Influence of HW-SW-Co-Design on Quantum Computing Scalability

The use of quantum processing units (QPUs) promises speed-ups for solving computational problems. Yet, current devices are limited by the number of qubits and suffer from significant imperfections, which prevents achieving quantum advantage. To step towards practical utility, one approach is to appl...

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Published in2023 IEEE International Conference on Quantum Software (QSW) pp. 104 - 115
Main Authors Safi, Hila, Wintersperger, Karen, Mauerer, Wolfgang
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.07.2023
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Abstract The use of quantum processing units (QPUs) promises speed-ups for solving computational problems. Yet, current devices are limited by the number of qubits and suffer from significant imperfections, which prevents achieving quantum advantage. To step towards practical utility, one approach is to apply hardware-software co-design methods. This can involve tailoring problem formulations and algorithms to the quantum execution environment, but also entails the possibility of adapting physical properties of the QPU to specific applications. In this work, we follow the latter path, and investigate how key figures-circuit depth and gate count-required to solve four cornerstone NP-complete problems vary with tailored hardware properties.Our results reveal that achieving near-optimal performance and properties does not necessarily require optimal quantum hardware, but can be satisfied with much simpler structures that can potentially be realised for many hardware approaches. Using statistical analysis techniques, we additionally identify an underlying general model that applies to all subject problems. This suggests that our results may be universally applicable to other algorithms and problem domains, and tailored QPUs can find utility outside their initially envisaged problem domains. The substantial possible improvements nonetheless highlight the importance of QPU tailoring to progress towards practical deployment and scalability of quantum software.
AbstractList The use of quantum processing units (QPUs) promises speed-ups for solving computational problems. Yet, current devices are limited by the number of qubits and suffer from significant imperfections, which prevents achieving quantum advantage. To step towards practical utility, one approach is to apply hardware-software co-design methods. This can involve tailoring problem formulations and algorithms to the quantum execution environment, but also entails the possibility of adapting physical properties of the QPU to specific applications. In this work, we follow the latter path, and investigate how key figures-circuit depth and gate count-required to solve four cornerstone NP-complete problems vary with tailored hardware properties.Our results reveal that achieving near-optimal performance and properties does not necessarily require optimal quantum hardware, but can be satisfied with much simpler structures that can potentially be realised for many hardware approaches. Using statistical analysis techniques, we additionally identify an underlying general model that applies to all subject problems. This suggests that our results may be universally applicable to other algorithms and problem domains, and tailored QPUs can find utility outside their initially envisaged problem domains. The substantial possible improvements nonetheless highlight the importance of QPU tailoring to progress towards practical deployment and scalability of quantum software.
Author Wintersperger, Karen
Mauerer, Wolfgang
Safi, Hila
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  givenname: Wolfgang
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  fullname: Mauerer, Wolfgang
  email: wolfgang.mauerer@othr.de
  organization: Technical University of Applied Sciences Regensburg,Siemens AG, Technology,Regensburg/Munich,Germany
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Snippet The use of quantum processing units (QPUs) promises speed-ups for solving computational problems. Yet, current devices are limited by the number of qubits and...
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StartPage 104
SubjectTerms Analytical models
Hardware
hardware-software co-design
Logic gates
quantum algorithm performance analysis
quantum computing
Qubit
Scalability
scalability of quantum applications
Software algorithms
software engineering
Statistical analysis
Title Influence of HW-SW-Co-Design on Quantum Computing Scalability
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