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.

Effects of cooling rates on microporosity in DC casting Al-Li alloy

During the direct chill(DC)casting process,primary cooling from the mold and bottom block,and secondary cooling from the waterjets produce a concave solid shell.The depth of this liquid pocket and mushy zone not only depends on the solidification range of the alloy but also the boundary conditions such as cooling rates.Al-Li alloys solidify in a long solidification range increasing the susceptibility of porosity nucleation in the semi-solid region.In this study,the effects of cooling rate on the porosity formation were quantified for the large ingot casting using X-ray computed tomography(XCT).By characterizing pore size distributions at four different cooling conditions,the correlation between the mechanical properties at both room and high temperatures and the microstructure features was identified.The constitutive equations were constructed.It is found that increasing the cooling rate reduces the grain size,increases the number density of micropores,and minimizes the number of large pores,thereby improving the mechanical performance.Therefore,long mushy zones and deep liquid pockets in Al-Li alloys can be effectively controlled by controlling the boundary conditions of the DC casting solidification process,thereby obtaining castings with excellent mechanical properties.

Beating standard quantum limit via two-axis magnetic susceptibility measurement

We report a metrology scheme which measures the magnetic susceptibility of an atomic spin ensemble along the x and z directions and produces parameter estimation with precision beating the standard quantum limit.The atomic ensemble is initialized via one-axis spin squeezing with optimized squeezing time and parameterΦ(to be estimated)assumed as uniformly distributed between 0 and 2πwhile fixed in each estimation.One estimation ofΦcan be produced with every two magnetic susceptibility data measured along the two axes respectively,which has an imprecision scaling(1.43±0.02)/N^(0.687±0.003)with respect to the number N of the atomic spins.The measurement scheme is easy to implement and is robust against the measurement fluctuation caused by environment noise and measurement defects.

Research progress of pattern recognition receptors and chronic periodontitis

Pattern recognition receptor(PRR)is a kind of sensor which is mainly expressed on the surface of innate immune cells.It can recognize pathogen related molecular patterns(PAMPs)or damage related molecular patterns(DAMPs).The innate immune system uses pattern recognition receptors to recognize pathogenic microorganisms in periodontal tissues and transmit signals to downstream pathways in time,thus triggering immune responses and then eliminating them.PRR has many family members,including toll like receptor family(TLRs),C-type lectin receptor family(CLRs),retinoic acid induced gene I(RIG-I)like receptor family(RLRs)and nucleotide binding oligomer domain(NOD)like receptor family(NLRs).Among them,RLRs are cytoplasmic receptors that recognize dsRNA from RNA viruses and have little association with chronic periodontitis.In this paper,the classification and structure of TLRs,CLRs,NLRs and the role of signal transduction pathway in chronic periodontitis are reviewed.In order to enrich the pathogenesis of periodontitis,provide new ideas for the treatment and prevention of chronic periodontitis.

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.