5 papers
Advancing Practical Quantum Embedding Simulations via Operator Commutativity Based State Preparation for Complex Chemical Systems
Dibyendu Mondal, Ashish Kumar Patra, Rahul Maitra
Determining the exponentially scaled ground state wavefunction and the associated molecular properties remains one of the central challenges in quantum chemistry. Hybrid quantum-cl…
Physics-Informed Generative Machine Learning for Accelerated Quantum-centric Supercomputing
Chayan Patra, Dibyendu Mondal, Sonaldeep Halder +4
Quantum centric supercomputing (QCSC) framework, such as sample-based quantum diagonalization (SQD) holds immense promise toward achieving practical quantum utility to solve challe…
Operator Commutativity Screening and Progressive Operator Block Reordering toward Many-body Inspired Quantum State Preparation
Dibyendu Mondal, Debaarjun Mukherjee, Rahul Maitra
In the field of quantum chemistry, the variational quantum eigensolver (VQE) has emerged as a highly promising approach to determine molecular energies and properties within the no…
Efficient quantum state preparation through seniority driven operator selection
Dipanjali Halder, Dibyendu Mondal, Rahul Maitra
Quantum algorithms require accurate representations of electronic states on a quantum device, yet the approximation of electronic wave functions for strongly correlated systems rem…
Machine Learning Approach towards Quantum Error Mitigation for Accurate Molecular Energetics
Srushti Patil, Dibyendu Mondal, Rahul Maitra
Despite significant efforts, the realization of the hybrid quantum-classical algorithms has predominantly been confined to proof-of-principles, mainly due to the hardware noise. Wi…