Quantum computing has shown great potential to transform specific algorithms and applications and is expected to work alongside traditional high-performance computing (HPC) environments. Furthermore, noisy intermediate-scale quantum (NISQ) devices have emerged as powerful computational platforms, but they face challenges such as limited qubit coherence times and a high probability of errors. Due to the complexity of quantum algorithms, the need for error correction becomes critical, which introduces additional complexity. When developing, testing, and debugging quantum algorithms, quantum simulators play an important role by providing a controlled and error-free environment. They also improve availability when physical resources are limited.
Existing works include several approaches to integrate quantum computing into HPC environments. This integration technique utilizes the power of quantum algorithms while maintaining the reliability and versatility of traditional computing. It falls into two main categories, loose integration and tight integration. Loose integration has a looser coupling between quantum and classical systems, while tight integration utilizes quantum processing units (QPUs) on HPC nodes directly, similar to how graphics processing units (GPUs) are integrated into HPC compute nodes. This coupling allows classical systems to handle traditional tasks while quantum processors solve specific problems that they solve better. However, resource management and performance optimization pose challenges in these hybrid systems.
Researchers at Oak Ridge National Laboratory in Oak Ridge, Tennessee, USA, have proposed a quantum-first framework (QFw) focused on flexible integration of quantum computing with HPC environments. This approach treats quantum computers as separate components within the larger HPC system and focuses on local integration. In this case, a quantum machine is connected to the HPC center using high-bandwidth interconnects and a distributed file system, which connects it to classical HPC systems. This framework provides a unified solution for hybrid applications with the maximum benefits of HPC for quantum simulation, with an easy transition to real quantum hardware. It also provides a flexible infrastructure on the Frontier supercomputer, supporting various quantum circuit construction tools and simulators.
The proposed QFw is designed to enable researchers to take full advantage of HPC resources for quantum computing, while enabling a seamless transition between simulation backends and real quantum hardware. With QFw, applications can separately allocate HPC resources for classical and quantum tasks and use any circuit composition software they prefer. The framework provides a backend for converting native quantum circuit structures into QASM 2.0, a common quantum task format. The Quantum Task Manager (QTM) layer enforces specific workflows such as circuit slicing and result aggregation. The Quantum Platform Manager (QPM) handles communication with the platform, executing quantum tasks through platform-specific operations.
The QFw is evaluated using different frontends such as Qiskit and PennyLane, and backends such as TNQVM and NWQ-Sim. The SupermarQ benchmark is used to generate a 20-qubit GHZ circuit and measure the performance. The results obtained by evaluating the QFw show the efficiency by running multiple simulations together and completing 8 simulations in 66.97 seconds, compared to 52.47 seconds for a single simulation. This highlights the time-saving potential of simulating independent circuits simultaneously and the benefits of intelligent resource management. Furthermore, a PennyLane application is successfully integrated, demonstrating the flexibility of the QFw to combine different frontends and backends.
In conclusion, researchers at Oak Ridge National Laboratory have presented a quantum framework (QFw) that offers researchers the flexibility to advance quantum research on the Frontier supercomputer without any technical barriers. It allows users to use any frontend circuit construction software with any backend simulation package, making it easier for researchers to focus on their tasks. QFw enables simulations on HPC systems to go beyond normal limits and be easily transferred to physical quantum hardware. Its versatility allows for the integration of different quantum platforms, without infrastructure or application changes. Furthermore, QFw’s plugin architecture provides a common API to integrate new platforms easily.
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Sajjad Ansari is a final year student from IIT Kharagpur. As a technology enthusiast, he delves into practical applications of ai, focusing on understanding the impact of ai technologies and their real-world implications. He aims to articulate complex ai concepts in a clear and accessible manner.
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