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• W3023877516 abstract "Variational quantum algorithms are promising tools for near-term quantum computers as their shallow circuits are robust to experimental imperfections. Their practical applicability, however, strongly depends on how many times their circuits need to be executed for sufficiently reducing shot-noise. We consider metric-aware quantum algorithms: variational algorithms that use a quantum computer to efficiently estimate both a matrix and a vector object. For example, the recently introduced quantum natural gradient approach uses the quantum Fisher information matrix as a metric tensor to correct the gradient vector for the co-dependence of the circuit parameters. We rigorously characterise and upper bound the number of measurements required to determine an iteration step to a fixed precision, and propose a general approach for optimally distributing samples between matrix and vector entries. Finally, we establish that the number of circuit repetitions needed for estimating the quantum Fisher information matrix is asymptotically negligible for an increasing number of iterations and qubits." @default.
• W3023877516 created "2020-05-13" @default.
• W3023877516 creator A5075181439 @default.
• W3023877516 creator A5081798961 @default.
• W3023877516 date "2020-05-11" @default.
• W3023877516 modified "2023-09-27" @default.
• W3023877516 title "Measurement cost of metric-aware variational quantum algorithms" @default.
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• W3023877516 hasPublicationYear "2020" @default.
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