Chao‐Hua Yu

Beijing University of Posts and Telecommunications

Author Statistics

Papers

Citation

H-Index

i-10 index

Research Trends

Author Order

Document Type

Co-Authors

Qiaoyan Wen

Beijing University of Posts and Telecommunications

Fei Gao

Beijing University of Posts and Telecommunications

Su‐Juan Qin

Beijing University of Posts and Telecommunications

Song Lin

Fujian Normal University

Lin‐Chun Wan

Southwest University

Gongde Guo

Fujian Normal University

Shi‐Jie Pan

Beijing University of Posts and Telecommunications

Hailing Liu

Beijing University of Posts and Telecommunications

Qiu-Ling Yue

Hainan University

Fei Gao

University of Michigan–Ann Arbor

Cooperative Institutions

Beijing University of Posts and Telecommunications

State Key Laboratory of Cryptology

Fujian Normal University

University of Western Australia

Xidian University

Switch

Chinese Academy of Sciences

Sun Yat-sen University

Nanjing University of Aeronautics and Astronautics

University of Science and Technology of China

Author Statistics

Papers

Citation

H-Index

i-10 index

Research Field

Self-tallying quantum anonymous voting

Physical review. A/Physical review, A (2016)

Qingle Wang Chao‐Hua Yu Fei Gao Haoyu Qi Qiaoyan Wen

Anonymous voting is a voting method of hiding the link between a vote and a voter, the context of which ranges from governmental elections to decision making in small groups like councils or companies. In this paper, we propose a quantum anonymous voting protocol assisted by two kinds of entangled quantum states. Particularly, we provide a mechanism of opening and permuting the ordered votes of all the voters in an anonymous manner; any party, who is interested in the voting results, can acquire a permutation copy, and then obtains the voting result through simple calculation. Unlike all previous quantum works on anonymous voting, our quantum anonymous protocol firstly possesses the properties of privacy, self-tallying, non-reusability, verifiability and fairness at the same time. Besides, we demonstrate that the entanglement of the novel quantum states used in our protocol makes the attack from outside eavesdropper and inside dishonest voters impossible. We also generalize our protocol to execute tasks of anonymous multi-party computation, such as anonymous broadcast and anonymous ranking.

Electronic voting

10.1103/physreva.94.022333

Cite

Citations (66)

Quantum Algorithms for Prediction Based on Ridge Regression

arXiv (Cornell University) (2021)

Menghan Chen Chao‐Hua Yu Gongde Guo Song Lin

We propose a quantum algorithm based on ridge regression model, which get the optimal fitting parameters w and a regularization hyperparameter α by analysing the training dataset. The algorithm consists of two subalgorithms. One is generating predictive value for a new input, the way is to apply the phase estimation algorithm to the initial state |Xi and apply the controlled rotation to the eigenvalue register. The other is finding an optimal regularization hyperparameter α , the way is to apply the phase estimation algorithm to the initial state |yi and apply the controlled rotation to the eigenvalue register. The second subalgorithm can compute the whole training dataset in parallel that improve the efficiency. Compared with the classical ridge regression algorithm, our algorithm overcome multicollinearity and overfitting. Moreover, it have exponentially faster. What's more, our algorithm can deal with the non-sparse matrices in comparison to some existing quantum algorithms and have slightly speedup than the existing quantum counterpart. At present, the quantum algorithm has a wide range of application and the proposed algorithm can be used as a subroutine of other quantum algorithms.

10.48550/arxiv.2104.13108

Cite

Citations (0)

Experimental Implementation of Quantum Algorithm for Association Rules Mining

IEEE Journal on Emerging and Selected Topics in Circuits and Systems (2022)

Chao‐Hua Yu

Recently, a quantum algorithm for a fundamentally important task in data mining, association rules mining (ARM), called qARM for short, has been proposed. Notably, qARM achieves significant speedup over its classical counterpart for implementing the main task of ARM, i.e., finding frequent itemsets from a transaction database. In this paper, we experimentally implement qARM on both real quantum computers and a quantum computing simulator via the IBM quantum computing platform. In the first place, we design quantum circuits of qARM for a 2

$\times $

2 transaction database (i.e., a transaction database involving two transactions and two items), and run it on four real five-qubit IBM quantum computers as well as on the simulator. For a larger 4

$\times $

4 transaction database which would lead to circuits with more qubits and a higher depth than the currently accessible IBM real quantum devices can handle, we also construct the quantum circuits of qARM and execute them on "aer_simulator" alone. Both experimental results show that all the frequent itemsets from the two transaction databases are successfully derived as desired, demonstrating the correctness and feasibility of qARM. Our work may serve as a benchmarking, and provide prototypes for implementing qARM for larger transaction databases on both noisy intermediate-scale quantum devices and universal fault-tolerant quantum computers.

IBM

Rollback

Speedup

10.1109/jetcas.2022.3201097

Cite

Citations (8)

Computation Offloading and Pricing Mechanism Based on Stackelberg Game

2022 7th International Conference on Intelligent Computing and Signal Processing (ICSP) (2024)

Chao‐Hua Yu Gang Xie Yuanan Liu

Stackelberg competition

Computation offloading

10.1109/icsp62122.2024.10743557

Cite

Citations (0)

Block-encoding-based quantum algorithm for linear systems with displacement structures

Physical review. A/Physical review, A (2021)

Lin‐Chun Wan Chao‐Hua Yu Shi‐Jie Pan Su‐Juan Qin Fei Gao

Matrices with the displacement structures of circulant, Toeplitz, and Hankel types as well as matrices with structures generalizing these types are omnipresent in computations of sciences and engineering. In this paper we present efficient and memory-reduced quantum algorithms for solving linear systems with such structures by devising an approach to implement the block-encodings of these structured matrices. More specifically, by decomposing $n\ifmmode\times\else\texttimes\fi{}n$ dense matrices into linear combinations of displacement matrices, we first deduce the parametrized representations of the matrices with displacement structures so that they can be treated similarly. With such representations, we then construct $\ensuremath{\epsilon}$-approximate block-encodings of these structured matrices in two different data access models, i.e., the black-box model and the quantum random access memory (QRAM) data structure model. It is shown the quantum linear system solvers based on the proposed block-encodings provide a quadratic speedup with respect to the dimension over classical algorithms in the black-box model and an exponential speedup in the QRAM data structure model. In particular, these linear system solvers subsume known results with significant improvements and also can motivate new instances where there was no specialized quantum algorithm before. As an application, one of the quantum linear system solvers is applied to the linear prediction of time series, which justifies the claimed quantum speedup is achievable for problems of practical interest.

Speedup

10.1103/physreva.104.062414

Cite

Citations (27)

Bayesian machine learning for Boltzmann machine in quantum-enhanced feature spaces

arXiv (Cornell University) (2019)

Yusen Wu Chao‐Hua Yu Su‐Juan Qin Qiaoyan Wen Fei Gao

Bayesian learning is ubiquitous for implementing classification and regression tasks, however, it is accompanied by computationally intractable limitations when the feature spaces become extremely large. Aiming to solve this problem, we develop a quantum bayesian learning framework of the restricted Boltzmann machine in the quantum-enhanced feature spaces. Our framework provides the encoding phase to map the real data and Boltzmann weight onto the quantum feature spaces and the training phase to learn an optimal inference function. Specifically, the training phase provides a physical quantity to measure the posterior distribution in quantum feature spaces, and this measure is utilized to design the quantum maximum a posterior (QMAP) algorithm and the quantum predictive distribution estimator (QPDE). It is shown that both quantum algorithms achieve exponential speed-up over their classical counterparts. Furthermore, it is interesting to note that our framework can figure out the classical bayesian learning tasks, i.e. processing the classical data and outputting corresponding classical labels. And a simulation, which is performed on an open-source software framework for quantum computing, illustrates that our algorithms show almost the same classification performance compared to their classical counterparts. Noting that the proposed quantum algorithms utilize the shallow circuit, our work is expected to be implemented on the noisy intermediate-scale quantum (NISQ) devices, and is one of the promising candidates to achieve quantum supremacy.

Restricted Boltzmann machine

Feature (linguistics)

Boltzmann machine

Quantum Machine Learning

10.48550/arxiv.1912.10857

Cite

Citations (2)

Arbitrated quantum signature scheme based on reusable key

Science China Physics Mechanics and Astronomy (2014)

Chao‐Hua Yu Gongde Guo Song Lin

Signature (topology)

10.1007/s11433-014-5491-4

Cite

Citations (13)

Graph comparison via nonlinear quantum search

arXiv (Cornell University) (2018)

Mitchell Chiew K. de Lacy Chao‐Hua Yu S. Marsh Jingbo Wang

In this paper we present an efficiently scaling quantum algorithm which finds the size of the maximum common edge subgraph for a pair of arbitrary graphs and thus provides a meaningful measure of graph similarity. The algorithm makes use of a two-part quantum dynamic process: in the first part we obtain information crucial for the comparison of two graphs through linear quantum computation. However, this information is hidden in the quantum system with vanishingly small amplitude that even quantum algorithms such as Grover's search are not fast enough to distill the information efficiently. In order to extract the information we call upon techniques in nonlinear quantum computing to provide the speed-up necessary for an efficient algorithm. The linear quantum circuit requires $\mathcal{O}(n^3 \log^3 (n) \log \log (n))$ elementary quantum gates and the nonlinear evolution under the Gross-Pitaevskii equation has a time scaling of $\mathcal{O}(\frac{1}{g} n^2 \log^3 (n) \log \log (n))$, where $n$ is the number of vertices in each graph and $g$ is the strength of the Gross-Pitaveskii non-linearity. Through this example, we demonstrate the power of nonlinear quantum search techniques to solve a subset of NP-hard problems.

Quantum circuit

10.48550/arxiv.1810.01647

Cite

Citations (0)

Quantum mean centering for block-encoding-based quantum algorithm

Physica A Statistical Mechanics and its Applications (2022)

Hailing Liu Chao‐Hua Yu Lin‐Chun Wan Su‐Juan Qin Fei Gao

Matrix (chemical analysis)

Block matrix

Data set

10.1016/j.physa.2022.128227

Cite

Citations (12)

A quantum algorithm for solving eigenproblem of the Laplacian matrix of a fully connected weighted graph

arXiv (Cornell University) (2022)

Hailing Liu Su‐Juan Qin Lin‐Chun Wan Chao‐Hua Yu Shijie Pan

Solving eigenproblem of the Laplacian matrix of a fully connected weighted graph has wide applications in data science, machine learning, and image processing, etc. However, this is very challenging because it involves expensive matrix operations. Here, we propose an efficient quantum algorithm to solve it based on a assumption that the element of each vertex and its norms can be effectively accessed via a quantum random access memory data structure. Specifically, we adopt the optimal Hamiltonian simulation technique based on the block-encoding framework to implement the quantum simulation of the Laplacian matrix. Then, the eigenvalues and eigenvectors of the Laplacian matrix are extracted by the quantum phase estimation algorithm. The core of our entire algorithm is to construct the block-encoding of the Laplacian matrix. To achieve this, we propose in detail how to construct the block-encodings of operators containing the information of the weight matrix and the degree matrix respectively, and further obtain the block-encoding of the Laplacian matrix. Compared with its classical counterpart, our algorithm has a polynomial speedup on the number of vertices and an exponential speedup on the dimension of each vertex. We also show that our algorithm can be extended to solve the eigenproblem of symmetric (non-symmetric) normalized Laplacian matrix.

Degree matrix

Speedup

Matrix (chemical analysis)

10.48550/arxiv.2203.14451

Cite

Citations (0)