arXiv subjects

Yu-Chen Zhou

Publications and source records attributed to Yu-Chen Zhou.

4 recordsLinked to original sources

arXiv2026

Solving Hamiltonian Constraint Equation with Physics-Informed Neural Networks

Numerical relativity (NR), solving Einstein equation numerically, plays an important role in source modelling for gravitational wave astronomy. Traditional methods for NR including finite difference method, spectral method and finite element method have been well developed. But newly developed neural network methods for partial differential equations (PDE) have not been well studied yet for NR. We present a Physics-Informed Neural Network (PINN) method to solve the Hamiltonian constraint equation for binary black hole (BBH) initial data in NR. This equation is a highly non-linear elliptic PDE, posing significant challenges for conventional PINN approaches. To overcome these difficulties, we introduce a set of new techniques. We show that our PINN together with these techniques can successfully solve the Hamiltonian constraint equation for generic BBH systems. Validation against the traditional results demonstrates the high accuracy and robustness of our method, revealing the immense potential of constructing a PINN-based initial data solution to all BBH systems for NR.

gr-qc↗

arXiv2024

Intermediate-mass-ratio inspirals with general dynamical friction in dark matter minispikes

The intermediate-mass-ratio inspirals (IMRIs) may be surrounded by dark matter (DM) minispikes. The dynamical friction from these DM minispike structures can affect the dynamics and the gravitational wave (GW) emission of the IMRIs. We analyze the effects of general dynamical friction, with a particular contribution from DM particles moving faster than the stellar-mass black hole in an eccentric IMRI. Our calculation show that these DM particles tends to eccentricify the orbit, therefore the evolution of the eccentricity depends on the competition between the fast moving DM particles and the slow moving DM particles. The results show that the dynamical friction enhances the eccentricity when $\gamma_\mathrm{sp}\lesssim2.0$, and the general dynamical friction is able to increase the eccentricity. We also analyze the effects of general dynamical friction on the GW characteristic strain. The results indicate that the characteristic strain is suppressed at lower frequencies, and the peak value of the characteristic strain occurs at higher frequencies as the power law index of DM minispike $\gamma_\mathrm{sp}$ increases. For the first time, a relation between the frequency peak value of characteristic strain of GWs and $\gamma_\mathrm{sp}$ is established. Using this analytical relation, the presence of DM and its halo density may be determined potentially from future GW data.

astro-ph.HE↗

arXiv2023

Singlet-triplet-state readout in silicon-metal-oxide-semiconductor double quantum dots

High-fidelity singlet-triplet state readout is essential for large-scale quantum computing. However, the widely used threshold method of comparing a mean value with the fixed threshold will limit the judgment accuracy, especially for the relaxed triplet state, under the restriction of relaxation time and signal-to-noise ratio. Here, we achieve an enhanced latching readout based on Pauli spin blockade in a Si-MOS double quantum dot device and demonstrate an average singlet-triplet state readout fidelity of 97.59% by the threshold method. We reveal the inherent deficiency of the threshold method for the relaxed triplet state classification and introduce machine learning as a relaxation-independent readout method to reduce the misjudgment. The readout fidelity for classifying the simulated single-shot traces can be improved to 99.67% by machine learning method, better than the threshold method of 97.54% which is consistent with the experimental result. This work indicates that machine learning method can be a strong potential candidate for alleviating the restrictions of stably achieving high-fidelity and high-accuracy singlet-triplet state readout in large-scale quantum computing.

cond-mat.mes-hall↗

arXiv2023

Ultrafast and Electrically Tunable Rabi Frequency in a Germanium Hut Wire Hole Spin Qubit

Hole spin qubits based on germanium (Ge) have strong tunable spin orbit interaction (SOI) and ultrafast qubit operation speed. Here we report that the Rabi frequency (f_Rabi) of a hole spin qubit in a Ge hut wire (HW) double quantum dot (DQD) is electrically tuned through the detuning energy and middle gate voltage (V_M). f_Rabi gradually decreases with increasing detuning energy; on the contrary, f_Rabi is positively correlated with V_M. We attribute our results to the change of electric field on SOI and the contribution of the excited state in quantum dots to f_Rabi. We further demonstrate an ultrafast f_Rabi exceeding 1.2 GHz, which evidences the strong SOI in our device. The discovery of an ultrafast and electrically tunable f_Rabi in a hole spin qubit has potential applications in semiconductor quantum computing.

cond-mat.mes-hall↗