From Standard Policy-Based Zero Trust to Absolute Zero Trust (AZT): A Quantum Leap to Q-Day Security

Cybercrime is projected to cost a whopping $23.8 Trillion by 2027. This is essentially because there’s no computer network that’s not vulnerable. Fool-proof cybersecurity of personal data in a connected computer is considered practically impossible. The advent of quantum computers (QC) will worsen cybersecurity. QC will be a boon for data-intensive industries by drastically reducing the computing time from years to minutes. But QC will render our current cryptography vulnerable to quantum attacks, breaking nearly all modern cryptographic systems. Before QCs with sufficient qubits arrive, we must be ready with quantum-safe strategies to protect our ICT infrastructures. Post-quantum cryptography (PQC) is being aggressively pursued worldwide as a defence from the potential Q-day threat. NIST (National Institute of Standards and Technology), in a rigorous process, tested 82 PQC schemes, 80 of which failed after the final round in 2022. Recently the remaining two PQCs were also cracked by a Swedish and a French team of cryptographers, placing NIST’s PQC standardization process in serious jeopardy. With all the NIST-evaluated PQCs failing, there’s an urgent need to explore alternate strategies. Although cybersecurity heavily relies on cryptography, recent evidence indicates that it can indeed transcend beyond encryption using Zero Vulnerability Computing (ZVC) technology. ZVC is an encryption-agnostic absolute zero trust (AZT) approach that can potentially render computers quantum resistant by banning all third-party permissions, a root cause of most vulnerabilities. Unachievable in legacy systems, AZT is pursued by an experienced consortium of European partners to build compact, solid-state devices that are robust, resilient, energy-efficient, and with zero attack surface, rendering them resistant to malware and future Q-Day threats.

Quantum Dynamics via a Hidden Liouville Space

The traditional simulations may occasionally turn out to be challenging for the quantum dynamics, particularly those governed by the nonlinear Hamiltonians. In this work, we introduce a nonstandard iterative technique where the Liouville space is briefly expanded with an additional (virtual) space only within ultrashort subintervals. This tremendously reduces the cost of time-consuming calculations. We implement our technique for an example of a charged particle in both harmonic and anharmonic potentials. The temporal evolutions of the probability for the particle being in the ground state are obtained numerically and compared to the analytical solutions. We further discuss the physics insight of this technique based on a thought-experiment. Successive processes intrinsically “hitchhiking” via virtual space in discrete ultrashort time duration, are the hallmark of our technique. We believe that this technique has potential for solving numerous problems which often pose a challenge when using the traditional approach based on time-ordered exponentials.

Ontology of Quantum Gravity

A theory of Quantum Gravity based on Primordial Field Theory is applied to a fundamental particle, the neutron. The result is compared to the current quantum description of the neutron bouncing in a gravitational field. Our quantum gravity theory yields results in agreement with the Q-bounce experimental data, but ontologically different from quantum mechanics. The differences are summarized and imply that this experiment on a fundamental particle has the potential to radically alter the ontology of field theory.

使用USTER QUANTUM 3型电子清纱器控制成纱质量

为了提高成纱质量,介绍了USTER QUANTUM 3型电子清纱器的特点和主要功能,重点从NSLT电清参数设置、Q-参数应用、珠链纱疵功能应用等方面详细进行分析。指出,USTER QUANTUM 3型电子清纱器为电容式电子清纱器,其智能清纱统计功能可实现在线监督检测;合理使用有助于优化车间电清参数,分析纱线质量水平,把关不合格管纱,达到控制质量的目的。

TWO DIMENSIONAL MELLIN TRANSFORM IN QUANTUM CALCULUS

In this article, we introduce the two dimensional Mellin transform M4（f）（s,t）, give some properties, establish the Paley-Wiener theorem and Plancherel formula, present the Hausdorff-Young inequality, and find several applications for the two dimensional Mellin transform.

Efficient scheme for realizing quantum dense coding with GHZ state in separated low-Q cavities

We propose an efficient scheme for realizing quantum dense coding with three-particle GHZ state in separated low-Q cavities. In this paper, the GHZ state is first prepared with three atoms trapped, respectively, in three spatial separated cavities. Meanwhile, with the assistance of a coherent optical pulse and X-quadrature homodyne measurement, we can im- plement quantum dense coding with three-particle GHZ state with a higher probability. Our scheme can also be generalized to realize N-particle quantum dense coding.

Novel quantum watermarking algorithm based on improved least significant qubit modification for quantum audio

As one of essential multimedia in quantum networks, the copyright protection of quantum audio has gradually be- come an important issue in the domain of quantum information hiding in the decades. In this paper, an improved quantum watermarking algorithm based on quantum audio by using least significant qubit （LSQb） modification is proposed. Com- pared with the previous achievements, it can effectively improve the robustness and security of watermark for copyright protection of quantum audio. In the new algorithm, the least significant bites and the peripheral least significant bits of the amplitudes are modified in terms of their logical consistency and correlation to enhance watermark robustness of resisting various illegal attacks. Furthermore, the new algorithm can avoid the weak robustness defect of many previous algorithms that directly embedded the watermark into the least significant bits. In order to implement the new algorithm, some spe- cific quantum circuits are designed to obtain better applicability and scalability for embedding and extracting watermark. Finally, the simulation results including the values of audio waveforms and signal to noise ratios （SNR） prove that the new algorithm has good transparency, robustness, and security.

Q Learning with Quantum Neural Networks

Applying quantum computing techniques to machine learning has attracted widespread attention recently and quantum machine learning has become a hot research topic. There are three major categories of machine learning: supervised, unsupervised, and reinforcement learning (RL). However, quantum RL has made the least progress when compared to the other two areas. In this study, we implement the well-known RL algorithm Q learning with a quantum neural network and evaluate it in the grid world environment. RL is learning through interactions with the environment, with the aim of discovering a strategy to maximize the expected cumulative rewards. Problems in RL bring in unique challenges to the study with their sequential nature of learning, potentially long delayed reward signals, and large or infinite size of state and action spaces. This study extends our previous work on solving the contextual bandit problem using a quantum neural network, where the reward signals are immediate after each action.

Quantum Algorithms and Experiment Implementations Based on IBM Q

With the rapid development of quantum theory and technology in recent years,especially the emergence of some quantum cloud computing platforms,more and more researchers are not satisfied with the theoretical derivation and simulation verification of quantum computation(especially quantum algorithms),experimental verification on real quantum devices has become a new trend.In this paper,three representative quantum algorithms,namely Deutsch-Jozsa,Grover,and Shor algorithms,are briefly depicted,and then their implementation circuits are presented,respectively.We program these circuits on python with QISKit to connect the remote real quantum devices(i.e.,ibmqx4,ibmqx5)on IBM Q to verify these algorithms.The experimental results not only show the feasibility of these algorithms,but also serve to evaluate the functionality of these devices.

Reinforcement Learning with Deep Quantum Neural Networks

The advantage of quantum computers over classical computers fuels the recent trend of developing machine learning algorithms on quantum computers, which can potentially lead to breakthroughs and new learning models in this area. The aim of our study is to explore deep quantum reinforcement learning (RL) on photonic quantum computers, which can process information stored in the quantum states of light. These quantum computers can naturally represent continuous variables, making them an ideal platform to create quantum versions of neural networks. Using quantum photonic circuits, we implement Q learning and actor-critic algorithms with multilayer quantum neural networks and test them in the grid world environment. Our experiments show that 1) these quantum algorithms can solve the RL problem and 2) compared to one layer, using three layer quantum networks improves the learning of both algorithms in terms of rewards collected. In summary, our findings suggest that having more layers in deep quantum RL can enhance the learning outcome.

Distributional Reinforcement Learning with Quantum Neural Networks

Traditional reinforcement learning (RL) uses the return, also known as the expected value of cumulative random rewards, for training an agent to learn an optimal policy. However, recent research indicates that learning the distribution over returns has distinct advantages over learning their expected value as seen in different RL tasks. The shift from using the expectation of returns in traditional RL to the distribution over returns in distributional RL has provided new insights into the dynamics of RL. This paper builds on our recent work investigating the quantum approach towards RL. Our work implements the quantile regression (QR) distributional Q learning with a quantum neural network. This quantum network is evaluated in a grid world environment with a different number of quantiles, illustrating its detailed influence on the learning of the algorithm. It is also compared to the standard quantum Q learning in a Markov Decision Process (MDP) chain, which demonstrates that the quantum QR distributional Q learning can explore the environment more efficiently than the standard quantum Q learning. Efficient exploration and balancing of exploitation and exploration are major challenges in RL. Previous work has shown that more informative actions can be taken with a distributional perspective. Our findings suggest another cause for its success: the enhanced performance of distributional RL can be partially attributed to its superior ability to efficiently explore the environment.

基于相对熵准则的CMP道集Q扫描

地下介质的大地滤波效应(主要包括薄层叠合效应、散射效应和非弹性效应等)使地震信号振幅变小、频带变窄、主频降低、相位畸变,这可用等效Q值引起的效应表示。合理估计等效Q值并进行相应的Q值补偿对提高地震资料的分辨率十分重要。吸收衰减是一个累积效应,当地层Q值一定时,地震子波的衰减现象随传播距离/时间的增加和频率的增大而愈明显,即深层、远炮检距反射子波的能量和高频成分衰减更严重。为此,假设地层为水平层状、反射同相轴满足双曲线规律,已知地下均方根速度模型,利用CMP道集,沿双曲线轨迹开时窗以提取单个衰减子波;然后选取一组Q值,在频率域对衰减子波振幅谱做Q值补偿处理,依据不同炮检距处补偿后地震子波振幅谱一致性最佳原则估计水平地层的等效Q值。该方法需要一个合适的度量准则,因此对比、分析了相似系数、质心频率和KL散度、JS散度等准则。实验表明,在选取合适频带范围(如0~60 Hz)后,JS散度准则对规范化后的振幅谱一致性度量的敏感性更高,抗噪能力更强;在合适的信噪比条件下,基于相对熵准则的CMP道集扫描估计品质因子方法可以得到比较稳定的等效Q值估计结果。

Completing the Standard Model with Gravity by General Relativizing Quantum Physics (RQP) (Coupling Spin-2 Gravitons with Spin-0 Particles to Generate Higgs Mass)

After a straightforward general relativistic calculation on a modified flat-spacetime metric (developed from the fluctuating vacuum energy interacting with a graviton field), a pair of n-valued covariant and contravariant energy momentum tensors emerged analogous to quantized raising and lower operators. Detaching these operators from the general relativistic field equations, and then transporting them to act on extreme spacetimes, these operators were able to generate fundamental particle boson masses. In particular, the operators precisely generated Higgs mass. Then by applying a consistency approach to the gravitational field equations—similar to how Maxwell applied to the electromagnetic ones—it allowed for the coupling of spin-to-mass, further restricting the particle mass to be in precise agreement with CODATA experimental values. Since this is a massless field approach integrated discretely with a massive one, it overcomes various renormalizing difficulties;moreover it solves the mass hierarchal problem of the Standard Model of particle physics, and generates its spin and therefore shows quantum physics to be a subset of General Relativity, just as Einstein had first imagined.

On <i>q</i>-Deformed Calculus in Quantum Geometry

The relation between noncommutative (or quantum) geometry and themathematics of spacesis in many ways similar to the relation between quantum physicsand classical physics. One moves from the commutative algebra of functions on a space (or a commutative algebra of classical observable in classical physics) to a noncommutative algebra representing a noncommutative space (or a noncommutative algebra of quantum observables in quantum physics). The object of this paper is to study the basic rules governing q-calculus as compared with the classical Newton-Leibnitz calculus.

Exact solution of Schrdinger equation with q-deformed quantum potentials using Nikiforov–Uvarov method

In this paper, we present the exact solution of the one-dimensional Schrrdinger equation for the q-deformed quantum potentials via the Nikiforov-Uvarov method. The eigenvalues and eigenfunctions of these potentials are obtained via this method. The energy equations and the corresponding wave functions for some special cases of these potentials are briefly discussed. The PT-symmetry and Hermiticity for these potentials are also discussed.

Quantum Multiple Q-Learning

In this paper, a collection of value-based quantum reinforcement learning algorithms are introduced which use Grover’s algorithm to update the policy, which is stored as a superposition of qubits associated with each possible action, and their parameters are explored. These algorithms may be grouped in two classes, one class which uses value functions (V(s)) and new class which uses action value functions (Q(s,a)). The new (Q(s,a))-based quantum algorithms are found to converge faster than V(s)-based algorithms, and in general the quantum algorithms are found to converge in fewer iterations than their classical counterparts, netting larger returns during training. This is due to fact that the (Q(s,a)) algorithms are more precise than those based on V(s), meaning that updates are incorporated into the value function more efficiently. This effect is also enhanced by the observation that the Q(s,a)-based algorithms may be trained with higher learning rates. These algorithms are then extended by adding multiple value functions, which are observed to allow larger learning rates and have improved convergence properties in environments with stochastic rewards, the latter of which is further improved by the probabilistic nature of the quantum algorithms. Finally, the quantum algorithms were found to use less CPU time than their classical counterparts overall, meaning that their benefits may be realized even without a full quantum computer.

线性再生散度模型的极大L_(q)-似然估计的渐近性质

线性再生散度模型是线性回归模型、广义线性模型和指数线性模型的自然推广,极大L_(q)-似然估计是基于非广义熵的新参数估计方法,是极大似然估计的推广。用极大L_(q)-似然估计研究线性再生散度模型,在一定的条件下,给出了线性再生散度模型的极大L_(q)-似然估计的弱相合性和渐近正态性。最后通过模拟算例表明:随着n的增大参数估计值越接近真值;当q→1时,ML_(q)E的参数估计值接近于MLE的参数估计值。

热载流子多结太阳能电池CdSe/CdS核壳量子点和纳米片的声子瓶颈效应机理

热载流子多结太阳能电池(HCMJSC)是热载流子及叠层电池概念相结合而提出的一种较有前景的第三代太阳能电池之一,其理论效率在一个标准太阳条件(即1000 W/m 2,25℃)下将高于65%,远高于32%的单节硅基电池极限效率。该型电池主要包括一个宽带隙的顶结薄膜和一个适中带隙的厚底结基底,以分别高效吸收利用高能和低能光子。其广泛应用于光电器件的宽带隙CdSe/CdS低维材料体系(如量子点、纳米片等)有望成为顶结薄膜的合适候选材料。然而,该材料体系中的声子瓶颈效应(PBE)机理目前尚不明晰。文章主要研究了CdSe/CdS核壳量子点(QDs)和纳米片(NPLs)中的PBE机理;通过稳态光致发光(SSPL)和皮秒时间分辨尺度光致发光(ps-TRPL)技术,计算该材料体系的热弛豫系数(Q th),从而定量分析激发载流子的弛豫速率,同时阐述了PBE和量子点中常见的俄歇复合之间的耦合关系,最终系统研究了QDs和NPLs中载流子弛豫过程机理,提出HCMJSC的发展路径和建议。

量子知识创造(Q-SECI)模型构建及机制初探——基于顿悟学习对SECI模型的一种拓展

在Nonaka等提出的SECI模型基础上,借鉴顿悟学习的心理学研究成果,以量子学习模型作为理论基础,构建了基于顿悟的量子知识创造模型(Q-SECI模型),进而提出知识创造力和知识集成力概念来分析Q-SECI模型知识创造螺旋跃迁机制,并结合企业组织学习的管理实践加以验证与推广。

地表过渡带近地表Q补偿与地表一致性反褶积处理效果对比研究

近地表吸收衰减严重,会引起地震子波的空间变化,地表一致性反褶积和近地表Q补偿是解决这一问题的关键技术。首先描述了基于质心频移的近地表Q计算方法及稳健的Q补偿方法;其次从假设条件、应用范围、受噪声的影响程度以及算子的稳定性等方面对比了近地表Q补偿和地表一致性反褶积的效果。对叠前粘弹波动方程正演模型数据和某近地表由沙漠区向公益林区过渡的三维地震资料,分别利用反褶积地表一致性反褶积和近地表Q补偿技术进行了处理和分析,得出结论:在近地表岩性变化剧烈的区域,激发和接收岩性对地震子波的影响起主要作用时,应使用地表一致性反褶积处理技术;在地表吸收起主要作用的沙漠区,应使用近地表Q补偿技术。