Adaptive time Compressed QITE (ACQ) and its geometrical interpretation

Preparing the ground state of a given Hamiltonian is a computational task of interest in many fields, such as material science, chemistry and even some optimization problems, to name a few. Efficiently preparing ground states for large, strongly correlated systems is a challenging task for both classical and quantum hardware. Drawing from classical optimization methods, e.g. dynamical optimization techniques, one may deduce the spectral decomposition in manner that avoids direct spectral decomposition and is amenable to Trotterization methods. An instance of the latter is ground state preparation by Imaginary Time Evolution (ITE), understood in physical terms as a natural cooling process. Its quantum version QITE (Quantum Imaginary Time Evolution) aims at implementing ITE in a quantum computer. In this paper we introduce a novel QITE algorithm, which leverages underlying geometric properties for algorithm-runtime and circuit depth reduction. This will materialize in the form of an iterative Line Search approach for minimization of energy as well as a Newton's method approach for the deduction of the optimal time-steps for each iteration of QITE. The depth-reduction will be carried out via approximating the resulting unitary operator estimated from the QITE algorithm with unitary operator which is an element of a one-parameter group; making expressible as a single unitary in a quantum circuit. Furthermore, we perform a numerical study to stablish the scaling of fidelities with the different truncation parameters and give gate counts estimates for each.