A Quantum Parallel Framework For Distributed Quantum Algorithm Execution, Architecture, Scheduling, And Industrial Case Studies Across Simulators and QPU Hardware
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Authors
Salcedo, Juan Carlos
Issue Date
2026-03
Type
Dissertation
Language
en
Keywords
Student Success Science , Business, Engineering, Science, & Technological Innovation , C++ , Closed Loop Systems , Parallel Distributed Processing , Python , Quantum , Software Framework
Alternative Title
Abstract
With the arrival of robust error correcting quantum processors in the 5-year horizon, some sectors of the industry will require quantum computing knowledge and capabilities to stay competitive by integrating solutions with industrial grade closed loop applications. Current projections place mainstream quantum computing by 2030. Quantum computing technologies will be fundamental to maintaining competitive advantages in key technologies including cryptography, optimization, modeling quantum systems, molecular medicine, and image processing. This research develops a novel Quantum Parallel Framework (QPF) and builds the expertise to deploy quantum algorithms for distributed processing for industrial applications. This research addresses the need to develop mature quantum parallel algorithms executing in closed loop by using simulations and hardware for Quantum Processing Units (QPUs). The QPF hosts, schedules and synchronizes the execution of parallel quantum algorithms across simulators as well as IBM QPU hardware. The QPF framework is comprised of a Qiskit interface to manage communications to QPUs, C++ code and an OpenGL scene generation Graphical User Interface (GUI). The case study integrates the QPF manager framework with a Quantum Hadamard Edge Detector, Quantum Convolutional Neural Network, Quantum Charge Coupled Device, and Quantum Crypto Key Distribution algorithms. QPF parallelizes the execution of multiple QPU instances of the algorithm. Also, the QPF will compare to Quantum Interlin-q, a similar framework. In this research we explored alternate parallel processing methodologies that successfully and significantly yield better performance over classical computing. Although quantum technology is still maturing, the study provides an opportunity to explore complex distributed parallel quantum algorithms in preparation for mainstream quantum computing by 2030.