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Foundations in signal processing, communications and networking

Quantum Computing and Programming

Riccardo Bassoli, Holger Boche, Christian Deppe, Roberto Ferrara, Frank H. P. Fitzek, Gisbert Janßen, Sajad Saeedinaeeni

Published Oct 30, 2020• 0 citations
Publisher
DOI
10.1007/978-3-030-62938-0_3

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Quantum computing (QC) is emerging as a new computing resource that could be superior to conventional computing (CC) for certain classes of optimization problems. However, in principle QC can only solve unconstrained binary programming problems, while mixed-integer linear programming (MIP) is of most interest in practice. We attempt to bridge the gap between the capability of QC and real-world applications by developing a new approach for MIP. The idea is decomposing the MIP into binary programming and linear programming (LP) problems, which are respectively solved by QC and conventional computing. We formalize a decomposition approach that ensures that with a sufficient number of back and forth iterations, the algorithm can reach the optimal solution of the original MIP problem. The algorithm is tested on a 2000Q D-Wave quantum processing units (QPU) and is shown to be effective for small-scaled test cases.
By leveraging quantum-mechanical properties like superposition, entanglement, and interference, quantum computing (QC) offers promising solutions for problems that classical computing has not been able to solve efficiently, such as drug discovery, cryptography, and physical simulation. Unfortunately, adopting QC remains difficult for potential users like QC beginners and application-specific domain experts, due to limited theoretical and practical knowledge, the lack of integrated interface-wise support, and poor documentation. For example, to use quantum computers, one has to convert conceptual logic into low-level codes, analyze quantum program results, and share programs and results. To support the wider adoption of QC, we, as designers and QC experts, propose interaction techniques for QC through design iterations. These techniques include writing quantum codes conceptually, comparing initial quantum programs with optimized programs, sharing quantum program results, and exploring quantum machines. We demonstrate the feasibility and utility of these techniques via use cases with high-fidelity prototypes.
Quantum Computing (QC) is a fast-growing field that has enhanced the emergence of new programming languages and frameworks. Furthermore, the increased availability of computational resources has also contributed to an influx in the development of quantum programs. Given that classical and QC are significantly different due to the intrinsic nature of quantum programs, several aspects of QC (e.g., performance, bugs) have been investigated, and novel approaches have been proposed. However, from a purely quantum perspective, maintenance, one of the major steps in a software development life-cycle, has not been considered by researchers yet. In this paper, we fill this gap and investigate the prevalence of code smells in quantum programs as an indicator of maintenance issues. We defined eight quantum-specific smells and validated them through a survey with 35 quantum developers. Since no tool specifically aims to detect quantum smells, we developed a tool called QSmell that supports the proposed quantum-specific smells. Finally, we conducted an empirical investigation to analyze the prevalence of quantum-specific smells in 15 open-source quantum programs. Our results showed that 11 programs (73.33%) contain at least one smell and, on average, a program has three smells. Furthermore, the long circuit is the most prevalent smell present in 53.33% of the programs.