Qubit-efficient exponential suppression of errors
ORAL
Abstract
Achieving a practical advantage with near-term quantum computers hinges on having effective methods to suppress errors. Recent breakthroughs have introduced methods capable of exponentially suppressing errors by preparing multiple noisy copies of a state and virtually distilling a more purified version. Here we present an alternative method, the Resource-Efficient Quantum Error Suppression Technique (REQUEST), that adapts this breakthrough to much fewer qubits by making use of active qubit resets, a feature now available on commercial platforms. Our approach exploits a space/time trade-off to achieve a similar error reduction using only 2N+1 qubits as opposed to MN+1 qubits, for M copies of an N qubit state. Additionally, we propose a method using near-Clifford circuits to find the optimal number of these copies in the presence of realistic noise, which limits this error suppression. We perform a numerical comparison between the original method and our qubit-efficient version with a realistic trapped-ion noise model. We find that REQUEST can reproduce the exponential suppression of errors of the virtual distillation approach, while out-performing virtual distillation when fewer than 3N+1 qubits are available. Finally, we examine the scaling of the number of shots N_S required for REQUEST to achieve useful corrections. We find that N_S remains reasonable well into the quantum advantage regime where N is hundreds of qubits.
*This work was supported by the Quantum Science Center (QSC), a National Quantum Information Science Research Center of the U.S. DOE and by the Laboratory Directed Research and Development (LDRD) program of Los Alamos National Laboratory (LANL) under project numbers 20190659PRD4 and 20210116DR. PJC also acknowledges support from the LANL ASC Beyond Moore's Law project. LC was also supported by the U.S. DOE, Office of Science, Office of Advanced Scientific Computing Research, under the Quantum Computing Application Teams program.
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Publication: https://arxiv.org/abs/2102.06056
Presenters
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Andrew T Arrasmith
- Los Alamos National Laboratory