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Hardware-efficient ansatz without barren plateaus in any depth

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arxiv 2403.04844 v1 pith:IXLVNLMC submitted 2024-03-07 quant-ph cond-mat.stat-mech

classification quant-phcond-mat.stat-mech
keywords barrenlocalplateausquantumcircuitsansatzcircuitcondition
verification ladder T0 review T1 audit T2 compute T3 formal
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Variational quantum circuits have recently gained much interest due to their relevance in real-world applications, such as combinatorial optimizations, quantum simulations, and modeling a probability distribution. Despite their huge potential, the practical usefulness of those circuits beyond tens of qubits is largely questioned. One of the major problems is the so-called barren plateaus phenomenon. Quantum circuits with a random structure often have a flat cost-function landscape and thus cannot be trained efficiently. In this paper, we propose two novel parameter conditions in which the hardware-efficient ansatz (HEA) is free from barren plateaus for arbitrary circuit depths. In the first condition, the HEA approximates to a time-evolution operator generated by a local Hamiltonian. Utilizing a recent result by [Park and Killoran, Quantum 8, 1239 (2024)], we prove a constant lower bound of gradient magnitudes in any depth both for local and global observables. On the other hand, the HEA is within the many-body localized (MBL) phase in the second parameter condition. We argue that the HEA in this phase has a large gradient component for a local observable using a phenomenological model for the MBL system. By initializing the parameters of the HEA using these conditions, we show that our findings offer better overall performance in solving many-body Hamiltonians. Our results indicate that barren plateaus are not an issue when initial parameters are smartly chosen, and other factors, such as local minima or the expressivity of the circuit, are more crucial.

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Cited by 5 Pith papers

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

  1. Challenges in Barren Plateau Mitigation with Dynamic Parameterized Quantum Circuits

    quant-ph 2026-06 unverdicted novelty 6.0 of 10

    Dynamic parameterized quantum circuits still leave a significant fraction of parameters untrainable despite cost anti-concentration, implying BP mitigation via DPQCs is at least as hard as designing BP-free unitaries.

  2. Exploiting biased noise in variational quantum models

    quant-ph 2025-10 conditional novelty 6.0 of 10

    Twirling amplitude-damping noise into uniform Pauli/depolarising channels reduces expressivity and gradient magnitudes, while preserving the noise bias yields better VQA optimisation in the studied models.

  3. Pitfalls when tackling the exponential concentration of parameterized quantum models

    quant-ph 2025-07 conditional novelty 6.0 of 10

    Exponentially concentrated measurement outcomes are statistically indistinguishable from fixed noise after polynomial shots, so classical post-processing cannot fix them, and common proposed remedies do not escape this.

  4. A unifying account of warm start guarantees for patches of quantum landscapes

    quant-ph 2025-02 accept novelty 6.0 of 10

    A new theorem shows that a patch of parameter space around any point with non-exponentially small curvature retains polynomially large loss variance, unifying and extending prior warm-start results for variational qua...

  5. Demonstration of Efficient Predictive Surrogates for Large-scale Quantum Processors

    quant-ph 2025-07 conditional novelty 5.0 of 10

    Classical surrogates using truncated trigonometric expansions emulate noisy quantum processors and cut measurement overhead in VQE pre-training and Floquet phase identification.

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