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The three key elements of a quantum simulation are state preparation, time evolution, and measurement. While the complexity scaling of dynamics and measurements are well known, many state preparation methods are strongly system-dependent and require prior knowledge of the systems eigenvalue spectrum. Here, we report on a quantum-classical implementation of the coupled-cluster Greens function (CCGF) method, which replaces explicit ground state preparation with the task of applying unitary operators to a simple product state. While our approach is broadly applicable to a wide range of models, we demonstrate it here for the Anderson impurity model (AIM). The method requires a number of T gates that grows as $ mathcal{O} left(N^5 right)$ per time step to calculate the impurity Greens function in the time domain, where $N$ is the total number of energy levels in the AIM. For comparison, a classical CCGF calculation of the same order would require computational resources that grow as $ mathcal{O} left(N^6 right)$ per time step.
Greens function methods within many-body perturbation theory provide a general framework for treating electronic correlations in excited states. Here we investigate the cumulant form of the one-electron Greens function based on the coupled-cluster eq
Coupled cluster Greens function (CCGF) approach has drawn much attention in recent years for targeting the molecular and material electronic structure problems from a many-body perspective in a systematically improvable way. Here, we will present a b
Quantum metrology plays a fundamental role in many scientific areas. However, the complexity of engineering entangled probes and the external noise raise technological barriers for realizing the expected precision of the to-be-estimated parameter wit
Within the self-energy embedding theory (SEET) framework, we study coupled cluster Greens function (GFCC) method in two different contexts: as a method to treat either the system or environment present in the embedding construction. Our study reveals
Many quantum algorithms have daunting resource requirements when compared to what is available today. To address this discrepancy, a quantum-classical hybrid optimization scheme known as the quantum variational eigensolver was developed with the phil