Numeric optimization for configurable, parallel, error-robust entangling
gates in large ion registers
- URL: http://arxiv.org/abs/2005.00366v1
- Date: Fri, 1 May 2020 13:17:59 GMT
- Title: Numeric optimization for configurable, parallel, error-robust entangling
gates in large ion registers
- Authors: Christopher D. B. Bentley, Harrison Ball, Michael J. Biercuk, Andre R.
R. Carvalho, Michael R. Hush, and Harry J. Slatyer
- Abstract summary: We study a class of entangling gates for trapped atomic ions and demonstrate the use of numeric optimization techniques to create a wide range of fast, error-robust gate constructions.
Our approach introduces a framework for numeric optimization using individually addressed, amplitude and phase modulated controls targeting maximally and partially entangling operations on ion pairs, complete multi-ion registers, multi-ion subsets of large registers, and parallel operations within a single register.
- Score: 0.0
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: We study a class of entangling gates for trapped atomic ions and demonstrate
the use of numeric optimization techniques to create a wide range of fast,
error-robust gate constructions. Our approach introduces a framework for
numeric optimization using individually addressed, amplitude and phase
modulated controls targeting maximally and partially entangling operations on
ion pairs, complete multi-ion registers, multi-ion subsets of large registers,
and parallel operations within a single register. Our calculations and
simulations demonstrate that the inclusion of modulation of the difference
phase for the bichromatic drive used in the M\o lmer-S\o rensen gate permits
approximately time-optimal control across a range of gate configurations, and
when suitably combined with analytic constraints can also provide robustness
against key experimental sources of error. We further demonstrate the impact of
experimental constraints such as bounds on coupling rates or modulation
band-limits on achievable performance. Using a custom optimization engine based
on TensorFlow we also demonstrate time-to-solution for optimizations on ion
registers up to 20 ions of order tens of minutes using a local-instance laptop,
allowing computational access to system-scales relevant to near-term
trapped-ion devices.
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