Energy landscape plummeting in variational quantum eigensolver: Subspace optimization, non-iterative corrections, and generator-informed initialization for improved quantum efficiency

Abstract

Variational Quantum Eigensolver (VQE) faces significant challenges due to hardware noise and the presence of barren plateaus and local traps in the optimization landscape. To mitigate the detrimental effects of these issues, we introduce a general formalism that optimizes hardware resource utilization and accuracy by projecting VQE optimizations onto a reduced-dimensional subspace, followed by a set of posteriori corrections. Our method partitions the ansatz into a lower dimensional principal subspace and a higher-dimensional auxiliary subspace based on a conjecture of temporal hierarchy present among the parameters during optimization. The adiabatic approximation exploits this hierarchy, restricting optimization to the lower dimensional principal subspace only. This is followed by an efficient higher dimensional auxiliary space reconstruction without the need to perform variational optimization. These reconstructed auxiliary parameters are subsequently included in the cost-function via a set of auxiliary subspace corrections (ASC), leading to a “plummeting effect” in the energy landscape toward a more optimal minimum without utilizing any additional quantum hardware resources. Numerical simulations show that, when integrated with any chemistry-inspired ansatz, our method can provide one to two orders of magnitude better estimation of the minima. In addition, based on the adiabatic approximation, we introduce a novel initialization strategy driven by unitary rotation generators for accelerated convergence of gradient-informed dynamic quantum algorithms. Our method shows heuristic evidence of alleviating the effects of local traps, facilitating convergence toward a more optimal minimum.