Readout-Rank Laws for Isotropic Quantum Tangents
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Abstract (arXiv)
Deep parameterized quantum circuits may remain sensitive to a parameter change while the observables retained by a learning model barely respond. We study this separation for a fixed computational-basis measurement. For a pure-state tangent, we compare the quantum Fisher information FQ, the Fisher information Frm full in the complete bitstring distribution, and the largest variance-normalized response mathcal Imathcal A available to a diagonal readout space mathcal A. If the joint state--tangent frame is Haar random, we prove that the two successive information fractions are independent Beta variables whose means are 1/2 and r/(2n-1), where r is the centered dimension of the readout. Consequently, even the joint span of all computational-basis Pauli strings through any fixed weight k retain only O(nk2-n) of the full-record information. Exact-statevector experiments across six circuit families show increasing finite-size agreement with this hierarchy in five nonconserving ensembles as the circuit depth grows. A number-conserving family departs strongly from the isotropic prediction even after correcting the support and readout rank, showing that rank alone is insufficient without tangent isotropy.
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References · 8
- 2018Supervised learning with quantum enhanced feature spacescited by 654
- 2018Barren plateaus in quantum neural network training landscapescited by 624
- 2020Predicting Many Properties of a Quantum System from Very Few Measurementscited by 229
- 2020Noise-Induced Barren Plateaus in Variational Quantum Algorithmscited by 192
- 2021Connecting ansatz expressibility to gradient magnitudes and barren plateauscited by 180
- 2019Quantum Natural Gradientcited by 104
- 2020Entanglement Induced Barren Plateauscited by 101
- 2019Stochastic gradient descent for hybrid quantum-classical optimizationcited by 59
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