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Found 8 papers, 2 papers with code
Near-Term Quantum Computing Techniques: Variational Quantum Algorithms, Error Mitigation, Circuit Compilation, Benchmarking and Classical Simulation
To address this challenge, several near-term quantum computing techniques, including variational quantum algorithms, error mitigation, quantum circuit compilation and benchmarking protocols, have been proposed to characterize and mitigate errors, and to implement algorithms with a certain resistance to noise, so as to enhance the capabilities of near-term quantum devices and explore the boundaries of their ability to realize useful applications.
Active Learning on a Programmable Photonic Quantum Processor
Here, we design and implement two AL-enpowered variational quantum classifiers, to investigate the potential applications and effectiveness of AL in quantum machine learning.
Parameter-Parallel Distributed Variational Quantum Algorithm
Variational quantum algorithms (VQAs) have emerged as a promising near-term technique to explore practical quantum advantage on noisy intermediate-scale quantum (NISQ) devices.
Quantum walks on a programmable two-dimensional 62-qubit superconducting processor
Quantum walks are the quantum mechanical analogue of classical random walks and an extremely powerful tool in quantum simulations, quantum search algorithms, and even for universal quantum computing.
Quantum Physics
Experimental Quantum Generative Adversarial Networks for Image Generation
For the first time, we experimentally achieve the learning and generation of real-world hand-written digit images on a superconducting quantum processor.
Variational Quantum Circuits for Quantum State Tomography
We demonstrate our method by performing numerical simulations for the tomography of the ground state of a one-dimensional quantum spin chain, using a variational quantum circuit simulator.
Quantum-Inspired Support Vector Machine
Support vector machine (SVM) is a particularly powerful and flexible supervised learning model that analyzes data for both classification and regression, whose usual algorithm complexity scales polynomially with the dimension of data space and the number of data points.
Demonstration of Topological Data Analysis on a Quantum Processor
Topological data analysis offers a robust way to extract useful information from noisy, unstructured data by identifying its underlying structure.
