The article discusses the use of a quantum-centric supercomputing architecture to simulate chemical systems, leveraging the capabilities of a quantum computer and classical distributed computing. The authors aim to address the limitations of current quantum computers by combining them with classical resources to tackle larger and more complex chemical problems. They focus on the simulation of electronic structure problems, specifically the breaking of the triple bond in N2 and the ground states of iron-sulfur clusters [2Fe-2S] and [4Fe-4S]. Key aspects of their approach include: 1. **Quantum Circuits and Classical Post-Processing**: They use a truncated version of the local unitary cluster Jastrow (LUCJ) ansatz to prepare the initial state on the quantum processor. The quantum circuits are executed on a quantum computer, and the results are processed using a hybrid estimator that combines quantum and classical techniques. 2. **Configuration Recovery**: To improve the accuracy of the quantum simulations, they introduce a self-consistent configuration recovery technique. This method helps in recovering noiseless configuration samples from noisy quantum measurements, improving the quality of the ground state approximation. 3. **Hybrid Quantum-Classical Workflow**: The workflow involves classical nodes for tasks such as projection and diagonalization, which are crucial for obtaining accurate energy eigenvalues and wavefunctions. The classical nodes process the noisy quantum samples to produce upper bounds on the ground-state energy and wavefunctions. 4. **Experimental Results**: The authors present experimental results using a subset of a 133-qubit Heron quantum processor and up to 6400 nodes of the Fugaku supercomputer. They simulate the breaking of the N2 triple bond and the ground states of iron-sulfur clusters, demonstrating the effectiveness of their approach in addressing complex chemical problems. The article highlights the potential of combining quantum and classical computing to overcome the limitations of current quantum hardware and to explore a broader range of chemical systems. The methods described in the paper provide a framework for future research in quantum chemistry and computational physics.The article discusses the use of a quantum-centric supercomputing architecture to simulate chemical systems, leveraging the capabilities of a quantum computer and classical distributed computing. The authors aim to address the limitations of current quantum computers by combining them with classical resources to tackle larger and more complex chemical problems. They focus on the simulation of electronic structure problems, specifically the breaking of the triple bond in N2 and the ground states of iron-sulfur clusters [2Fe-2S] and [4Fe-4S]. Key aspects of their approach include: 1. **Quantum Circuits and Classical Post-Processing**: They use a truncated version of the local unitary cluster Jastrow (LUCJ) ansatz to prepare the initial state on the quantum processor. The quantum circuits are executed on a quantum computer, and the results are processed using a hybrid estimator that combines quantum and classical techniques. 2. **Configuration Recovery**: To improve the accuracy of the quantum simulations, they introduce a self-consistent configuration recovery technique. This method helps in recovering noiseless configuration samples from noisy quantum measurements, improving the quality of the ground state approximation. 3. **Hybrid Quantum-Classical Workflow**: The workflow involves classical nodes for tasks such as projection and diagonalization, which are crucial for obtaining accurate energy eigenvalues and wavefunctions. The classical nodes process the noisy quantum samples to produce upper bounds on the ground-state energy and wavefunctions. 4. **Experimental Results**: The authors present experimental results using a subset of a 133-qubit Heron quantum processor and up to 6400 nodes of the Fugaku supercomputer. They simulate the breaking of the N2 triple bond and the ground states of iron-sulfur clusters, demonstrating the effectiveness of their approach in addressing complex chemical problems. The article highlights the potential of combining quantum and classical computing to overcome the limitations of current quantum hardware and to explore a broader range of chemical systems. The methods described in the paper provide a framework for future research in quantum chemistry and computational physics.