Quantum Amplitude Estimation (QAE) is a fundamental quantum routine that offers a quadratic speed-up over classical approaches. The original QAE protocol is based on phase estimation, whose quantum circuit depth and width, and assumptions of fault tolerance, are unfavorable for near-term quantum technology. Subsequent approaches attempt to replace the original protocol with hybrid iterative quantum-classical strategies. In this seminar, I will present BAE, a noise-aware Bayesian algorithm for QAE that combines quantum circuits with a statistical inference backbone. BAE can dynamically characterize device noise and adapt to it in real-time. Regardless of the presence of noise, it achieves lower error than any other QAE algorithm as a function of the cost. In the presence of decoherence, it is capable of learning when other algorithms fail. I will also explore extended applications of this quantum algorithm, namely in quantum metrology, calibration of superconducting qubits, and sensing.
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