Information flow in parameterized quantum circuits

Abstract

In this work, we introduce a new way to quantify information flow in quantum systems, especially for parameterized quantum circuits. We use a graph representation of the circuits and propose a new distance metric using the mutual information between gate nodes. We then present an optimization procedure for variational algorithms using paths based on the distance measure. We explore the features of the algorithm by means of the variational quantum eigensolver, in which we compute the ground state energies of the Heisenberg model. In addition, we employ the method to solve a binary classification problem using variational quantum classification. From numerical simulations, we show that our method can be successfully used for optimizing the parameterized quantum circuits primarily used in near-term algorithms. We further note that information-flow based paths can be used to improve convergence of existing stochastic gradient based methods.

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Cited by in corpus (5)

• Hamiltonian-based graph-state ansatz for variational quantum algorithms
• Leveraging commuting groups for an efficient variational Hamiltonian ansatz
• Equivalence checking of quantum circuits via intermediary matrix product operator
• Graph-theoretic insights on the constructability of complex entangled states
• Unbiased observable estimation with approximate channels in fault-tolerant quantum computation