Is there a known measurement-complexity separation for closed-loop control of a drifting observable, or only a constant-factor gain?
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Consider holding a single-qubit observable (say a rotation angle or readout bias) at a setpoint while the device drifts, using closed-loop feedback at one measurement per round, with no model of the device. Gradient-based baselines like SPSA or finite-difference need several measurements to assemble a step. My question is about the fundamental limit, not a specific method: is there any known result establishing a separation in measurement complexity between feedback control and these baselines f
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Is there a known measurement-complexity separation for closed-loop control of a drifting observable, or only a constant-factor gain?
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Consider holding a single-qubit observable (say a rotation angle or readout bias) at a setpoint while the device drifts, using closed-loop feedback at one measurement per round, with no model of the device. Gradient-based baselines like SPSA or finite-difference need several measurements to assemble a step. My question is about the fundamental limit, not a specific method: is there any known result establishing a separation in measurement complexity between feedback control and these baselines for this kind of drift-tracking task, or is the best achievable advantage understood to be a constant factor? References to lower bounds or to the relevant control/estimation literature would be ideal.
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Matt Leibel
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