Quantum Computing

The project aims to benchmark quantum circuit simulation performance on different hardware architectures, including CPUs, GPUs, and FPGAs. By initially testing on CPUs and GPUs, it will establish a baseline for performance and identify areas where optimizations are possible. Following this, the project will incorporate FPGA-based accelerators, specifically using Alveo Cards (such as U55 and U250), to significantly enhance the speed of quantum circuit simulations. The FPGA accelerations offer parallel processing capabilities, which are ideal for handling complex quantum simulations that involve large datasets and intricate computations.

Use Cases

• Testing and Developing Quantum Algorithms
Quantum simulators enable researchers to test, refine, and optimize quantum algorithms in a cost-effective way before deploying them on actual quantum hardware. This includes testing algorithms like variational quantum circuits and quantum machine learning models. By identifying issues and optimizing performance in a simulated environment, researchers can reduce the cost of hardware usage significantly.
• Quantum Chemistry and Materials Science
Quantum simulators play a vital role in modeling complex molecular interactions, chemical reactions, and material properties. They help researchers design new drugs, materials, and catalysts by simulating electron behaviour and chemical bonding with greater accuracy than classical models, facilitating breakthroughs in chemistry and materials engineering.
• Optimization in Finance and Logistics
Quantum simulators allow for the development and testing of quantum algorithms to solve optimization problems such as portfolio optimization, risk management, and logistics. Algorithms like Grover’s search and QAOA can be tested on simulators, enabling industries to explore quantum solutions to large-scale, complex optimization tasks without needing quantum hardware immediately.
• High-Energy Physics and Quantum dynamics
Simulating quantum field theories and particle interactions is a formidable challenge for classical computers. Quantum simulators allow physicists to explore fundamental aspects of particle interactions, high-energy physics, and quantum states evolution phenomena like energy transfer in quantum batteries providing insights into areas that are otherwise computationally intractable.
• Climate Modeling and Weather Forecasting
Quantum simulators can be used to model chaotic and highly complex systems involved in climate science, such as fluid dynamics and atmospheric processes. Simulating these systems may lead to more accurate predictions in climate modeling and weather forecasting, potentially offering improved long-term and short-term predictions through quantum-enhanced models.

Salient Features

• High-Performance Quantum-Classical Algorithm Simulation: Enables quantum researchers to simulate and benchmark complex quantum circuits efficiently using a hybrid CPU-GPU-FPGA setup, for algorithm development.
• Hybrid Quantum-Classical Algorithm Implementation: The integration and testing of quantum machine learning (QML) models, such as quantum neural networks (QNNs), across different hardware architectures to optimize performance.
• Benchmarking and Performance Analysis: Provides a framework for comparing execution times and accuracy of quantum simulations on CPU, GPU, and FPGA, supporting hardware selection and optimization for specific workloads.
• FPGA-Accelerated Quantum Computing: Leverages FPGA hardware to accelerate computationally intensive quantum operations, enabling faster processing and reduced energy consumption.
• Interactive Development Environment: Offers a Python-based interface compatible with Jupyter notebooks, VS Code, and Colab for accessible coding, debugging, and optimization of quantum circuits.