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Variational quantum multidimensional scaling algorithm
Quantum multidimensional scaling is a quantum dimensionality reduction algorithm. Its complex quantum circuit design structure and excessive qubits...
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Quantum state clustering algorithm based on variational quantum circuit
Clustering, a well-studied problem in the machine learning community, becomes even more intriguing with the emergence of quantum machine learning....
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Pure quantum gradient descent algorithm and full quantum variational eigensolver
Optimization problems are prevalent in various fields, and the gradient-based gradient descent algorithm is a widely adopted optimization method....
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Variational quantum algorithm for experimental photonic multiparameter estimation
Variational quantum metrology represents a powerful tool to optimize estimation strategies, resulting particularly beneficial for multiparameter...
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Finding eigenvectors with a quantum variational algorithm
This paper presents a hybrid variational quantum algorithm that finds a random eigenvector of a unitary matrix with a known quantum circuit. The...
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Variational Quantum Computation Integer Factorization Algorithm
The integer factorization problem is a major challenge in the field of computer science, and Shor’s algorithm provides a promising solution for this...
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Variational shadow quantum neural network based on immune optimisation algorithm
Quantum neural networks are neural network models based on the principles of quantum mechanics, a combination of the advantages of classical neural...
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Variational quantum algorithms for scanning the complex spectrum of non-Hermitian systems
Solving non-Hermitian quantum many-body systems on a quantum computer by minimizing the variational energy is challenging as the energy can be...
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Analyzing variational quantum landscapes with information content
The parameters of the quantum circuit in a variational quantum algorithm induce a landscape that contains the relevant information regarding its...
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Enhancing quantum support vector machines through variational kernel training
We introduce a new model in quantum machine learning (QML) that combines the strengths of existing quantum kernel SVM (QK-SVM) and quantum...
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Variational Algorithms, Quantum Approximate Optimization Algorithm, and Neural Network Quantum States with Two Qubits
We describe different variational ansatzes and study entanglement in the ground state of the two-qubit transverse-field Ising model.
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Variational quantum algorithms: fundamental concepts, applications and challenges
Quantum computing is a new discipline combining quantum mechanics and computer science, which is expected to solve technical problems that are...
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A design method for efficient variational quantum models based on specific Pauli axis
The combination of quantum computing and machine learning is expected to solve problems that cannot be realized by classical computers in machine...
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Muon/pion identification at BESIII based on variational quantum classifier
In collider physics experiments, particle identification (PID), i.e., the identification of the charged particle species in the detector is usually...
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Parallelized variational quantum classifier with shallow QRAM circuit
In this paper, we present a novel quantum variational circuit, harnessing the capabilities of a pre-determined quantum random access memory (QRAM)...
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Near-term quantum computing techniques: Variational quantum algorithms, error mitigation, circuit compilation, benchmarking and classical simulation
Quantum computing is a game-changing technology for global academia, research centers and industries including computational science, mathematics,...
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Determination of molecular energies via variational-based quantum imaginary time evolution in a superconducting qubit system
As a valid tool for solving ground state problems, imaginary time evolution (ITE) is widely used in physical and chemical simulations. Different...
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A universal quantum algorithm for weighted maximum cut and Ising problems
We propose a hybrid quantum-classical algorithm to compute approximate solutions of binary combinatorial problems. We employ a shallow-depth quantum...
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Variational quantum state eigensolver
Extracting eigenvalues and eigenvectors of exponentially large matrices will be an important application of near-term quantum computers. The...
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Quantifying the effect of gate errors on variational quantum eigensolvers for quantum chemistry
Variational quantum eigensolvers (VQEs) are leading candidates to demonstrate near-term quantum advantage. Here, we conduct density-matrix...
