基于开源Quantum ESPRESSO软件的固体物理教学模式创新与实践

为了解决固体物理课程学习中的难点,授课时引入开源Quantum ESPRESSO软件为学生提供全面的理论学习和实践训练。实践训练分为理论学习和实际操作两个阶段,使学生在理解固体物理的难点的同时获得实际操作经验。通过自主学习、实验报告的撰写和实操演示等分层次的学习方式,学生逐渐提升对固体物理的整体理解水平。学生对这一学习方式的反应良好。Quantum ESPRESSO软件为学生提供了先进的学习工具,有效提高了固体物理课程的学习效果。

N-doped graphene quantum dot-decorated N-TiO2/P-doped porous hollow g-C_(3)N_(4) nanotube composite photocatalysts for antibiotic photodegradation and H2 production

Exclusive responsiveness to ultraviolet light (~3.2 eV) and high photogenerated charge recombination rate are the two primary drawbacks of pure TiO_(2). We combined N-doped graphene quantum dots (N-GQDs), morphology regulation, and heterojunction construction strategies to synthesize N-GQD/N-doped TiO_(2)/P-doped porous hollow g-C_(3)N_(4) nanotube (PCN) composite photocatalysts (denoted as G-TPCN). The optimal sample (G-TPCN doped with 0.1wt% N-GQD, denoted as 0.1% G-TPCN) exhibits significantly enhanced photoabsorption, which is attributed to the change in bandgap caused by elemental doping (P and N), the improved light-harvesting resulting from the tube structure, and the upconversion effect of N-GQDs. In addition, the internal charge separation and transfer capability of0.1% G-TPCN are dramatically boosted, and its carrier concentration is 3.7, 2.3, and 1.9 times that of N-TiO_(2), PCN, and N-TiO_(2)/PCN(TPCN-1), respectively. This phenomenon is attributed to the formation of Z-scheme heterojunction between N-TiO_(2) and PCNs, the excellent electron conduction ability of N-GQDs, and the short transfer distance caused by the porous nanotube structure. Compared with those of N-TiO_(2), PCNs, and TPCN-1, the H2 production activity of 0.1%G-TPCN under visible light is enhanced by 12.4, 2.3, and 1.4times, respectively, and its ciprofloxacin (CIP) degradation rate is increased by 7.9, 5.7, and 2.9 times, respectively. The optimized performance benefits from excellent photoresponsiveness and improved carrier separation and migration efficiencies. Finally, the photocatalytic mechanism of 0.1% G-TPCN and five possible degradation pathways of CIP are proposed. This study clarifies the mechanism of multiple modification strategies to synergistically improve the photocatalytic performance of 0.1% G-TPCN and provides a potential strategy for rationally designing novel photocatalysts for environmental remediation and solar energy conversion.

Threshold-independent method for single-shot readout of spin qubits in semiconductor quantum dots

The single-shot readout data process is essential for the realization of high-fidelity qubits and fault-tolerant quantum algorithms in semiconductor quantum dots. However, the fidelity and visibility of the readout process are sensitive to the choice of the thresholds and limited by the experimental hardware. By demonstrating the linear dependence between the measured spin state probabilities and readout visibilities along with dark counts, we describe an alternative threshold-independent method for the single-shot readout of spin qubits in semiconductor quantum dots. We can obtain the extrapolated spin state probabilities of the prepared probabilities of the excited spin state through the threshold-independent method. We then analyze the corresponding errors of the method, finding that errors of the extrapolated probabilities cannot be neglected with no constraints on the readout time and threshold voltage. Therefore, by limiting the readout time and threshold voltage, we ensure the accuracy of the extrapolated probability. We then prove that the efficiency and robustness of this method are 60 times larger than those of the most commonly used method. Moreover, we discuss the influence of the electron temperature on the effective area with a fixed external magnetic field and provide a preliminary demonstration for a single-shot readout of up to 0.7K/1.5T in the future.

Quantum circuit-based proxy blind signatures:A novel approach and experimental evaluation on the IBM quantum cloud platform

This paper presents a novel approach to proxy blind signatures in the realm of quantum circuits,aiming to enhance security while safeguarding sensitive information.The main objective of this research is to introduce a quantum proxy blind signature(QPBS)protocol that utilizes quantum logical gates and quantum measurement techniques.The QPBS protocol is constructed by the initial phase,proximal blinding message phase,remote authorization and signature phase,remote validation,and de-blinding phase.This innovative design ensures a secure mechanism for signing documents without revealing the content to the proxy signer,providing practical security authentication in a quantum environment under the assumption that the CNOT gates are securely implemented.Unlike existing approaches,our proposed QPBS protocol eliminates the need for quantum entanglement preparation,thus simplifying the implementation process.To assess the effectiveness and robustness of the QPBS protocol,we conduct comprehensive simulation studies in both ideal and noisy quantum environments on the IBM quantum cloud platform.The results demonstrate the superior performance of the QPBS algorithm,highlighting its resilience against repudiation and forgeability,which are key security concerns in the realm of proxy blind signatures.Furthermore,we have established authentic security thresholds(82.102%)in the presence of real noise,thereby emphasizing the practicality of our proposed solution.

Quantum synchronization with correlated baths

We study quantum synchronization under the nonequilibrium reservoirs.We consider a two-qubit XXZ chain coupled independently to their own reservoirs modeled by the collisional model.Two reservoir particles,initially prepared in a thermal state or a state with coherence,are correlated through a unitary transformation and afterward interact locally with the two quantum subsystems.We study the quantum effect of reservoir on synchronous dynamics of system.By preparing different reservoir initial states or manipulating the reservoir particles coupling and the temperature gradient,we find that quantum entanglement of reservoir is the key to control quantum synchronization of system qubits.

Design of a novel hybrid quantum deep neural network in INEQR images classification

We redesign the parameterized quantum circuit in the quantum deep neural network, construct a three-layer structure as the hidden layer, and then use classical optimization algorithms to train the parameterized quantum circuit, thereby propose a novel hybrid quantum deep neural network(HQDNN) used for image classification. After bilinear interpolation reduces the original image to a suitable size, an improved novel enhanced quantum representation(INEQR) is used to encode it into quantum states as the input of the HQDNN. Multi-layer parameterized quantum circuits are used as the main structure to implement feature extraction and classification. The output results of parameterized quantum circuits are converted into classical data through quantum measurements and then optimized on a classical computer. To verify the performance of the HQDNN, we conduct binary classification and three classification experiments on the MNIST(Modified National Institute of Standards and Technology) data set. In the first binary classification, the accuracy of 0 and 4 exceeds98%. Then we compare the performance of three classification with other algorithms, the results on two datasets show that the classification accuracy is higher than that of quantum deep neural network and general quantum convolutional neural network.

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.

Enhancing quantum metrology for multiple frequencies of oscillating magnetic fields by quantum control

Quantum multi-parameter estimation has recently attracted increased attention due to its wide applications, with a primary goal of designing high-precision measurement schemes for unknown parameters. While existing research has predominantly concentrated on time-independent Hamiltonians, little has been known about quantum multi-parameter estimation for time-dependent Hamiltonians due to the complexity of quantum dynamics. This work bridges the gap by investigating the precision limit of multi-parameter quantum estimation for a qubit in an oscillating magnetic field model with multiple unknown frequencies. As the well-known quantum Cramer–Rao bound is generally unattainable due to the potential incompatibility between the optimal measurements for different parameters, we use the most informative bound instead which is always attainable and equivalent to the Holevo bound in the asymptotic limit. Moreover, we apply additional Hamiltonian to the system to engineer the dynamics of the qubit. By utilizing the quasi-Newton method, we explore the optimal schemes to attain the highest precision for the unknown frequencies of the magnetic field, including the simultaneous optimization of initial state preparation, the control Hamiltonian and the final measurement. The results indicate that the optimization can yield much higher precisions for the field frequencies than those without the optimizations. Finally,we study the robustness of the optimal control scheme with respect to the fluctuation of the interested frequencies, and the optimized scheme exhibits superior robustness to the scenario without any optimization.

Single-photon scattering and quantum entanglement of two giant atoms with azimuthal angle differences in a waveguide system

We theoretically investigate coherent scattering of single photons and quantum entanglement of two giant atoms with azimuthal angle differences in a waveguide system.Using the real-space Hamiltonian,analytical expressions are derived for the transport spectra scattered by these two giant atoms with four azimuthal angles.Fano-like resonance can be exhibited in the scattering spectra by adjusting the azimuthal angle difference.High concurrence of the entangled state for two atoms can be implemented in a wide angle-difference range,and the entanglement of the atomic states can be switched on/off by modulating the additional azimuthal angle differences from the giant atoms.This suggests a novel handle to effectively control the single-photon scattering and quantum entanglement.

Proposal for sequential Stern-Gerlach experiment with programmable quantum processors

The historical significance of the Stern–Gerlach(SG)experiment lies in its provision of the initial evidence for space quantization.Over time,its sequential form has evolved into an elegant paradigm that effectively illustrates the fundamental principles of quantum theory.To date,the practical implementation of the sequential SG experiment has not been fully achieved.In this study,we demonstrate the capability of programmable quantum processors to simulate the sequential SG experiment.The specific parametric shallow quantum circuits,which are suitable for the limitations of current noisy quantum hardware,are given to replicate the functionality of SG devices with the ability to perform measurements in different directions.Surprisingly,it has been demonstrated that Wigner’s SG interferometer can be readily implemented in our sequential quantum circuit.With the utilization of the identical circuits,it is also feasible to implement Wheeler’s delayed-choice experiment.We propose the utilization of cross-shaped programmable quantum processors to showcase sequential experiments,and the simulation results demonstrate a strong alignment with theoretical predictions.With the rapid advancement of cloud-based quantum computing,such as BAQIS Quafu,it is our belief that the proposed solution is well-suited for deployment on the cloud,allowing for public accessibility.Our findings not only expand the potential applications of quantum computers,but also contribute to a deeper comprehension of the fundamental principles underlying quantum theory.

Sharing quantum nonlocality in the noisy scenario

It was showed in [Phys. Rev. Lett. 125 090401(2020)] that there exist unbounded number of independent Bobs who can share quantum nonlocality with a single Alice by performing sequentially measurements on the Bob's half of the maximally entangled pure two-qubit state. However, from practical perspectives, errors in entanglement generation and noises in quantum measurements will result in the decay of nonlocality in the scenario. In this paper, we analyze the persistency and termination of sharing nonlocality in the noisy scenario. We first obtain the two sufficient conditions under which there exist n independent Bobs who can share nonlocality with a single Alice under noisy measurements and the noisy initial two qubit entangled state. Analyzing the two conditions, we find that the influences on persistency under different kinds of noises can cancel each other out. Furthermore, we describe the change patterns of the maximal nonlocality-sharing number under the influence of different noises. Finally, we extend our investigation to the case of arbitrary finite-dimensional systems.

Integer multiple quantum image scaling based on NEQR and bicubic interpolation

As a branch of quantum image processing,quantum image scaling has been widely studied.However,most of the existing quantum image scaling algorithms are based on nearest-neighbor interpolation and bilinear interpolation,the quantum version of bicubic interpolation has not yet been studied.In this work,we present the first quantum image scaling scheme for bicubic interpolation based on the novel enhanced quantum representation(NEQR).Our scheme can realize synchronous enlargement and reduction of the image with the size of 2^(n)×2^(n) by integral multiple.Firstly,the image is represented by NEQR and the original image coordinates are obtained through multiple CNOT modules.Then,16 neighborhood pixels are obtained by quantum operation circuits,and the corresponding weights of these pixels are calculated by quantum arithmetic modules.Finally,a quantum matrix operation,instead of a classical convolution operation,is used to realize the sum of convolution of these pixels.Through simulation experiments and complexity analysis,we demonstrate that our scheme achieves exponential speedup over the classical bicubic interpolation algorithm,and has better effect than the quantum version of bilinear interpolation.

Quantum control based on three forms of Lyapunov functions

This paper introduces the quantum control of Lyapunov functions based on the state distance, the mean of imaginary quantities and state errors.In this paper, the specific control laws under the three forms are given.Stability is analyzed by the La Salle invariance principle and the numerical simulation is carried out in a 2D test system.The calculation process for the Lyapunov function is based on a combination of the average of virtual mechanical quantities, the particle swarm algorithm and a simulated annealing algorithm.Finally, a unified form of the control laws under the three forms is given.

A Theory of Bio-Quantum Genetics

The physical mechanism of heredity or inheritance of genes is a quantum mechanical and/or quantum computational process. A theory of bio-quantum genetics is established in this paper. Principle of Bio-quantum Genetics is suggested. I propose and define the soft-genes of genetics controlling the processes of heredity or inheritance of genes. This research deals with the quantum mechanisms of Mendel plant heredity and family inheritance as examples of bio-quantum genetics, deepening our understanding of heredity or inheritance. I believe that more contributions will be made to promote researches of bio-quantum genetics or quantum biology at large.

Thermodynamics in a quantum corrected Reissner-Nordstr?m-AdS black hole and its GUP-corrections

We calculate the thermodynamic quantities in the quantum corrected Reissner-Nordstr?m-AdS(RN-AdS)black hole,and examine their quantum corrections.By analyzing the mass and heat capacity,we give the critical state and the remnant state,respectively,and discuss their consistency.Then,we investigate the quantum tunneling from the event horizon of massless scalar particle by using the null geodesic method,and charged massive boson W^(±)and fermions by using the Hamilton-Jacob method.It is shown that the same Hawking temperature can be obtained from these tunneling processes of different particles and methods.Next,by using the generalized uncertainty principle(GUP),we study the quantum corrections to the tunneling and the temperature.Then the logarithmic correction to the black hole entropy is obtained.

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.

Remote entangling gate between a quantum dot spin and a transmon qubit mediated by microwave photons

Spin qubits and superconducting qubits are promising candidates for realizing solid-state quantum information processors.Designing a hybrid architecture that combines the advantages of different qubits on the same chip is a highly desirable but challenging goal.Here we propose a hybrid architecture that utilizes a high-impedance SQUID array resonator as a quantum bus,thereby coherently coupling different solid-state qubits.We employ a resonant exchange spin qubit hosted in a triple quantum dot and a superconducting transmon qubit.Since this hybrid system is highly tunable,it can operate in a dispersive regime,where the interaction between the different qubits is mediated by virtual photons.By utilizing such interactions,entangling gate operations between different qubits can be realized in a short time of 30 ns with a fidelity of up to 96.5%under realistic parameter conditions.Further utilizing this interaction,remote entangled state between different qubits can be prepared and is robust to perturbations of various parameters.These results pave the way for exploring efficient fault-tolerant quantum computation on hybrid quantum architecture platforms.

Enhancing IoT Security:Quantum-Level Resilience against Threats

The rapid growth of the Internet of Things(IoT)operations has necessitated the incorporation of quantum computing technologies tomeet its expanding needs.This integration ismotivated by the need to solve the specific issues provided by the expansion of IoT and the potential benefits that quantum computing can offer in this scenario.The combination of IoT and quantum computing creates new privacy and security problems.This study examines the critical need to prevent potential security concerns from quantum computing in IoT applications.We investigate the incorporation of quantum computing approaches within IoT security frameworks,with a focus on developing effective security mechanisms.Our research,which uses quantum algorithms and cryptographic protocols,provides a unique solution to protecting sensitive information and assuring the integrity of IoT systems.We rigorously analyze critical quantum computing security properties,building a hierarchical framework for systematic examination.We offer concrete solutions flexible to diverse aswell as ambiguous opinions through using a unified computational model with analytical hierarchy process(AHP)multi-criteria decision-making(MCDM)as the technique for ordering preferences by similarity to ideal solutions(TOPSIS)in a fuzzy environment.This study adds practical benefit by supporting practitioners in recognizing,choosing,and prioritizing essential security factors from the standpoint of quantum computing.Our approach is a critical step towards improving quantum-level security in IoT systems,strengthening their resilience against future threats,and preserving the IoT ecosystem’s long-term prosperity.

Simulation of optimal work extraction for quantum systems with work storage

The capacity to extract work from a quantum heat machine is not only of practical value but also lies at the heart of understanding quantum thermodynamics.In this paper,we investigate optimal work extraction for quantum systems with work storage,where extracting work is completed by a unitary evolution on the composite system.We consider the physical requirement of energy conservation both strictly and on average.For both,we construct their corresponding unitaries and propose variational quantum algorithms for optimal work extraction.We show that maximal work extraction in general can be feasible when energy conservation is satisfied on average.We demonstrate with numeral simulations using a continuous-variable work storage.Our work show an implementation of a variational quantum computing approach for simulating work extraction in quantum systems.

An anti-aliasing filtering of quantum images in spatial domain using a pyramid structure

As a part of quantum image processing,quantum image filtering is a crucial technology in the development of quantum computing.Low-pass filtering can effectively achieve anti-aliasing effects on images.Currently,most quantum image filterings are based on classical domains and grayscale images,and there are relatively fewer studies on anti-aliasing in the quantum domain.This paper proposes a scheme for anti-aliasing filtering based on quantum grayscale and color image scaling in the spatial domain.It achieves the effect of anti-aliasing filtering on quantum images during the scaling process.First,we use the novel enhanced quantum representation(NEQR)and the improved quantum representation of color images(INCQI)to represent classical images.Since aliasing phenomena are more pronounced when images are scaled down,this paper focuses only on the anti-aliasing effects in the case of reduction.Subsequently,we perform anti-aliasing filtering on the quantum representation of the original image and then use bilinear interpolation to scale down the image,achieving the anti-aliasing effect.The constructed pyramid model is then used to select an appropriate image for upscaling to the original image size.Finally,the complexity of the circuit is analyzed.Compared to the images experiencing aliasing effects solely due to scaling,applying anti-aliasing filtering to the images results in smoother and clearer outputs.Additionally,the anti-aliasing filtering allows for manual intervention to select the desired level of image smoothness.