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| 1 | Quantum-Inspired Evolutionary Algorithm for Continuous Space Optimization显示文摘 | LI Panchi LI Shiyong | 2008 | Chinese Journal of Electronics2008，17，1： | 29 |
| 2 | Learning algorithm and application of quantum BP neural networks based on universal quantum gates显示文摘A quantum BP neural networks model with learning algorithm is proposed. First, based on the universality of single qubit rotation gate and two-qubit controlled-NOT gate, a quantum neuron model is constructed, which is composed of input, phase rotation, aggregation, reversal rotation and output. In this model, the input is described by qubits, and the output is given by the probability of the state in which |1 is observed. The phase rotation and the reversal rotation are performed by the universal quantum gates. Secondly, the quantum BP neural networks model is constructed, in which the output layer and the hide layer are quantum neurons. With the application of the gradient descent algorithm, a learning algorithm of the model is proposed, and the continuity of the model is proved. It is shown that this model and algorithm are superior to the conventional BP networks in three aspects: convergence speed, convergence rate and robustness, by two application examples of pattern recognition and function approximation. | Li Panchi Li Shiyong | 2008 | Journal of Systems Engineering and Electronics2008，19，1： | 26 |
| 3 | Model and Algorithm of Sequence-Based Quantum-Inspired Neural Networks显示文摘To enhance the approximation ability of traditional Artificial neural network(ANN), by introducing the quantum rotation gates and the multi-qubits controlled-NOT gates to ANN, we proposed a Sequence input-based quantum-inspired neural network(SIQNN).In our model, the hidden nodes are composed of some multi-qubits controlled-NOT gates, the inputs are described by the multi-dimensional discrete qubits sequences,the output nodes are the traditional neurons. The model parameters include the rotation angles of quantum rotation gates in hide layer and the weights in output layer.The learning algorithms were derived by employing the Levenberg-Marquardt algorithm. Simulation results of predicting the runoff of the Hongjiadu Reservoir show that,the SIQNN is obviously superior to the ANN. | LI Panchi ZHAO Ya | 2018 | Chinese Journal of Electronics2018，27，1： | 3 |
| 4 | Design and Implementation of Color Image Encryption Based on Qubit Rotation About Axis显示文摘Currently, almost all color image encryption/decryption algorithms are designed based on a classical computer, in which the key space is relatively small,and the huge gains from quantum parallelism are not obtained. To address this problem, we propose a novel color image encryption/decryption method based on random rotation of qubit and Quantum Fourier transform(QFT).First, the color image is represented in a quantum superposition state |Image>, in which the color information of each pixel is described by only one qubit |c>. Then, the |c> are randomly rotated on the Bloch sphere about three coordinate axis, and the QFT is performed on the |Image>.Once again, the |c> is randomly rotated on the Bloch sphere and then the inverse QFT is performed on the |Image>,which the encryption process is implemented. The keys are the rotation angles of two above-mentioned rotations.The decryption is the inverse process of the encryption.Our method may run on a quantum computer in the future. The simulation results on the classic computer show that our approaches have better security. | LIU Xiande XIAO Hong LI Panchi ZHAO Ya | 2018 | Chinese Journal of Electronics2018，27，4： | 2 |
| 5 | Double chains quantum genetic algorithm withapplication to neuro-fuzzy controller design显示文摘 | LI Panchi | 2011 | Advancesin Engineering Software2011，42，： | 1 |
| 6 | An improved quantum-behaved particle swarm optimization algorithm显示文摘 | Panchi Li Hong Xiao | 2014 | Applied Intelligence2014，，3： | 1 |
| 7 | MODEL AND ALGORITHM OF NEURAL NETWORKS WITH QUANTUM GATED NODES显示文摘 | Li Panchi Song Kaoping Yang Erlong | 2010 | Neural Network World2010，，2： | 1 |
| 8 | Quantum ant colony optimization algorithm based on bloch spherical scarch显示文摘 | Li Panchi Wang Haiying | 2012 | Neural Network World2012，22，4： | 1 |
| 9 | Double chains quantum genetic algorithm with application to neuro?fuzzy controller design显示文摘 | Li Panchi Song K P Shang F H | 2011 | Advances in Engineering Software2011，42，： | 1 |
| 10 | Quantum-inspired evolutionary algorithm for continuous space optimization based on Bloch coordinates of qubits显示文摘 | Li Panchi Li Shiyong | | 0，，1： | 1 |
| 11 | Plasma C-reactive protein in hemodialysis:A cross-sectional,longitudinal clinical survey显示文摘 | Panchi V Migliori M Depietro S | 2000 | Blood Purif2000，18，1： | 1 |
| 12 | Grover quantum searching algorithm based on weighted targets显示文摘The current Grover quantum searching algorithm cannot identify the difference in importance of the search targets when it is applied to an unsorted quantum database, and the probability for each search target is equal. To solve this problem, a Grover searching algorithm based on weighted targets is proposed. First, each target is endowed a weight coefficient according to its importance. Applying these different weight coefficients, the targets are represented as quantum superposition states. Second, the novel Grover searching algorithm based on the quantum superposition of the weighted targets is constructed. Using this algorithm, the probability of getting each target can be approximated to the corresponding weight coefficient, which shows the flexibility of this algorithm. Finally, the validity of the algorithm is proved by a simple searching example. | Li Panchi Li Shiyong | 2008 | Journal of Systems Engineering and Electronics2008，19，2： | 1 |
| 13 | Two Improvements in Grover's Algorithm显示文摘 | LI Panchi LI Shiyong | 2008 | Chinese Journal of Electronics2008，17，1： | 0 |
| 14 | A Second-Order Semi-Implicit Method for the Inertial Landau-Lifshitz-Gilbert Equation显示文摘Electron spins in magnetic materials have preferred orientations collectively and generate the macroscopic magnetization.Its dynamics spans over a wide range of timescales from femtosecond to picosecond,and then to nanosecond.The Landau-Lifshitz-Gilbert(LLG)equation has been widely used in micromagnetics simulations over decades.Recent theoretical and experimental advances have shown that the inertia of magnetization emerges at sub-picosecond timescales and contributes significantly to the ultrafast magnetization dynamics,which cannot be captured intrinsically by the LLG equation.Therefore,as a generalization,the inertial LLG(iLLG)equation is proposed to model the ultrafast magnetization dynamics.Mathematically,the LLG equation is a nonlinear system of parabolic type with(possible)degeneracy.However,the iLLG equation is a nonlinear system of mixed hyperbolic-parabolic type with degeneracy,and exhibits more complicated structures.It behaves as a hyperbolic system at sub-picosecond timescales,while behaves as a parabolic system at larger timescales spanning from picosecond to nanosecond.Such hybrid behaviors impose additional difficulties on designing efficient numerical methods for the iLLG equation.In this work,we propose a second-order semiimplicit scheme to solve the iLLG equation.The second-order temporal derivative of magnetization is approximated by the standard centered difference scheme,and the first-order temporal derivative is approximated by the midpoint scheme involving three time steps.The nonlinear terms are treated semi-implicitly using one-sided interpolation with second-order accuracy.At each time step,the unconditionally unique solvability of the unsymmetric linear system is proved with detailed discussions on the condition number.Numerically,the second-order accuracy of the proposed method in both time and space is verified.At sub-picosecond timescales,the inertial effect of ferromagnetics is observed in micromagnetics simulations,in consistency with the hyperbolic property of the iLLG model;at nanosecond timescales,the results of the iLLG model are in nice agreements with those of the LLG model,in consistency with the parabolic feature of the iLLG model. | Panchi Li Lei Yang Jin Lan Rui Du Jingrun Chen | 2023 | Numerical Mathematics(Theory,Methods and Applications)2023，16，1： | 0 |
| 15 | Behavior of quantum search algorithm with small phase rotations显示文摘 | 李欣 Cheng Chuntian Li Panchi Xu Shaohua | 2011 | High Technology Letters2011，17，1： | 0 |
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