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Jun-Chao Wang

Publications and source records attributed to Jun-Chao Wang.

8 recordsLinked to original sources

Probing Non-Cold Dark Matter with Modified Emergent Dark Energy

In the standard $Λ$CDM cosmology, dark matter is assumed to be a pressureless cold fluid with $w_{\rm dm}=0$. However, the microscopic nature of dark matter remains unknown, and whether its equation-of-state parameter strictly vanishes deserves observational scrutiny. In this work, we introduce a free dark matter equation-of-state parameter $w_{\rm dm}$ within the Modified Emergent Dark Energy (MEDE) framework, constructing the MEDE+$w_{\rm dm}$ model. We systematically derive its background evolution and linear perturbation equations, and constrain the model parameters using Planck 2018 cosmic microwave background (CMB), DESI DR2 baryon acoustic oscillation (BAO), and three independent Type Ia supernova datasets: Pantheon+, Union3, and DES5YR. Using the CMB + BAO + DES5YR combination, we find a preference for a positive dark matter equation of state, $w_{\rm dm}=0.00128\pm0.00044$, together with a 3$σ$ level preference for quintessence-like dark energy evolution, $α=-0.66\pm0.22$. When the local $H_0$ prior is included, the constraint on $w_{\rm dm}$ remains essentially unchanged, whereas $α$ shifts toward the $Λ$CDM limit, yielding $α=-0.18\pm0.18$. Bayesian model comparison favors $Λ$CDM over MEDE+$w_{\rm dm}$, although the preference is reduced to the weak level after including the local $H_0$ prior. Overall, current observations exhibit a $2.6σ$--$3σ$ preference for a nonzero $w_{\rm dm}$ at the parameter-posterior level, but this indication does not yet constitute a robust detection of non-cold dark matter.

physics.gen-ph

Towards Fault-Tolerant Quantum Deep Learning: Designing and Analyzing Quantum ResNet and Transformer with Quantum Arithmetic and Linear Algebra Primitives

Achieving a practical quantum speedup for deep neural networks (DNNs) remains a central yet elusive goal, hindered by the dual challenges of constructing deep architectures and the prohibitive overhead of data loading and measurement. We introduce a framework to overcome these barriers, specifically targeting an asymptotic speedup with respect to the large input dimensions of modern DNNs (e.g., sequence length or image size). Our framework enables the design of multi-layer Quantum ResNet and Quantum Transformer models by strategically decomposing tasks: computationally intensive operations on the large input dimension are assigned to quantum linear algebra subroutines, while operations on the smaller, fixed feature dimension are handled by efficient quantum arithmetic. A cornerstone of our approach is a novel data transfer protocol, Discrete Chebyshev Decomposition (DCD), which facilitates this modularity. Numerical validation reveals a pivotal insight: the measurement cost required to maintain a target accuracy scales sublinearly with the input dimension. This sublinear scaling is the key to preserving the quantum advantage, ensuring that I/O overhead does not nullify the computational gains. A rigorous resource analysis further corroborates the superiority of our models in both efficiency and flexibility. Powered by this targeted acceleration strategy and the efficiency of DCD, our framework establishes a viable path toward scalable quantum deep learning.

quant-ph

A new unified dark sector model and its implications on the $σ_8$ and $S_8$ tensions

In this paper, we introduced the Unified Three-Form Dark Sector (UTFDS) model, a unified dark sector model that combines dark energy and dark matter through a three-form field. In this framework, the potential of the three-form field acts as dark matter, while the kinetic term represents dark energy. The interaction between dark matter and dark energy is driven by the energy exchange between these two terms. Given the dynamical equations of UTFDS, we provide an autonomous system of evolution equations for UTFDS and perform a stability analysis of its fixed points. The result aligns with our expectations for a unified dark sector. Furthermore, we discover that the dual Lagrangian of the UTFDS Lagrangian is equivalent to a Dirac-Born-Infeld (DBI) Lagrangian. By fixing the parameter $κX_0$ to 250, 500, 750, we refer to the resulting models as the $\overline{\rm UTFDS}$ model with $κX_0$=250, 500, 750, respectively. We then place constraints on these three $\overline{\rm UTFDS}$ models and the $Λ$CDM model in light of the Planck 2018 Cosmic Microwave Background (CMB) anisotropies, Redshift Space Distortions (RSD) observations, Baryon Acoustic Oscillation (BAO) measurements, and the $S_8$ prior chosen according to the KiDS1000 Weak gravitational Lensing (WL) measuement. We find that the $\overline{\rm UTFDS}$ model with $κX_0$=500 is the only one among the four models where both $σ_8$ and $S_8$ tensions, between CMB and RSD+BAO+WL datasets, are below 2.0$σ$. Furthermore, the tensions are relieved without exacerbating the $H_0$ tension. Although both the CMB and RSD+BAO+WL datasets provide definite/positive evidence favoring $Λ$CDM over the $\overline{\rm UTFDS}$ model with $κX_0$=500, the evidence is not strong enough to rule out further study of this model.

astro-ph.CO

HiMA: Hierarchical Quantum Microarchitecture for Qubit-Scaling and Quantum Process-Level Parallelism

Quantum computing holds immense potential for addressing a myriad of intricate challenges, which is significantly amplified when scaled to thousands of qubits. However, a major challenge lies in developing an efficient and scalable quantum control system. To address this, we propose a novel Hierarchical MicroArchitecture (HiMA) designed to facilitate qubit scaling and exploit quantum process-level parallelism. This microarchitecture is based on three core elements: (i) discrete qubit-level drive and readout, (ii) a process-based hierarchical trigger mechanism, and (iii) multiprocessing with a staggered triggering technique to enable efficient quantum process-level parallelism. We implement HiMA as a control system for a 72-qubit tunable superconducting quantum processing unit, serving a public quantum cloud computing platform, which is capable of expanding to 6144 qubits through three-layer cascading. In our benchmarking tests, HiMA achieves up to a 4.89x speedup under a 5-process parallel configuration. Consequently, to the best of our knowledge, we have achieved the highest CLOPS (Circuit Layer Operations Per Second), reaching up to 43,680, across all publicly available platforms.

cs.AR

Enabling Large-Scale and High-Precision Fluid Simulations on Near-Term Quantum Computers

Quantum computational fluid dynamics (QCFD) offers a promising alternative to classical computational fluid dynamics (CFD) by leveraging quantum algorithms for higher efficiency. This paper introduces a comprehensive QCFD method, including an iterative method "Iterative-QLS" that suppresses error in quantum linear solver, and a subspace method to scale the solution to a larger size. We implement our method on a superconducting quantum computer, demonstrating successful simulations of steady Poiseuille flow and unsteady acoustic wave propagation. The Poiseuille flow simulation achieved a relative error of less than $0.2\%$, and the unsteady acoustic wave simulation solved a 5043-dimensional matrix. We emphasize the utilization of the quantum-classical hybrid approach in applications of near-term quantum computers. By adapting to quantum hardware constraints and offering scalable solutions for large-scale CFD problems, our method paves the way for practical applications of near-term quantum computers in computational science.

physics.comp-ph

Observational constraints on noncold dark matter and phenomenological emergent dark energy

It is well known that there are several long-standing problems implying the discordance of the $Λ$CDM model. Although most of the models proposed to resolve these problems assume that dark matter is pressureless, it is still possible that dark matter is not cold, as current observations have not ruled out this possibility yet. Therefore, in this article, we treat the dark matter equation of state parameter as a free parameter, and apply observational data to investigate the non-coldness of dark matter. Impressing by the simplicity of the phenomenological emergent dark energy (PEDE) and its ability to relieve the Hubble tension, we propose the PEDE+$w_{\rm dm}$ model based on PEDE and non-cold dark matter. We then place constraints on this model in light of the Planck 2018 Cosmic Microwave Background (CMB) anisotropies, baryon acoustic oscillation (BAO) measurements, and the Pantheon compilation of Type Ia supernovae. The results indicate a preference for a negative dark matter equation of state parameter at $95\%$ CL for all data sets except CMB alone and CMB+BAO, which suggests that the non-coldness assumption of dark matter worth to be investigated further in order to understand the nature of dark matter. The Hubble tension is alleviated in this scenario compared to the $Λ$CDM model, with a significance below 3$σ$ level for all data sets except CMB+Pantheon. However, from the analysis based on Bayesian evidence, we clearly see that the data sets favor $Λ$CDM over the PEDE+$w_{\rm dm}$ model.

astro-ph.CO

Exploring the deviation of cosmological constant by a generalized pressure dark energy model

We bring forward a generalized pressure dark energy (GPDE) model to explore the evolution of the universe. This model has covered three common pressure parameterization types and can be reconstructed as quintessence and phantom scalar fields, respectively. We adopt the cosmic chronometer (CC) datasets to constrain the parameters. The results show that the inferred late-universe parameters of the GPDE model are (within $1σ$): The present value of Hubble constant $H_{0}=(72.30^{+1.26}_{-1.37})$km s$^{-1}$ Mpc$^{-1}$; Matter density parameter $Ω_{\text{m0}}=0.302^{+0.046}_{-0.047}$, and the universe bias towards quintessence. While when we combine CC data and the $H_0$ data from Planck, the constraint implies that our model matches the $Λ$CDM model nicely. Then we perform dynamic analysis on the GPDE model and find that there is an attractor or a saddle point in the system corresponding to the different values of parameters. Finally, we discuss the ultimate fate of the universe under the phantom scenario in the GPDE model. It is demonstrated that three cases of pseudo rip, little rip, and big rip are all possible.

astro-ph.CO

A pressure parametric dark energy model

In this paper, we propose a new pressure parametric model of the total cosmos energy components in a spatially flat Friedmann-Robertson-Walker (FRW) universe and then reconstruct the model into quintessence and phantom scenarios, respectively. By constraining with the datasets of the type Ia supernova (SNe Ia), the baryon acoustic oscillation (BAO) and the observational Hubble parameter data(OHD), we find that $Ω_{m0}=0.270^{+0.039}_{-0.034}$ at the 1$σ$ level and our universe slightly biases towards quintessence behavior. Then we use two diagnostics including $Om(a)$ diagnostic and statefinder to discriminate our model from the cosmology constant cold dark matter ($Λ$CDM) model. From $Om(a)$ diagnostic, we find that our model has a relatively large deviation from the $Λ$CDM model at high redshifts and gradually approaches the $Λ$CDM model at low redshifts and in the future evolution, but they can be easily differentiated from each other at the 1$σ$ level all along. By the statefinder, we find that both of quintessence case and phantom case can be well distinguished from the $Λ$CDM model and will gradually deviate from each other. Finally, we discuss the fate of universe evolution (named the rip analysis) for the phantom case of our model and find that the universe will run into a little rip stage.

astro-ph.CO