and biological neural networks (BNNs). Our quantum model is more efficient than classical computers, which can struggle to simulate large, complex systems of particles.

The application of quantum coherence as a resource

Quantum coherence is a basic concept in quantum mechanics, representing one of the most fundamental characteristics that distinguishes quantum mechanics from classical physics. Quantum coherence is the basis for multi-particle interference and quantum entanglement. It is also the essential ingredient for various physical phenomena in quantum optics, quantum information, etc. In recent years, with the proposal of a quantum coherence measurement scheme based on a resource theory framework, quantum coherence as a quantum resource has been extensively investigated. This article reviews the resource theories of quantum coherence and introduces the important applications of quantum coherence in quantum computing,quantum information, and interdisciplinary fields, particularly in quantum thermodynamics and quantum biology. Quantum coherence and its applications are still being explored and developed. We hope this review can provide inspiration for relevant research.

Learning algorithm and application of quantum BP neural networks based on universal quantum gates

A quantum BP neural networks model with learning algorithm is proposed. First, based on the universality of single qubit rotation gate and two-qubit controlled-NOT gate, a quantum neuron model is constructed, which is composed of input, phase rotation, aggregation, reversal rotation and output. In this model, the input is described by qubits, and the output is given by the probability of the state in which （1） is observed. The phase rotation and the reversal rotation are performed by the universal quantum gates. Secondly, the quantum BP neural networks model is constructed, in which the output layer and the hide layer are quantum neurons. With the application of the gradient descent algorithm, a learning algorithm of the model is proposed, and the continuity of the model is proved. It is shown that this model and algorithm are superior to the conventional BP networks in three aspects： convergence speed, convergence rate and robustness, by two application examples of pattern recognition and function approximation.

Quantum-Inspired Neural Networks with Application

In this paper, a novel neural network is proposed based on quantum rotation gate and controlled- NOT gate. Both the input layer and the hide layer are quantum-inspired neurons. The input is given by qubits, and the output is the probability of qubit in the state . By employing the gradient descent method, a training algorithm is introduced. The experimental results show that this model is superior to the common BP networks.

Quantum-Inspired Neural Network with Quantum Weights and Real Weights

To enhance the approximation ability of neural networks, by introducing quantum rotation gates to the traditional BP networks, a novel quantum-inspired neural network model is proposed in this paper. In our model, the hidden layer consists of quantum neurons. Each quantum neuron carries a group of quantum rotation gates which are used to update the quantum weights. Both input and output layer are composed of the traditional neurons. By employing the back propagation algorithm, the training algorithms are designed. Simulation-based experiments using two application examples of pattern recognition and function approximation, respectively, illustrate the availability of the proposed model.

Continuous Variable Quantum MNIST Classifiers—Classical-Quantum Hybrid Quantum Neural Networks

In this paper, classical and continuous variable (CV) quantum neural network hybrid multi-classifiers are presented using the MNIST dataset. Currently available classifiers can classify only up to two classes. The proposed architecture allows networks to classify classes up to nm classes, where n represents cutoff dimension and m the number of qumodes on photonic quantum computers. The combination of cutoff dimension and probability measurement method in the CV model allows a quantum circuit to produce output vectors of size nm. They are then interpreted as one-hot encoded labels, padded with nm - 10 zeros. The total of seven different classifiers is built using 2, 3, …, 6, and 8-qumodes on photonic quantum computing simulators, based on the binary classifier architecture proposed in “Continuous variable quantum neural networks” [1]. They are composed of a classical feed-forward neural network, a quantum data encoding circuit, and a CV quantum neural network circuit. On a truncated MNIST dataset of 600 samples, a 4-qumode hybrid classifier achieves 100% training accuracy.

Six-State Quantum Key Distribution Using Photons with Orbital Angular Momentum

A new implementation of high-dimensional quantum key distribution （QKD） protocol is discussed. Using three mutual unbiased bases, we present a d？level six-state QKD protocol that exploits the orbital angular momentum with the spatial mode of the light beam. The protocol shows that the feature of a high capacity since keys are encoded using photon modes in d-level Hilbert space. The devices for state preparation and measurement are also discussed. This protocol has high security and the alignment of shared reference frames is not needed between sender and receiver.

Quantum Polynomial-Time Fixed-Point Attack for RSA

Security analysis of public-key cryptosystems is of fundamental significance for both theoretical research and applications in cryptography. In particular, the security of widely used public-key cryptosystems merits deep research to protect against new types of attacks. It is therefore highly meaningful to research cryptanalysis in the quantum computing environment. Shor proposed a wellknown factoring algorithm by finding the prime factors of a number n =pq, which is exponentially faster than the best known classical algorithm. The idea behind Shor's quantum factoring algorithm is a straightforward programming consequence of the following proposition: to factor n, it suffices to find the order r; once such an r is found, one can compute gcd( a^(r/2) ±1, n)=p or q. For odd values of r it is assumed that the factors of n cannot be found(since a^(r/2) is not generally an integer). That is, the order r must be even. This restriction can be removed, however, by working from another angle. Based on the quantum inverse Fourier transform and phase estimation, this paper presents a new polynomial-time quantum algorithm for breaking RSA, without explicitly factoring the modulus n. The probability of success of the new algorithm is greater than 4φ( r)/π~2 r, exceeding that of the existing quantum algorithm forattacking RSA based on factorization. In constrast to the existing quantum algorithm for attacking RSA, the order r of the fixed point C for RSA does not need to be even. It changed the practices that cryptanalysts try to recover the private-key, directly from recovering the plaintext M to start, a ciphertext-only attack attacking RSA is proposed.

Error-detected single-photon quantum routing using a quantum dot and a double-sided microcavity system

Based on a hybrid system consisting of a quantum dot coupled with a double-sided micropillar cavity, we investigate the implementation of an error-detected photonic quantum routing controlled by the other photon. The computational errors from unexpected experimental imperfections are heralded by single photon detections, resulting in a unit fidelity for the present scheme, so that this scheme is intrinsically robust. We discuss the performance of the scheme with currently achievable experimental parameters. Our results show that the present scheme is efficient. Furthermore, our scheme could provide a promising building block for quantum networks and distributed quantum information processing in the future.

The Time Division Multi-Channel Communication Model and the Correlative Protocol Based on Quantum Time Division Multi-Channel Communication

Based on the classical time division multi-channel communication theory, we present a scheme of quantum time- division multi-channel communication （QTDMC）. Moreover, the model of quantum time division switch （QTDS） and correlative protocol of QTDMC are proposed. The quantum bit error rate （QBER） is analyzed and the QBER simulation test is performed. The scheme shows that the QTDS can carry out multi-user communication through quantum channel, the QBER can also reach the reliability requirement of communication, and the protocol of QTDMC has high practicability and transplantable. The scheme of QTDS may play an important role in the establishment of quantum communication in a large scale in the future.

Cryptanalysis and Improvement of Quantum Secret Sharing Protocol between Multiparty and Multiparty with Single Photons and Unitary Transformations

In a recent paper [Yan F L et al. Chin.Phys.Lett. 25（2008）1187], a quantum secret sharing the protocol between multiparty and multiparty with single photons and unitary transformations was presented. We analyze the security of the protocol and find that a dishonest participant can eavesdrop the key by using a special attack. Finally, we give a description of this strategy and put forward an improved version of this protocol which can stand against this kind of attack.

Implementing a Multi-Qubit Quantum Phase Gate Encoded by Photonic Qubit

A protocol is proposed to implement a three-qubit phase gate for photonic qubits in a three-mode cavity. The idea can be extended to directly implement a N-qubit phase gate. We also show that the interaction time remains unchanged with the increasing number of qubits. In addition, the influence of cavity decay and atomic spontaneous emission on the gate fidelity and photon loss probability is also discussed by numerical calculation.

Entanglement Purification for Mixed Entangled Quantum Dot States via Superconducing Transmission Line Resonators

An entanglement purification protocol for mixed entangled states is presented via double quantum dot molecules inside a superconducing transmission line resonator （TLR）. In the current scenario, coupling for arbitrary double quantum dot molecules can be tuned via the TLR in the large detuning region by controlling the qubit level splitting. The TLR is always empty and only virtually excited, so the interaction is insensitive to both the TLR decay and thermal field. Discussion about the feasibility of our scheme shows that the entanglement purification can be implemented with high fidelity and successful probability.

Population Swap of a Pair of Quantum Dots Coupling to a Plasmonic Nanocavity

We theoretically design a single-mode plasmonic ring nanocavity. Based on the plasmonic cavity, the exciton dynamics between two identical quantum dots （QD-p, QD-q） coupled to the nanocavity are investigated. It is shown that the coupling factors gi （i = p, q） between QD-i and surface plasmons are both equal to 12.53meV in our model and exeiton population swap between the two QDs can be realized. The periods and amplitudes of population oscillations can be modified by the coupling factors. Our results may have potential applications in quantum information and quantum computation on a chip.

Quantum Swap Gate with Molecular Ensembles via Cavity Quantum Electrodynamics

We present a simple method to realize a swap gate at one step with two molecular ensembles in a stripline cavity. In this scheme, we can benefit from the enhancement of the coherent coupling and acquire a long coherent time with encoding qubits in different spin states of the rotational ground state in the molecular ensembles. As a by-product, a scheme to create an entangled state with one excitation stored in two ensembles is proposed.