Abstract Accurate state preparation is a critical bottleneck in many quantum algorithms, particularly those for ground-state energy estimation. Even in fault-tolerant quantum computing, preparing a quantum state with sufficient overlap with the desired eigenstate remains a major challenge. To address this, we develop a unified cost-aware framework for filtered-state preparation that enhances the overlap of a given input state through spectral filtering. The framework covers polynomial and trigonometric realizations of filters and makes explicit the trade-off among overlap amplification, preparation success probability, and filter-implementation cost. As representative examples, we analyze Gaussian filters and introduce a modified Krylov-subspace-based filter that improves the success-probability/overlap trade-off relevant to filtered state preparation. Within this framework, we study a filtered variant of quantum phase estimation (FQPE) that mitigates the unfavorable dependence on the initial overlap present in standard quantum phase estimation. Numerical experiments on Fermi–Hubbard models show that FQPE reduces the total runtime by more than two orders of magnitude in the high-precision regime, with overlap amplification exceeding a factor of one hundred.
Phase estimation, due to Kitaev \cite{Kit95}, is one of the most fundamental subroutines in quantum computing. In the basic scenario, one is given black-box access to a unitary U , and an eigenstate | ψ ⟩ of U with unknown eigenvalue e i θ , and the task is to estimate the eigenphase θ within ± δ , with high probability. The cost of an algorithm for us is the number of applications of U and U − 1 .We tightly characterize the cost of several variants of phase estimation where we are no longer given an eigenstate, but are required to estimate the maximum eigenphase of U , aided by advice in the form of states (or a unitary preparing those states) which are promised to have at least a certain overlap γ with the top eigenspace.We give algorithms and nearly matching lower bounds for all ranges of parameters.We show that a small number of copies of the advice state (or of an advice-preparing unitary) are not significantly better than having no advice at all. We also show that having lots of advice (applications of the advice-preparing unitary) does not significantly reduce cost, and neither does knowledge of the eigenbasis of U . We immediately obtain a lower bound on the complexity of the Unitary recurrence time problem, resolving an open question of She and Yuen \cite{SY23}.Lastly, we study how efficiently one can reduce the error probability in the basic phase-estimation scenario. We show that a phase-estimation algorithm with precision δ and error probability ϵ has cost Ω ( 1 δ log 1 ϵ ) , matching an easy upper bound. This contrasts with some other scenarios in quantum computing (e.g., search) where error-probability reduction costs only a factor O ( log ( 1 / ϵ ) ) . Our lower bound uses a variant of the polynomial method with trigonometric polynomials.
In recent years, quantum algorithms have been proposed which use quantum phase estimation (QPE) coherently as a subroutine without measurement. In order to do this effectively, the routine must be able to distinguish eigenstates with success probability close to unity. In this paper, we provide the first systematic comparison between two approaches towards maximizing this success probability, one using the quantum singular value transform and the other leveraging window functions, which have been previously studied as priors of the phase value distribution. We find that the quantum singular value transform is significantly outclassed by the window function approach, with the latter able to achieve between 3 and 5 orders of magnitude improvement in the success probability with approximately 1/4 the query cost. Our circuit simulation results indicate that QPE is not a domain which benefits from the integration of QSVT and we show that the use of the Kaiser window function is currently the most practical choice for realizing QPE with high success probability.
Suppose we can apply the unitary U=eiH for some Hamiltonian H, and are given access to a unitary that prepares a guiding state promised to have overlap at least γ>0 with the ground space of H. Our goal is to estimate the ground-state energy of H within additive error δ>0 and success probability at least 1−ε, ε>0. How many applications of U and its inverse U−1 are necessary and sufficient? This quantity corresponds to the total Hamiltonian-simulation time needed. An upper bound O(log(1/ε)log(1/γ)/γδ) was known, and was improved to O(log(1/ε)/γδ) very recently [JW26]. A matching lower bound was known whenever one of the three parameters δ,γ,ε was held constant [MdW26]. In this paper we prove the joint lower bound Ω(log(1/ε)/γδ) with the tight ε-dependence provided the dimension of H is at least log(1/ε)/γ2. Furthermore, we show that this same lower bound (with slightly larger dimension) holds for both the special case in which the ground state is guaranteed to be unique and H has a gap of δ between its first and second eigenvalue; and for ground-state preparation, where δ denotes the spectral gap and ε now is the approximation error. The lower bounds also apply when the Hamiltonian can be accessed via its block-encoding, and when fractional powers of U are allowed, as in continuous-time Hamiltonian simulation. Lastly, improved upper bounds are known when H is nonnegative and presented as a sum of squares; and our results imply the lower bound Ω(log(1/ε)/γδ) for this case.