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- W2892880351 abstract "Quantum phase estimation is the workhorse behind any quantum algorithm and apromising method for determining ground state energies of strongly correlatedquantum systems. Low-cost quantum phase estimation techniques make use ofcircuits which only use a single ancilla qubit, requiring classicalpost-processing to extract eigenvalue details of the system. We investigatechoices for phase estimation for a unitary matrix with low-depth noise-free ornoisy circuits, varying both the phase estimation circuits themselves as wellas the classical post-processing to determine the eigenvalue phases. We work inthe scenario when the input state is not an eigenstate of the unitary matrix.We develop a new post-processing technique to extract eigenvalues from phaseestimation data based on classical time-series analysis, and contrast this toan analysis via Bayesian methods. We calculate the variance in estimatingsingle eigenvalues via the time-series analysis analytically, finding that itscales to first order in the number of experiments performed, and to first orsecond order (depending on the experiment design) in the circuit depth.Numerical simulations confirm this scaling for both estimators. We observenumerically that these post-processing techniques lead to a variance inestimating the lowest eigenvalue phase which scales to first order in theoverlap between the starting and ground states, to second order in the gapbetween the ground and excited states, and to second order in the decoherencetime of the system. We attempt to compensate for noise in both classicalpost-processing techniques, finding good results in the presence ofdepolarizing noise, but smaller improvements in realistic circuit-levelsimulations." @default.
- W2892880351 created "2018-10-05" @default.
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- W2892880351 date "2018-09-25" @default.
- W2892880351 modified "2023-09-27" @default.
- W2892880351 title "Quantum phase estimation for noisy, small-scale experiments" @default.
- W2892880351 hasPublicationYear "2018" @default.
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