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Variational Quantum Algorithms
M. Cerezo, Andrew Arrasmith, Ryan Babbush +8
Applications such as simulating complicated quantum systems or solving large-scale linear algebra problems are very challenging for classical computers due to the extremely high co…
Variational Quantum Algorithm for Estimating the Quantum Fisher Information
Jacob L. Beckey, M. Cerezo, Akira Sone +1
The Quantum Fisher information (QFI) quantifies the ultimate precision of estimating a parameter from a quantum state, and can be regarded as a reliability measure of a quantum sys…
Generalized Measure of Quantum Fisher Information
Akira Sone, M. Cerezo, Jacob L. Beckey +1
In this work, we present a lower bound on the quantum Fisher information (QFI) which is efficiently computable on near-term quantum devices. This bound itself is of interest, as we…
Variational Hamiltonian Diagonalization for Dynamical Quantum Simulation
Benjamin Commeau, M. Cerezo, Zoë Holmes +3
Dynamical quantum simulation may be one of the first applications to see quantum advantage. However, the circuit depth of standard Trotterization methods can rapidly exceed the coh…
Higher Order Derivatives of Quantum Neural Networks with Barren Plateaus
M. Cerezo, Patrick J. Coles
Quantum neural networks (QNNs) offer a powerful paradigm for programming near-term quantum computers and have the potential to speedup applications ranging from data science to che…
Variational Quantum State Eigensolver
M. Cerezo, Kunal Sharma, Andrew Arrasmith +1
Extracting eigenvalues and eigenvectors of exponentially large matrices will be an important application of near-term quantum computers. The Variational Quantum Eigensolver (VQE) t…
Cost Function Dependent Barren Plateaus in Shallow Parametrized Quantum Circuits
M. Cerezo, Akira Sone, Tyler Volkoff +2
Variational quantum algorithms (VQAs) optimize the parameters of a parametrized quantum circuit to minimize a cost function . While VQAs may enable practical…