Optimal low-depth quantum signal-processing phase estimation

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Udgivet i:Nature Communications vol. 16, no. 1 (2025), p. 1504
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Nature Publishing Group
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022 |a 2041-1723 
024 7 |a 10.1038/s41467-025-56724-x  |2 doi 
035 |a 3165227986 
045 2 |b d20250101  |b d20251231 
084 |a 145839  |2 nlm 
245 1 |a Optimal low-depth quantum signal-processing phase estimation 
260 |b Nature Publishing Group  |c 2025 
513 |a Journal Article 
520 3 |a Quantum effects like entanglement and coherent amplification can be used to drastically enhance the accuracy of quantum parameter estimation beyond classical limits. However, challenges such as decoherence and time-dependent errors hinder Heisenberg-limited amplification. We introduce Quantum Signal-Processing Phase Estimation algorithms that are robust against these challenges and achieve optimal performance as dictated by the Cramér-Rao bound. These algorithms use quantum signal transformation to decouple interdependent phase parameters into largely orthogonal ones, ensuring that time-dependent errors in one do not compromise the accuracy of learning the other. Combining provably optimal classical estimation with near-optimal quantum circuit design, our approach achieves a standard deviation accuracy of 10−4 radians for estimating unwanted swap angles in superconducting two-qubit experiments, using low-depth (&#xa0;<&#xa0;10) circuits. This represents up to two orders of magnitude improvement over existing methods. Theoretically and numerically, we demonstrate the optimality of our algorithm against time-dependent phase errors, observing that the variance of the time-sensitive parameter φ scales faster than the asymptotic Heisenberg scaling in the small-depth regime. Our results are rigorously validated against the quantum Fisher information, confirming our protocol’s ability to achieve unmatched precision for two-qubit gate learning.Fault-tolerant quantum computing would require very high accuracy in quantum gate characterisation. Here, the authors introduce an optimal low-depth phase estimation method inspired by quantum signal processing, significantly improving gate calibration accuracy. 
653 |a Time dependence 
653 |a Accuracy 
653 |a Quantum computing 
653 |a Quantum entanglement 
653 |a Cramer-Rao bounds 
653 |a Algorithms 
653 |a Parameter sensitivity 
653 |a Fault tolerance 
653 |a Parameter robustness 
653 |a Circuit design 
653 |a Machine learning 
653 |a Qubits (quantum computing) 
653 |a Signal processing 
653 |a Asymptotic methods 
653 |a Parameter estimation 
653 |a Optimization 
653 |a Errors 
653 |a Fisher information 
653 |a Design standards 
653 |a Environmental 
773 0 |t Nature Communications  |g vol. 16, no. 1 (2025), p. 1504 
786 0 |d ProQuest  |t Health & Medical Collection 
856 4 1 |3 Citation/Abstract  |u https://www.proquest.com/docview/3165227986/abstract/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch 
856 4 0 |3 Full Text - PDF  |u https://www.proquest.com/docview/3165227986/fulltextPDF/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch