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Quantum Computing is an emerging technology that combines the principles of computer science and quantum mechanics to solve computationally challenging problems significantly faster than classical computers. In this paper, we present a proof-of-principle procedure for generating hardware-executable quantum circuits for Noisy Intermediate-Scale Quantum (NISQ) devices that follows the paradigm of approximate computing.Our approach starts from the reference circuit and trans-forms it into an executable circuit with tuneable parameters by replacing the high-level quantum operations by approximate decompositions into hardware-native gates. An inner optimization loop over the rotation gates’ angles ensures that the so-created circuit behaves in the same way as the reference one in terms of its expectation-value landscape. This technique is complemented by compiler-based optimizations to further reduce or aggregate gate groups of the optimized circuit. This three-step procedure is embedded into an outer genetic algorithm framework that inspects many different circuit designs with placements of single- and multi-qubit gates according to the hardware’s lattice structure, and returns a set of approximate quantum circuits that can be executed on NISQ devices directly.We have validated our approach for superconducting quantum systems from IBM and Rigetti for various benchmark algorithms. In nearly all cases, our approach outperforms the vendors’ quantum-compiler frameworks and produces significantly smaller circuits with up to 50% reduction in the number of gates.

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Variational quantum algorithms are suitable for use on noisy quantum systems. One of the most important use-cases is the quantum simulation of materials, using the variational quantum eigensolver (VQE). To optimize VQE performance, a suitable parameterized quantum circuit (ansatz) must be selected. We investigate a class of ansatze that incorporates knowledge of the quantum hardware, namely the hardware efficient ansatze. The performance of hardware efficient ansatze is affected differently by noise, and our goal is to study the effect of noise on evaluating which ansatz gives more accurate results in practice. First, we study the effect of noise on the different hardware efficient ansatze by benchmarking and ranking the performance of each ansatz family (i) on a chemistry application using VQE and (ii) by the recently established metric of "expressibility". The results demonstrate the ranking of optimal circuits does not remain constant in the presence of noise. Second, we evaluate the suitability of the expressibility measure in this context by performing a correlation study between expressibility and the performance of the same circuits on a chemistry application using VQE. Our simulations reveal a weak correlation and therefore demonstrate that expressibility is not an adequate measure to quantify the effectiveness of parameterized quantum circuits for quantum chemistry. Third, we evaluate the effect of different quantum device noise models on the ordering of which ansatz family is best. Interestingly, we see that to decide which ansatz is optimal for use, one needs to consider the specific hardware used even within the same family of quantum hardware., 13 pages, 17 figures, 4 tables, conference paper, IEEE International Conference on Quantum Computing and Engineering (QCE21)