Quantum Computing

Quantum computing promises to revolutionize quantum chemistry and materials science by enabling the simulation of complex systems that are intractable for classical computers. We have demonstrated that algorithms like the Variational Quantum Eigensolver (VQE) allow quantum devices to variationally determine ground-state energies of molecular systems, holding promise for practical applications even on today's noisy intermediate-scale quantum (NISQ) hardware. 

To facilitate the above, we have developed more efficient and noise-resilient ansatzes (e.g., the Parallelized Givens Ansatz), as well as leveraged machine learning techniques to optimize wavefunction parameters and mitigate errors. Currently, we are implementing the sample-based quantum diagonalization (SQD) techniques to scale our simulations to even larger systems.

Relevant Publications

1. Nirmal, M. R.; Khandelwal, A.; Nambiar, M.; Yamijala, S. S. R. K. C. Parallelized Givens Ansatz for Molecular Ground-States: Bridging Accuracy and Efficiency on NISQ Platforms. arXiv [quant-ph], 2025. Full text

2. Nirmal, M. R.; Yamijala, S. S. R. K. C.; Ghosh, K.; Kumar, S.; Nambiar, M. Resource-Efficient Quantum Circuits for Molecular Simulations: A Case Study of Umbrella Inversion in Ammonia. In 2024 16th International Conference on COMmunication Systems & NETworkS (COMSNETS); IEEE, 2024. Full text

3. Ghosh, Kalpak, Sumit Kumar, Nirmal Mammavalappil Rajan, and Sharma SRKC Yamijala. Deep neural network assisted quantum chemistry calculations on quantum computers. ACS omega, 8, no. 50, 48211-48220, (2023). Full text