Burst Phase Distribution of SGR J1935+2154 Based on Insight-HXMT

On 2020 April 27,the soft gamma-ray repeater SGR J1935+2154 entered its intense outburst episode again.Insight-HXMT carried out about one month observation of the source.A total number of 75 bursts were detected during this activity episode by Insight-HXMT,and persistent emission data were also accumulated.We report on the spin period search result and the phase distribution of burst start times and burst photon arrival times of the Insight-HXMT high energy detectors and Fermi/Gamma-ray Burst Monitor(GBM).We find that the distribution of burst start times is uniform within its spin phase for both Insight-HXMT and Fermi/GBM observations,whereas the phase distribution of burst photons is related to the type of a burst’s energy spectrum.The bursts with the same spectrum have different distribution characteristics in the initial and decay episodes for the activity of magnetar SGR J1935+2154.

The influence of the Insight-HXMT/LE time response on timing analysis

The LE is the low energy telescope that is carried on Insight-HXMT.It uses swept charge devices(SCDs)to detect soft X-ray photons.LE’s time response is caused by the structure of the SCDs.With theoretical analysis and Monte Carlo simulations we discuss the influence of LE time response(LTR)on the timing analysis from three aspects:the power spectral density,the pulse profile and the time lag.After the LTR,the value of power spectral density monotonously decreases with the increasing frequency.The power spectral density of a sinusoidal signal reduces by a half at frequency 536 Hz.The corresponding frequency for quasi-periodic oscillation(QPO)signals is 458 Hz.The root mean square(RMS)of QPOs holds a similar behaviour.After the LTR,the centroid frequency and full width at half maxima(FWHM)of QPOs signals do not change.The LTR reduces the RMS of pulse profiles and shifts the pulse phase.In the time domain,the LTR only reduces the peak value of the cross-correlation function while it does not change the peak position;thus it will not affect the result of the time lag.When considering the time lag obtained from two instruments and one among them is LE,a 1.18 ms lag is expected caused by the LTR.The time lag calculated in the frequency domain is the same as that in the time domain.

ScQ cloud quantum computation for generating Greenberger-Horne-Zeilinger states of up to 10 qubits

In this study, we introduce an online public quantum computation platform, named as ScQ, based on a 1D array of a 10-qubit superconducting processor. Single-qubit rotation gates can be performed on each qubit. Controlled-NOT gates between nearest-neighbor sites on the 1D array of 10 qubits are available. We show the online preparation and verification of Greenberger-Horne-Zeilinger states of up to 10 qubits through this platform for all possible blocks of qubits in the chain. The graphical user interface and quantum assembly language methods are presented to achieve the above tasks, which rely on a parameter scanning feature implemented on ScQ. The performance of this quantum computation platform, such as fidelities of logic gates and details of the superconducting device, is presented.

Quafu-Qcover:Explore combinatorial optimization problems on cloud-based quantum computers

We introduce Quafu-Qcover,an open-source cloud-based software package developed for solving combinatorial optimization problems using quantum simulators and hardware backends.Quafu-Qcover provides a standardized and comprehensive workflow that utilizes the quantum approximate optimization algorithm(QAOA).It facilitates the automatic conversion of the original problem into a quadratic unconstrained binary optimization(QUBO)model and its corresponding Ising model,which can be subsequently transformed into a weight graph.The core of Qcover relies on a graph decomposition-based classical algorithm,which efficiently derives the optimal parameters for the shallow QAOA circuit.Quafu-Qcover incorporates a dedicated compiler capable of translating QAOA circuits into physical quantum circuits that can be executed on Quafu cloud quantum computers.Compared to a general-purpose compiler,our compiler demonstrates the ability to generate shorter circuit depths,while also exhibiting superior speed performance.Additionally,the Qcover compiler has the capability to dynamically create a library of qubits coupling substructures in real-time,utilizing the most recent calibration data from the superconducting quantum devices.This ensures that computational tasks can be assigned to connected physical qubits with the highest fidelity.The Quafu-Qcover allows us to retrieve quantum computing sampling results using a task ID at any time,enabling asynchronous processing.Moreover,it incorporates modules for results preprocessing and visualization,facilitating an intuitive display of solutions for combinatorial optimization problems.We hope that Quafu-Qcover can serve as an instructive illustration for how to explore application problems on the Quafu cloud quantum computers.

Quafu-RL:The cloud quantum computers based quantum reinforcement learning

With the rapid advancement of quantum computing,hybrid quantum–classical machine learning has shown numerous potential applications at the current stage,with expectations of being achievable in the noisy intermediate-scale quantum(NISQ)era.Quantum reinforcement learning,as an indispensable study,has recently demonstrated its ability to solve standard benchmark environments with formally provable theoretical advantages over classical counterparts.However,despite the progress of quantum processors and the emergence of quantum computing clouds,implementing quantum reinforcement learning algorithms utilizing parameterized quantum circuits(PQCs)on NISQ devices remains infrequent.In this work,we take the first step towards executing benchmark quantum reinforcement problems on real devices equipped with at most 136 qubits on the BAQIS Quafu quantum computing cloud.The experimental results demonstrate that the policy agents can successfully accomplish objectives under modified conditions in both the training and inference phases.Moreover,we design hardware-efficient PQC architectures in the quantum model using a multi-objective evolutionary algorithm and develop a learning algorithm that is adaptable to quantum devices.We hope that the Quafu-RL can be a guiding example to show how to realize machine learning tasks by taking advantage of quantum computers on the quantum cloud platform.