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Quantum natural gradient generalised to noisy and non-unitary circuits

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arxiv 1912.08660 v5 pith:7CQMEGKR submitted 2019-12-18 quant-ph

classification quant-ph
keywords quantumcircuitsmetricnoisygradientnaturalspacestates
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Variational quantum algorithms are promising tools whose efficacy depends on their optimisation method. For noise-free unitary circuits, the quantum generalisation of natural gradient descent has been introduced and shown to be equivalent to imaginary time evolution: the approach is effective due to a metric tensor reconciling the classical parameter space to the device's Hilbert space. Here we generalise quantum natural gradient to consider arbitrary quantum states (both mixed and pure) via completely positive maps; thus our circuits can incorporate both imperfect unitary gates and fundamentally non-unitary operations such as measurements. We employ the quantum Fisher information (QFI) as the core metric in the space of density operators. A modification of the Error Suppression by Derangements (ESD) and Virtual Distillation (VD) techniques enables an accurate and experimentally-efficient approximation of the QFI via the Hilbert-Schmidt metric tensor using prior results on the dominant eigenvector of noisy quantum states. Our rigorous proof also establishes the fundamental observation that the geometry of typical noisy quantum states is (approximately) identical in either the Hilbert-Schmidt metric or as characterised by the QFI. In numerical simulations of noisy quantum circuits we demonstrate the practicality of our approach and confirm it can significantly outperform other variational techniques.

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Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Variational-State Quantum Metrology

    quant-ph 2019-08 conditional novelty 8.0 of 10

    A variational algorithm finds non-symmetric quantum probe states that significantly outperform conventional symmetric states for noisy quantum metrology on up to 9 qubits.

  2. Symmetry Constraints Regularize Neural Quantum State Learning

    quant-ph 2026-08 conditional novelty 6.0 of 10

    Hard-coding translational, reflection, and bit-flip symmetries into Boltzmann-style neural quantum states cuts parameters from thousands to tens and speeds up training while preserving ground-state accuracy, with new ...

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