# A Reconfigurable Framework for Hybrid Quantum-Classical Computing
## 2 Background and Related Work
### Related Work  
Numerous ongoing efforts [5] are investigating hybrid quantum-classical applications, and some of the recent and relevant work is discussed here. The work by [PERSON] et al. [3] proposed a cloud orchestration framework for quantum-classical applications that run on heterogeneous systems. Here, a high-level and hardware agnostic software environment was developed, which includes a resource estimator and scheduler running on software. A major research effort at Oak Ridge National Laboratory (ORNL) [2] focuses on integrating QPU capabilities into existing HPC workflows. Their framework integrates hardware and software workflows in a synergistic environment for quantum and classical resources, focusing mostly on CPU/GPU heterogeneity. Another multi-institutional collaborative research effort [26] is investigating the possibilities and challenges of QC-HPC for use in materials science. A collaborative effort led by IBM [4] investigated research ideas for quantum-centric supercomputing for use in quantum chemistry and quantum simulations of chemical reactions. The results of their work showed that classical HPC coupled to quantum resources can produce better approximate solutions to quantum simulation problem sizes that are intractable for classical-only or quantum-only systems.  
A recent study by [PERSON] et al. [12] introduces a hybrid quantum-classical CNN deployment specifically tailored for FPGA hardware, integrating parameter quantization and a novel quantum layer leveraging Hilbert vector space computations. Despite its success, demonstrated through improved speed and accuracy on the MNIST dataset, the [PERSON] et al. approach remains algorithm-specific, primarily optimized around their unique CNN architecture. This specialization limits its applicability across diverse quantum-classical computing applications. FPGA usage in quantum-classical systems has mostly been restricted to specific tasks, such as qubit control processors [7; 8], qubit readout error mitigation [9], and specialized simulation or emulation frameworks [27; 28; 29]. These specialized frameworks, while effective in achieving considerable speedups over traditional simulators, are typically algorithm-specific [30] and often pose challenges for novice users due to complexities involved with RTL or HLS tools.  
In contrast, our proposed research aims to establish a generalized FPGA-based framework capable of accelerating classical subroutines integral to a wide variety of hybrid quantum-classical (HQC) algorithms. Unlike algorithm-specific implementations, ourapproach emphasizes flexibility, enabling adaptation across different computational tasks by leveraging FPGA characteristics such as high throughput and low latency. Additionally, our unique contribution lies in demonstrating how FPGAs can synergize with quantum processing unit (QPU) accelerators to achieve significant computational advantages. A major research thrust of this effort involves developing a high-level methodology that seamlessly integrates the management of FPGA resources and quantum resources through a unified software stack. Thus, our methodology not only demonstrates FPGA's potential to accelerate classical computations within quantum-classical workflows but also advances accessibility and usability, paving the way for broader adoption in QC-HPC hybrid systems.