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Optimal low-depth quantum signal-processing phase estimation
Yulong Dong1,2, Jonathan A Gross3, Murphy Yuezhen Niu4,5
1Google Quantum AI, Venice, California, CA, 90291, USA. dongyl@berkeley.edu.
Nature Communications
|February 10, 2025
Summary
Quantum signal processing enhances parameter estimation accuracy beyond classical limits. New algorithms achieve high precision in quantum experiments, overcoming decoherence and errors for improved two-qubit gate learning.
Area of Science:
- Quantum Information Science
- Quantum Computing
- Quantum Metrology
Background:
- Quantum effects like entanglement offer enhanced parameter estimation accuracy.
- Decoherence and time-dependent errors limit Heisenberg-limited amplification in quantum systems.
Purpose of the Study:
- Introduce robust Quantum Signal-Processing Phase Estimation algorithms.
- Achieve optimal quantum parameter estimation performance beyond classical limits.
- Mitigate challenges posed by decoherence and time-dependent errors.
Main Methods:
- Employ quantum signal transformation to decouple phase parameters.
- Utilize provably optimal classical estimation techniques.
- Combine with near-optimal quantum circuit design for low-depth circuits (<10 gates).
Main Results:
- Achieve standard deviation accuracy of 10-4 radians in estimating unwanted swap angles.
- Demonstrate up to two orders of magnitude improvement over existing methods.
- Show algorithm optimality against time-dependent phase errors, with variance scaling faster than Heisenberg limit in the small-depth regime.
Conclusions:
- Validated against quantum Fisher information, confirming unmatched precision for two-qubit gate learning.
- Quantum Signal-Processing Phase Estimation algorithms are robust against decoherence and time-dependent errors.
- The developed protocol significantly advances the precision of quantum parameter estimation.
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