Resource Optimal Executable Quantum Circuit Generation Using Approximate Computing

Adarsh, Smaran; Möller, Matthias

doi:10.1109/qce52317.2021.00040

Cited by 4 publications

(2 citation statements)

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“…We are, to our knowledge, the first to choose such a cost function for finding quantum circuits geared at mimicking probabilistic operations and search operations for learning quantum circuits. A similar cost function was used in [7], where they use this cost function to optimize a reference circuit.…”

Section: Paper Outlinementioning

confidence: 99%

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Learning Based Hardware-Centric Quantum Circuit Generation

Möller

2022

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In this paper we present an approach to find quantum circuits suitable to mimic probabilistic and search operations on a physical NISQ device. We present both a gradient based and a non-gradient based machine learning approach to optimize the created quantum circuits. In our optimization procedure we make use of a cost function that differentiates between the vector representing the probabilities of measurement of each basis state after applying our learned circuit and the desired probability vector. As such our quantum circuit generation (QCG) approach leads to thinner quantum circuits which behave better when executed on physical quantum computers. Our approach moreover ensures that the created quantum circuit obeys the restrictions of the chosen hardware. By catering to specific quantum hardware we can avoid unforeseen and potentially unnecessary circuit depth, and we return circuits that need no further transpilation. We present the results of running the created circuits on quantum computers by IBM, Rigetti and Quantum Inspire.

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Section: Paper Outlinementioning

confidence: 99%

“…The function f expresses the desired behaviour as described in Sect. 2 and f U (Θ) is defined as in [7]:…”

Section: Cost Functionmentioning

confidence: 99%