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Unified evolutionary optimization for high-fidelity spin qubit operations
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Unified evolutionary optimization for high-fidelity spin qubit operations
abstract
Developing optimal strategies to calibrate quantum processors for high-fidelity operation is one of the outstanding challenges in quantum computing today. Here, we demonstrate multiple examples of high-fidelity operations achieved using a unified global optimization-driven automated calibration routine on a six dot semiconductor quantum processor. Within the same algorithmic framework we optimize readout, shuttling and single-qubit quantum gates by tailoring task-specific cost functions and tuning parameters based on the underlying physics of each operation. Our approach reaches systematically $99\%$ readout fidelity, $>99\%$ shuttling fidelity over an effective distance of 10$\mu$m, and $>99.5\%$ single-qubit gate fidelity on timescales similar or shorter compared to those of expert human operators. The flexibility of our gradient-free closed loop algorithmic procedure allows for seamless application across diverse qubit functionalities while providing a systematic framework to tune-up semiconductor quantum devices and enabling interpretability of the identified optimal operation points.
Forward citations
Cited by 4 Pith papers
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A generative QMLC framework tokenizes GST data, embeds it via curriculum-trained set-vision transformers into a context-aware latent space, and uses diffusion models to synthesize circuits conditioned on desired measu...
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Four-state discrimination for a pair of spin qubits via gate reflectometry
Gate reflectometry on a silicon double quantum dot with a micromagnet can discriminate all four two-qubit basis states in a single Pauli-spin-blockade measurement.
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g-tensor Optimization in Ge/SiGe Quantum Dots
A flexible optimization framework is introduced to suppress in-plane g-tensor components in SiGe-Ge-SiGe quantum wells by tuning silicon concentration, enabling gapless single-spin qubit encoding.
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