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Abstract

Summary

Quantum computing is a rapidly growing technology field with increasingly useful applications across industry and research. This new paradigm of computing has the potential to solve classically intractable problems by exploiting an exponentially increasing computational space. This allows quantum algorithms to dramatically reduce the runtime for solving computationally resource-intensive problems. Quantum Neural Networks (QNNs) [ ] present a promising opportunity for overcoming the scaling problem in classical machine learning. However, training QNNs requires significant circuit evaluations, making them a resource-intensive use case for quantum processing units (QPUs). This study compares software development kits (SDKs) and hardware platforms to determine the fastest and most cost-efficient combination for developing novel quantum algorithms and uses QNNs as a general benchmark. The aim is to find the optimal balance of runtime, cost, and accuracy for executing quantum circuits.

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/content/papers/10.3997/2214-4609.2023630022
2023-09-25
2026-01-25

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/content/papers/10.3997/2214-4609.2023630022
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Benchmarking Simulated and Physical Quantum Processing Units Using Quantum and Hybrid Algorithms
Conference Proceedings 2023, 1 (2023); https://doi.org/10.3997/2214-4609.2023630022
/content/papers/10.3997/2214-4609.2023630022
/content/papers/10.3997/2214-4609.2023630022
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