Speaker
Description
Quantum state preparation is a central challenge in quantum computation and quantum simulation, enabling the exploration of complex many-body phenomena in Quantum Mechanics, Quantum Field Theory, and Quantum Chemistry. Existing paradigms such as Variational Quantum Algorithms (VQAs) and Adiabatic Preparation (AP) offer viable pathways, but each suffers from intrinsic limitations—VQAs from barren plateaus and optimization overheads, and AP from stringent adiabaticity requirements and spectral bottlenecks.
In this work, we introduce Branched-Subspaces Adiabatic Preparation (B-SAP), a hybrid algorithm that merges the conceptual advantages of VQAs and AP by leveraging concepts from group theory and classical post-processing to enable efficient approximation of ground and excited states of many-body Hamiltonians. B-SAP generates a sequence of branched, symmetry-adapted subspaces that steer the adiabatic evolution and confine the search space, significantly reducing quantum resource requirements while relaxing the strict conditions typically required for the adiabatic theorem.
We validate the method on the one-dimensional XYZ Heisenberg model with periodic boundary conditions, benchmarking performance across a wide range of anisotropies and system sizes. Our results demonstrate accurate preparation of the low-energy eigenstates with circuit depths that scale polynomially with system size, highlighting B-SAP as a resource-efficient and scalable approach for quantum state preparation.
References
https://arxiv.org/abs/2505.13717
| Sessions | Quantum Simulation |
|---|---|
| Invited | No |