Two-qubit CZ gates robust against charge noise in silicon while compensating for crosstalk using neural network

Abstract

The fidelity of two-qubit gates using silicon spin qubits is limited by charge noise. When attempting to dynamically compensate for charge noise using local echo pulses, crosstalk can cause complications. We present a method of using a deep neural network to optimize the components of an analytically designed composite pulse sequence, resulting in a two-qubit gate robust against charge noise errors while also taking crosstalk into account. We analyze two experimentally motivated scenarios. For a scenario with strong EDSR driving and negligible crosstalk, the composite pulse sequence yields up to an order of magnitude improvement over a simple cosine pulse. In a scenario with moderate ESR driving and appreciable crosstalk such that simple analytical control fields are not effective, optimization using the neural network approach allows one to maintain order-of-magnitude improvement despite the crosstalk.

arXiv comment10 pages, 12 figures, supplementary materials included as ancillary files

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• Devitalizing noise-driven instability of entangling logic in silicon devices with bias controls
• Neural-network-designed three-qubit gates robust against charge noise and crosstalk in silicon
• Theory of the Simultaneous Transient Dispersive Readout of Multiple Spin Qubits
• Variational quantum compiling for three-qubit gates design in quantum dots
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• Enhanced local addressability of a spin array with local exchange pulses and global microwave driving