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4U 1954+31 is a high-mass X-ray binary (HMXB) that contains a neutron star and an M supergiant companion. The neutron star has a spin period of ~5.4 hr. The traditional wind-accreting model requires an ultra-strong magnetic field for the neutron star to explain its extremely long spin period, which seems problematic for the neutron star with an age of a few tens of million years. In this work, we take into account the unsteady feature of wind accretion, which results in alternation of the direction of the wind matter's angular momentum. Accordingly, the torque exerted by the accreted wind matter varies between positive and negative from time to time, and largely cancels out over long time. In such a scenario, neutron stars can naturally attain long spin periods without the requirement of a very strong magnetic field. This may also provide a reasonable explanation for the spin period distribution of long-period neutron stars in HMXBs., Comment: 9 pages, 6 figures, accepted for publication in MNRAS

We study the renormalizable group equations (RGEs) of the extended strong and weak gauge couplings in an ${\rm SU}(8)$ theory, where three-generational SM fermions are non-trivially embedded. This framework was previously found to generate the observed SM quark/lepton mass hierarchies and the Cabibbo-Kobayashi-Maskawa mixing pattern through its maximally breaking pattern. The field theoretical two-loop RGEs can not achieve the gauge coupling unification with the minimal setup, unless additional adjoint Higgs fields as well as the gravity-induced $d=5$ term to the ${\rm SU}(8)$ field strength term are included., Comment: 38 pages with references, two appendices, 11 tables, 2 figures. Sequel to: arXiv:2307.07921, arXiv:2402.10471

We propose a detector concept, LAYered CAvern Surface Tracker (LAYCAST), to be installed on the ceiling and the wall of the cavern hosting the main experiment of future electron-positron colliders such as CEPC and FCC-ee. With detailed and realistic considerations of the design of such a new experiment, the proposed detector is dedicated to extending the sensitivity reach of the main detector to various theoretical scenarios of long-lived particles (LLPs). We study carefully four such scenarios involving a light scalar boson $X$, the heavy neutral lepton $N$, the lightest neutralino $\tilde{\chi}^0_1$ in the R-parity-violating supersymmetry, and the axion-like particle $a$. Long-lived light scalar bosons are considered to be produced from the Standard-Model (SM) Higgs boson's decay ($h \to X X$) at the center-of-mass energy $\sqrt{s} =$ 240 GeV, while the other three types of LLPs are produced either from $Z$-boson decays (viz. $Z \to \nu\, N, ~\tilde{\chi}^0_1\, \tilde{\chi}^0_1 $) or direct scattering process ($ e^- e^+ \to ~\gamma\, a$) at $\sqrt{s} =$ 91.2 GeV, where $\gamma$ and $\nu$ denote the SM photon and neutrino, respectively. With Monte-Carlo simulations, we derive the sensitivities of the proposed experiment to these LLPs and the corresponding signal-event numbers. Our findings show that LAYCAST can probe large new parameter space beyond both current bounds and the expected reach of the main experiments at CEPC and FCC-ee. Comparison with existing works in similar directions is also made., Comment: 23 pages, 19 figures, 1 table

This demo paper examines the susceptibility of Federated Learning (FL) systems to targeted data poisoning attacks, presenting a novel system for visualizing and mitigating such threats. We simulate targeted data poisoning attacks via label flipping and analyze the impact on model performance, employing a five-component system that includes Simulation and Data Generation, Data Collection and Upload, User-friendly Interface, Analysis and Insight, and Advisory System. Observations from three demo modules: label manipulation, attack timing, and malicious attack availability, and two analysis components: utility and analytical behavior of local model updates highlight the risks to system integrity and offer insight into the resilience of FL systems. The demo is available at https://github.com/CathyXueqingZhang/DataPoisoningVis.

The model inputs play a key role in the performance of the Bayesian optimization approach. In this paper, we investigate the influence of the inputs on the improved predictions of phenomenological nuclear charge radius formulas using an approach combining those original formulas and the Bayesian neural network (BNN). We find that there is no improvement in predictions after the abnormal odd-even staggering effect of 181,183,185Hg is injected into the BNN, while the original phenomenological formulas themselves possess rich physical information or rigid constraints. It indicates the abundance and intensity of physical inputs affect the performance of the Bayesian optimization approach as well as the robustness of the BNN. We further demonstrate that, by ensuring that the number of neurons in the hidden layer is larger than the number of NN inputs, adding hidden layers into the BNN can significantly improve the predictions of nuclear charge radii formulas within the Bayesian optimization approach., Comment: 10 pages,4 figures, 4 talbes

Despite quantum computing's rapid development, current systems remain limited in practical applications due to their limited qubit count and quality. Various technologies, such as superconducting, trapped ions, and neutral atom quantum computing technologies are progressing towards a fault tolerant era, however they all face a diverse set of challenges in scalability and control. Recent efforts have focused on multi-node quantum systems that connect multiple smaller quantum devices to execute larger circuits. Future demonstrations hope to use quantum channels to couple systems, however current demonstrations can leverage classical communication with circuit cutting techniques. This involves cutting large circuits into smaller subcircuits and reconstructing them post-execution. However, existing cutting methods are hindered by lengthy search times as the number of qubits and gates increases. Additionally, they often fail to effectively utilize the resources of various worker configurations in a multi-node system. To address these challenges, we introduce FitCut, a novel approach that transforms quantum circuits into weighted graphs and utilizes a community-based, bottom-up approach to cut circuits according to resource constraints, e.g., qubit counts, on each worker. FitCut also includes a scheduling algorithm that optimizes resource utilization across workers. Implemented with Qiskit and evaluated extensively, FitCut significantly outperforms the Qiskit Circuit Knitting Toolbox, reducing time costs by factors ranging from 3 to 2000 and improving resource utilization rates by up to 3.88 times on the worker side, achieving a system-wide improvement of 2.86 times.

Quantum state preparation involves preparing a target state from an initial system, a process integral to applications such as quantum machine learning and solving systems of linear equations. Recently, there has been a growing interest in qumodes due to advancements in the field and their potential applications. However there is a notable gap in the literature specifically addressing this area. This paper aims to bridge this gap by providing performance benchmarks of various optimizers used in state preparation with Variational Quantum Algorithms. We conducted extensive testing across multiple scenarios, including different target states, both ideal and sampling simulations, and varying numbers of basis gate layers. Our evaluations offer insights into the complexity of learning each type of target state and demonstrate that some optimizers perform better than others in this context. Notably, the Powell optimizer was found to be exceptionally robust against sampling errors, making it a preferred choice in scenarios prone to such inaccuracies. Additionally, the Simultaneous Perturbation Stochastic Approximation optimizer was distinguished for its efficiency and ability to handle increased parameter dimensionality effectively.

Over the past decade, the rapid advancement of deep learning and big data applications has been driven by vast datasets and high-performance computing systems. However, as we approach the physical limits of semiconductor fabrication in the post-Moore's Law era, questions arise about the future of these applications. In parallel, quantum computing has made significant progress with the potential to break limits. Major companies like IBM, Google, and Microsoft provide access to noisy intermediate-scale quantum (NISQ) computers. Despite the theoretical promise of Shor's and Grover's algorithms, practical implementation on current quantum devices faces challenges, such as demanding additional resources and a high number of controlled operations. To tackle these challenges and optimize the utilization of limited onboard qubits, we introduce ReSaQuS, a resource-efficient index-value searching system within a quantum-classical hybrid framework. Building on Grover's algorithm, ReSaQuS employs an automatically managed iterative search approach. This method analyzes problem size, filters fewer probable data points, and progressively reduces the dataset with decreasing qubit requirements. Implemented using Qiskit and evaluated through extensive experiments, ReSaQuS has demonstrated a substantial reduction, up to 86.36\% in cumulative qubit consumption and 72.72\% in active periods, reinforcing its potential in optimizing quantum computing application deployment.

Recent advancements have highlighted the limitations of current quantum systems, particularly the restricted number of qubits available on near-term quantum devices. This constraint greatly inhibits the range of applications that can leverage quantum computers. Moreover, as the available qubits increase, the computational complexity grows exponentially, posing additional challenges. Consequently, there is an urgent need to use qubits efficiently and mitigate both present limitations and future complexities. To address this, existing quantum applications attempt to integrate classical and quantum systems in a hybrid framework. In this study, we concentrate on quantum deep learning and introduce a collaborative classical-quantum architecture called co-TenQu. The classical component employs a tensor network for compression and feature extraction, enabling higher-dimensional data to be encoded onto logical quantum circuits with limited qubits. On the quantum side, we propose a quantum-state-fidelity-based evaluation function to iteratively train the network through a feedback loop between the two sides. co-TenQu has been implemented and evaluated with both simulators and the IBM-Q platform. Compared to state-of-the-art approaches, co-TenQu enhances a classical deep neural network by up to 41.72% in a fair setting. Additionally, it outperforms other quantum-based methods by up to 1.9 times and achieves similar accuracy while utilizing 70.59% fewer qubits., Comment: IEEE Transactions on Quantum Engineering

The origins of Standard Model (SM) quark/lepton masses and the Cabibbo-Kobayashi-Maskawa (CKM) mixing pattern are described in an ${\rm SU}(8)$ theory, where three-generational SM fermions transform differently. A realistic symmetry breaking pattern with three intermediate scales is described, along which all vectorlike electrically charged fermions become massive. A set of $d=5$ operators that explicitly break the emergent global symmetries are analyzed to generate the observed three-generational SM quark/lepton mass hierarchies as well as the CKM mixing pattern. All SM flavors are identified in the spectrum., Comment: 37 pages with references, one appendix, 8 tables. Sequel to: arXiv:2307.07921

Quantum secret sharing (QSS) is the result of merging the principles of quantum mechanics with secret information sharing. It enables a sender to share a secret among receivers, and the receivers can then collectively recover the secret when the need arises. To enhance the practicality of these quantum protocols, an innovative concept of quantum anonymous secret sharing (QASS) is advanced. In this paper, we propose a QASS protocol via W states, which can share secrets while ensuring recover-ability, recover-security, and recover-anonymity. We have rigorously evaluated our protocols, verifying their accuracy and fortifying their security against scenarios involving the active adversary. This includes considerations for dishonest receivers and non-receivers. Moreover, acknowledging the imperfections inherent in real-world communication channels, we have also undertaken an exhaustive analysis of our protocol's security and effectiveness in a quantum network where some form of noise is present. Our investigations reveal that W states exhibit good performance in mitigating noise interference, making them apt for practical applications., Comment: 18 pages, 6 figures

The rapid advancement of quantum computing has pushed classical designs into the quantum domain, breaking physical boundaries for computing-intensive and data-hungry applications. Given its immense potential, quantum-based computing systems have attracted increasing attention with the hope that some systems may provide a quantum speedup. For example, variational quantum algorithms have been proposed for quantum neural networks to train deep learning models on qubits, achieving promising results. Existing quantum learning architectures and systems rely on single, monolithic quantum machines with abundant and stable resources, such as qubits. However, fabricating a large, monolithic quantum device is considerably more challenging than producing an array of smaller devices. In this paper, we investigate a distributed quantum system that combines multiple quantum machines into a unified system. We propose DQuLearn, which divides a quantum learning task into multiple subtasks. Each subtask can be executed distributively on individual quantum machines, with the results looping back to classical machines for subsequent training iterations. Additionally, our system supports multiple concurrent clients and dynamically manages their circuits according to the runtime status of quantum workers. Through extensive experiments, we demonstrate that DQuLearn achieves similar accuracies with significant runtime reduction, by up to 68.7% and an increase per-second circuit processing speed, by up to 3.99 times, in a 4-worker multi-tenant setting., Comment: IEEE BigData 2023

Quantum computing offers significant speedup compared to classical computing, which has led to a growing interest among users in learning and applying quantum computing across various applications. However, quantum circuits, which are fundamental for implementing quantum algorithms, can be challenging for users to understand due to their underlying logic, such as the temporal evolution of quantum states and the effect of quantum amplitudes on the probability of basis quantum states. To fill this research gap, we propose QuantumEyes, an interactive visual analytics system to enhance the interpretability of quantum circuits through both global and local levels. For the global-level analysis, we present three coupled visualizations to delineate the changes of quantum states and the underlying reasons: a Probability Summary View to overview the probability evolution of quantum states; a State Evolution View to enable an in-depth analysis of the influence of quantum gates on the quantum states; a Gate Explanation View to show the individual qubit states and facilitate a better understanding of the effect of quantum gates. For the local-level analysis, we design a novel geometrical visualization Dandelion Chart to explicitly reveal how the quantum amplitudes affect the probability of the quantum state. We thoroughly evaluated QuantumEyes as well as the novel QuantumEyes integrated into it through two case studies on different types of quantum algorithms and in-depth expert interviews with 12 domain experts. The results demonstrate the effectiveness and usability of our approach in enhancing the interpretability of quantum circuits.

We study the origin of the global $B-L$ symmetry in a class of flavor-unified theories with gauge groups of ${\rm SU}(N\geq 6)$. In particular, we focus on the ${\rm SU}(8)$ theory which can minimally embed three-generational SM fermions non-trivially. A reformulation of the third law for the flavor sector proposed by Georgi is useful to manifest the underlying global symmetries. The 't Hooft anomaly matching and the generalized neutrality conditions for Higgs fields play the key roles in defining the $B-L$ symmetry. Based on the global $B-L$ symmetry, we count the Higgs fields that can develop the VEVs and the massless sterile neutrinos in the ${\rm SU}(8)$ theory. We also prove that a global $B-L$ symmetry can always be defined in any ${\rm SU}(N\geq 6)$ theory when it is spontaneously broken to the SM gauge symmetry., Comment: 35 pages plus references, 11 tables, matches the published version

Two classes of far detectors have been proposed or are under operation at the LHC. The first class is a series of neutrino detectors that are sensitive to light active neutrinos via either charged-current or neutral-current interactions; exemplary ideas are FASER$\nu$, SND@LHC, and FLArE. Another type aims primarily at looking for displaced decays of long-lived particles (LLPs) into charged final-state particles, including ANUBIS and FASER. In this work, we propose searches for probing lepton number violation associated with a Majorana active/sterile neutrino, for the first time with these experiments, which, if discovered, would be a clear signature of new physics beyond the Standard Model. With Monte-Carlo simulation, we find that while the neutrino detectors, unfortunately, are estimated to have signal-event rates orders of magnitude below $\mathcal{O}(1)$, some LLP far detectors such as ANUBIS, if upgraded, would be most promising for discovering a Majorana sterile neutrino of mass $\mathcal{O}(\text{1})$ GeV in certain so-far unexcluded parameter space. In this exploratory work, we emphasize on the importance of leveraging the LHC far detectors for purposes beyond the planned ones, such as searching for lepton number violation., Comment: v1: 17 pages + references, 4 figures, 4 tables; v2: 20 pages + refs, 4 figures, 4 tables, minor changes, accepted for publication in PRD

We propose a novel method to test CP-violation in the heavy (pseudo)scalar sector of an extended Higgs model, in which we make simultaneous use of the $HVV$ ($V=W^\pm, Z$) and $Ht\bar{t}$ interactions of a heavy Higgs state $H$. This is possible at the Compact Linear Collider (CLIC) by exploiting $H$ production from Vector-Boson Fusion (VBF) and decay to $t\bar{t}$ pairs. We analyze the distribution of the azimuthal angle between the leptons coming from top and antitop quarks, that would allow one to disentangle the CP nature of such a heavy Higgs state. We also show its implications for the 2-Higgs-Doublet Model (2HDM) with CP-violation., Comment: 22 pages, 6 figures

Neutron stars (NSs) play essential roles in modern astrophysics. Magnetic fields and spin periods of newborn (zero age) NSs have large impact on the further evolution of NSs, which are however poorly explored in observation due to the difficulty of finding newborn NSs. In this work, we aim to infer the magnetic fields and spin periods (Bi and Pi) of zero-age NSs from the observed properties of NS population. We select non-accretion NSs (NANSs) whose evolution is solely determined by magnetic dipole radiation. We find that both Bi and Pi can be described by log-normal distribution and the fitting sensitively depends on our parameters., Comment: 8 pages, 5 figures, accepted for publication in ApJ

Searching for new physics~(NP) is one of the areas of high-energy physics that requires the most processing of large amounts of data. At the same time, quantum computing has huge potential advantages when dealing with large amounts of data. The principal component analysis~(PCA) algorithm may be one of the bridges connecting these two aspects. On the one hand, it can be used for anomaly detection, and on the other hand, there are corresponding quantum algorithms for PCA. In this paper, we investigate how to use PCA to search for NP. Taking the example of anomalous quartic gauge couplings in the tri-photon process at muon colliders, we find that PCA can be used to search for NP. Compared with the traditional event selection strategy, the expected constraints on the operator coefficients obtained by PCA based event selection strategy are even better., Comment: correct one of the author names

Visualizations have played a crucial role in helping quantum computing users explore quantum states in various quantum computing applications. Among them, Bloch Sphere is the widely-used visualization for showing quantum states, which leverages angles to represent quantum amplitudes. However, it cannot support the visualization of quantum entanglement and superposition, the two essential properties of quantum computing. To address this issue, we propose VENUS, a novel visualization for quantum state representation. By explicitly correlating 2D geometric shapes based on the math foundation of quantum computing characteristics, VENUS effectively represents quantum amplitudes of both the single qubit and two qubits for quantum entanglement. Also, we use multiple coordinated semicircles to naturally encode probability distribution, making the quantum superposition intuitive to analyze. We conducted two well-designed case studies and an in-depth expert interview to evaluate the usefulness and effectiveness of VENUS. The result shows that VENUS can effectively facilitate the exploration of quantum states for the single qubit and two qubits.

Medical image segmentation methods are generally designed as fully-supervised to guarantee model performance, which require a significant amount of expert annotated samples that are high-cost and laborious. Semi-supervised image segmentation can alleviate the problem by utilizing a large number of unlabeled images along with limited labeled images. However, learning a robust representation from numerous unlabeled images remains challenging due to potential noise in pseudo labels and insufficient class separability in feature space, which undermines the performance of current semi-supervised segmentation approaches. To address the issues above, we propose a novel semi-supervised segmentation method named as Rectified Contrastive Pseudo Supervision (RCPS), which combines a rectified pseudo supervision and voxel-level contrastive learning to improve the effectiveness of semi-supervised segmentation. Particularly, we design a novel rectification strategy for the pseudo supervision method based on uncertainty estimation and consistency regularization to reduce the noise influence in pseudo labels. Furthermore, we introduce a bidirectional voxel contrastive loss to the network to ensure intra-class consistency and inter-class contrast in feature space, which increases class separability in the segmentation. The proposed RCPS segmentation method has been validated on two public datasets and an in-house clinical dataset. Experimental results reveal that the proposed method yields better segmentation performance compared with the state-of-the-art methods in semi-supervised medical image segmentation. The source code is available at https://github.com/hsiangyuzhao/RCPS.

Because of the difficulty in detecting final state taus, the mixing parameter $|V_{\tau N}|^2$ for heavy neutrino $N$ is not well studied at current experiments, compared with other mixing parameters $|V_{e N}|^2$ and $|V_{\mu N}|^2$. In this paper, we focus on a challenging scenario where $N$ mixes with active neutrino of tau flavour only, i.e. $ |V_{\tau N}|^2 \neq 0 $ and $|V_{e N}|^2 = |V_{\mu N}|^2 = 0$. We derive current constraints on $|V_{\tau N}|^2$ from the rare $Z$-boson decay and electroweak precision data (EWPD). To forecast the future limits, we also investigate the signal $p p \to \tau^{\pm} \tau^{\pm} j j $ via a Majorana heavy neutrino at future proton-proton colliders. To suppress the background, both taus are required to decay leptonically into muons, leading to the final state containing two same sign muons, at least two jets plus moderate missing energy. The signal and relevant background processes are simulated at the HL-LHC and SppC/FCC-hh with center-of-mass energy of 14 TeV and 100 TeV. The preselection and multivariate analyses based on machine-learning are performed to reduce background. Limits on $|V_{\tau N}|^2$ are shown for heavy neutrino mass in the range 10-1000 GeV based on measurements from the rare $Z$-boson decay and EWPD, and searches at the HL-LHC and SppC/FCC-hh with integrated luminosities of 3 and 20 ab$^{-1}$., Comment: 9 figures, 4 tables. arXiv admin note: text overlap with arXiv:2210.17050

We utilize the lepton number violation signal process $p\, e^- \to \tau^+ jjj$ to search for heavy Majorana neutrinos at future proton-electron colliders. The LHeC (FCC-eh) is considered to run with an electron beam energy of 60 GeV, a proton beam energy of 7 (50) TeV and an integrated luminosity of 1 (3) ab$^{-1}$, and the electron beam is considered to be unpolarized. We apply detector configurations and simulate signal and related standard model background events for both hadronic $\tau_h$ and leptonic $\tau_\ell$ final states, $\ell$ being a muon. After preselection, multivariate analyses are performed to reject the background. The strategy to reconstruct the heavy neutrino mass is developed and distributions of reconstructed mass are presented. Discovery sensitivities on parameter $|V_{\tau N}|^2 |V_{eN}|^2 / ( |V_{\tau N}|^2 + |V_{eN}|^2 )$ for the heavy neutrino mass between 10 and 3000 GeV are predicted. At the 2-$\sigma$ significance, the best discovery sensitivity is $\sim 1.2 \times10^{-5} \,\,(5.0 \times 10^{-6})$ at the LHeC (FCC-eh) when $m_N \sim 100$ GeV for the hadronic $\tau_h$ final state. Sensitivities for the leptonic $\tau_\ell$ final state are found to be similar to those for the hadronic $\tau_h$ final state for most of the parameter space investigated. We also derive the limits on mixing parameters from electroweak precision data (EWPD) and DELPHI experiment. Assuming $|V_{\tau N}|^2 = |V_{eN}|^2 = |V_{\ell N}|^2$, sensitivity bounds from the LHeC and FCC-eh experiments are found to be stronger than those from EWPD when $m_N \lesssim 900$ GeV, and also stronger than those from DELPHI when $m_N \gtrsim 70$ GeV. Constraints are also interpreted and compared in the $|V_{\tau N}|^2$ vs. $|V_{e N}|^2$ plane., Comment: 17 figures, 6 tables. Updates: new background process B5a and analysis for the hadronic $\tau_h$ final state are updated; typos are fixed. arXiv admin note: text overlap with arXiv:2201.12997

Quantum Machine Learning continues to be a highly active area of interest within Quantum Computing. Many of these approaches have adapted classical approaches to the quantum settings, such as QuantumFlow, etc. We push forward this trend and demonstrate an adaption of the Classical Convolutional Neural Networks to quantum systems - namely QuCNN. QuCNN is a parameterised multi-quantum-state based neural network layer computing similarities between each quantum filter state and each quantum data state. With QuCNN, back propagation can be achieved through a single-ancilla qubit quantum routine. QuCNN is validated by applying a convolutional layer with a data state and a filter state over a small subset of MNIST images, comparing the back propagated gradients, and training a filter state against an ideal target state.

We study a possible gauge symmetry breaking pattern in an ${\rm SU}(7)$ grand unified theory, which describes the mass origins of all electrically charged SM fermions of the second and the third generations. Two intermediate gauge symmetries of ${\cal G}_{341}\equiv {\rm SU}(3)_c \otimes {\rm SU}(4)_W \otimes {\rm U}(1)_{X_0}$ and ${\cal G}_{331}\equiv {\rm SU}(3)_c \otimes {\rm SU}(3)_W \otimes {\rm U}(1)_{X_1}$ arise above the electroweak scale. SM fermion mass hierarchies between two generations can be obtained through a generalized seesaw mechanism. The mechanism can be achieved with suppressed symmetry breaking VEVs from multiple Higgs fields that are necessary to avoid tadpole terms in the Higgs potential. Some general features of the ${\rm SU}(7)$ fermion spectrum will be described, which include the existence of vectorlike fermions, the tree-level flavor changing weak currents between the SM fermions and heavy partner fermions, and the flavor non-universality between different SM generations from the extended weak sector., Comment: 55 pages, 1 figure, 14 tables, preprint matches the published version

Recent advances in quantum computing systems attract tremendous attention. Commercial companies, such as IBM, Amazon, and IonQ, have started to provide access to noisy intermediate-scale quantum computers. Researchers and entrepreneurs attempt to deploy their applications that aim to achieve a quantum speedup. Grover's algorithm and quantum phase estimation are the foundations of many applications with the potential for such a speedup. While these algorithms, in theory, obtain marvelous performance, deploying them on existing quantum devices is a challenging task. For example, quantum phase estimation requires extra qubits and a large number of controlled operations, which are impractical due to low-qubit and noisy hardware. To fully utilize the limited onboard qubits, we propose IQuCS, which aims at index searching and counting in a quantum-classical hybrid system. IQuCS is based on Grover's algorithm. From the problem size perspective, it analyzes results and tries to filter out unlikely data points iteratively. A reduced data set is fed to the quantum computer in the next iteration. With a reduction in the problem size, IQuCS requires fewer qubits iteratively, which provides the potential for a shared computing environment. We implement IQuCS with Qiskit and conduct intensive experiments. The results demonstrate that it reduces qubits consumption by up to 66.2%.

Quantum computing has attracted considerable public attention due to its exponential speedup over classical computing. Despite its advantages, today's quantum computers intrinsically suffer from noise and are error-prone. To guarantee the high fidelity of the execution result of a quantum algorithm, it is crucial to inform users of the noises of the used quantum computer and the compiled physical circuits. However, an intuitive and systematic way to make users aware of the quantum computing noise is still missing. In this paper, we fill the gap by proposing a novel visualization approach to achieve noise-aware quantum computing. It provides a holistic picture of the noise of quantum computing through multiple interactively coordinated views: a Computer Evolution View with a circuit-like design overviews the temporal evolution of the noises of different quantum computers, a Circuit Filtering View facilitates quick filtering of multiple compiled physical circuits for the same quantum algorithm, and a Circuit Comparison View with a coupled bar chart enables detailed comparison of the filtered compiled circuits. We extensively evaluate the performance of VACSEN through two case studies on quantum algorithms of different scales and an in-depth interviews with 12 quantum computing users. The results demonstrate the effectiveness and usability of VACSEN in achieving noise-aware quantum computing.

We study a toy ${\rm SU}(6)$ model with the symmetry breaking pattern of the extended $331$ symmetry of ${\rm SU}(3)_c \otimes {\rm SU}(3)_W \otimes {\rm U}(1)_X$. A "fermion-Higgs mismatching" symmetry breaking pattern is proposed for more realistic model building. Within such symmetry breaking pattern, only one Higgs doublet develops vacuum expectation value for the spontaneous electroweak symmetry breaking, and gives tree-level top quark mass. A natural VEV splittings in the $331$ breaking Higgs fields gives tree-level masses to both bottom quark and tau lepton. The $125\,{\rm GeV}$ SM-like Higgs boson discovered at the LHC can have Yukawa couplings to bottom quark and tau lepton as in the SM prediction, and this suggests the $331$ symmetry breaking scale to be $\sim {\cal O}(10)\,{\rm TeV}$., Comment: 32 pages, 2 tables, one appendix, matches the published version