Community Detection Across Emerging Quantum Architectures

Abstract

One of the roadmap plans for quantum computers is an integration within HPC ecosystems assigning them a role of accelerators for a variety of computationally hard tasks. However, in the near term, quantum hardware will be in a constant state of change. Heading towards solving real-world problems, we advocate development of portable, architecture-agnostic hybrid quantum-classical frameworks and demonstrate one for the community detection problem evaluated using quantum annealing and gate-based universal quantum computation paradigms.

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Cited by in corpus (12)

• A Tutorial on Formulating and Using QUBO Models
• Learning to Optimize Variational Quantum Circuits to Solve Combinatorial Problems
• Layer VQE: A Variational Approach for Combinatorial Optimization on Noisy Quantum Computers
• Detecting Multiple Communities Using Quantum Annealing on the D-Wave System
• Exploiting Symmetry Reduces the Cost of Training QAOA
• Evaluation of QAOA based on the approximation ratio of individual samples
• Partitioning Dense Graphs with Hardware Accelerators
• Effective electromagnetic actions for Lorentz violating theories exhibiting the axial anomaly
• Reinforcement-Learning-Based Variational Quantum Circuits Optimization for Combinatorial Problems
• Binary matrix factorization on special purpose hardware
• ELRUNA: Elimination Rule-based Network Alignment
• Quantum-Assisted Graph Clustering and Quadratic Unconstrained D-ary Optimisation