Prof. Ravi’s research revolves around bringing together expertise from quantum computing, classical computing, and often from the sciences, to build toward a hybrid computing ecosystem for practical quantum advantage. Research thrusts include:
Classical Application Transformation: Methods to classically ‘prepare’ an application’s quantum circuit(s) prior to execution, to minimize their noisy and expensive quantum footprint. Such classical preparation can often involve the use of HPC systems or classically accelerated scientific computing methods.
Adaptive Noise Mitigation: Classically-inspired deployment of noise mitigation techniques that adapt to the dynamic noise characteristics of devices and the requirements of applications. A good understanding of the applications of interest (often scientific use cases) is important.
Scalable Error Correction: Designing fault tolerant systems with quantum error correction (QEC) in a scalable manner, alleviating bottlenecks at the quantum-classical technology interface. This involves work in cryogenic electronics, accelerating graph computations, and more.
Efficient Resource Management: Classical techniques to map applications to appropriate quantum devices, as well as post-process outputs in a device-aware manner, to boost application fidelity. Applications can vary from physics, chemistry usecases, to optimization problems, to cryptography, and more.