(11) for Variational Quantum Simulation 27ī.
#Google quantum error correction verification#
Basic Variational Quantum Algorithms 2Ģ.2 Real and imaginary time evolution quantum simulator 4ģ.1 Quantum approximate optimisation algorithms 6ģ.2 Variational algorithms for machine learning 6ģ.2.2 Data-driven quantum circuit learning 7ģ.2.3 Quantum generative adversarial networks 8ģ.2.4 Quantum autoencoder for quantum data compression 8ģ.2.5 Variational quantum state eigensolver 9ģ.3 Variational algorithms for linear algebra 9ģ.4 Excited state-search variational algorithms 10ģ.4.4 Calculation of the Green's function 11ģ.6 Variational-state quantum metrology 13ģ.7 Variational quantum algorithms for quantum error correction 13ģ.7.1 Variational circuit compiler for quantum error correction 14ģ.7.2 Variational quantum error corrector (QVECTOR) 14ģ.8 Dissipative-system variational quantum eigensolver 14Ĥ.1 Variational quantum simulation algorithms for density matrix 15Ĥ.1.1 Variational real time simulation for open quantum system dynamics 15Ĥ.1.2 Variational imaginary time simulation for a density matrix 15Ĥ.2 Variational quantum simulation algorithms for general processes 16Ĥ.2.2 Matrix multiplication and linear equations 16Ĥ.4 Variational quantum simulation algorithms for estimating the Green's function 17ĥ.1.3 Methods to boost physical errors 19ĥ.1.4 Mitigation of algorithmic errors 20ĥ.2 Least square fitting for several noise parameters 20ĥ.8 Learning-based quantum error mitigation 24ĥ.8.1 Quantum error mitigation via Clifford data regression 24ĥ.8.2 Learning-based quasi-probability method 24ĥ.10 Combination of error mitigation techniques 25ĥ.10.1 Symmetry verification with error extrapolation 25ĥ.10.2 Quasi-probability method with error extrapolation 26ĥ.10.3 Symmetry verification with quasi-probability method 26ĥ.10.4 Combining quasi-probability, symmetry verification and error extrapolation 27Ī. Since quantum computing with NISQ devices is an actively developing field, we expect this review to be a useful basis for future studies.Ģ. In this article, we review the basic results for hybrid quantum-classical algorithms and quantum error mitigation techniques. Meanwhile, mitigation of errors on quantum processors is also crucial to obtain reliable results. Hybrid quantum-classical algorithms are regarded as well-suited for execution on NISQ devices by combining quantum computers with classical computers, and are expected to be the first useful applications for quantum computing. However, the question of what can be implemented on NISQ devices is still not fully explored, and discovering useful tasks for such devices is a topic of considerable interest. In experiment, quantum supremacy has recently been achieved by the Google team by using a noisy intermediate-scale quantum (NISQ) device with over 50 qubits.
Quantum computers can exploit a Hilbert space whose dimension increases exponentially with the number of qubits.
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