REVIEW 5 cited by
Hardware-efficient ansatz without barren plateaus in any depth
Not yet reviewed by Pith; the record is open.
This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.
SPECIMEN: schema-true, not a live event
T0 review · schema-true
One-sentence machine reading of the paper's core claim.
pith:XXXXXXXX · record.json · timestamp
read the original abstract
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.
Forward citations
Cited by 5 Pith papers
-
Challenges in Barren Plateau Mitigation with Dynamic Parameterized Quantum Circuits
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.
-
Exploiting biased noise in variational quantum models
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.
-
Pitfalls when tackling the exponential concentration of parameterized quantum models
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.
-
A unifying account of warm start guarantees for patches of quantum landscapes
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...
-
Demonstration of Efficient Predictive Surrogates for Large-scale Quantum Processors
Classical surrogates using truncated trigonometric expansions emulate noisy quantum processors and cut measurement overhead in VQE pre-training and Floquet phase identification.
Discussion (0). Continue with ORCID to comment.