Quantum eigenvalue estimation via time series analysis
By Rolando D. Somma. New Journal of Physics, 2019
133 citations recorded by the source corpus.
We present an efficient method for estimating the eigenvalues of a Hamiltonian H from the expectation values of the evolution operator for various times. For a given quantum state ρ, our method outputs a list of eigenvalue estimates and approximate probabilities. Each probability depends on the support of ρ in those eigenstates of H associated with eigenvalues within an arbitrarily small range. The complexity of our method is polynomial in the inverse of a given precision parameter ϵ, which is the gap between eigenvalue estimates. Unlike the well-known quantum phase estimation algorithm that uses the quantum Fourier transform, our method does not require large ancillary systems, large sequences of controlled operations, or preserving coherence between experiments, and is therefore more attractive for near-term applications. The output of our method can be used to estimate spectral properties of H and other expectation values efficiently, within additive error proportional to ϵ.
Subfields: Artificial Intelligence
View Paper: https://www.semanticscholar.org/paper/8302c020683fc75d9af7bf40805b9c6d4b3b77dd
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In Artificial Intelligence
Citations in the corpus
- Error mitigation with Clifford quantum-circuit data (2020, methodology): “…precisely it estimates probability of | χ i being in an eigenstate of H with an eigenvalue ˜ λ j − (cid:15) λ ˜ λ j + (cid:15) where ˜ λ j is a center of the bin and (cid:15) defines width of the bin with a quantity q j which definition and convergence properties are discussed in detail in [43].”