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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| Udgivet: |
Nature Publishing Group
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| Fag: | |
| Online adgang: | Citation/Abstract Full Text - PDF |
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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 ( < 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 |