REVIEW 39 cited by
Does provable absence of barren plateaus imply classical simulability?
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
Signed reviews
read the original abstract
A large amount of effort has recently been put into understanding the barren plateau phenomenon. In this perspective article, we face the increasingly loud elephant in the room and ask a question that has been hinted at by many but not explicitly addressed: Can the structure that allows one to avoid barren plateaus also be leveraged to efficiently simulate the loss classically? We collect evidence-on a case-by-case basis-that many commonly used models whose loss landscapes avoid barren plateaus can also admit classical simulation, provided that one can collect some classical data from quantum devices during an initial data acquisition phase. This follows from the observation that barren plateaus result from a curse of dimensionality, and that current approaches for solving them end up encoding the problem into some small, classically simulable, subspaces. Thus, while stressing that quantum computers can be essential for collecting data, our analysis sheds doubt on the information processing capabilities of many parametrized quantum circuits with provably barren plateau-free landscapes. We end by discussing the (many) caveats in our arguments including the limitations of average case arguments, the role of smart initializations, models that fall outside our assumptions, the potential for provably superpolynomial advantages and the possibility that, once larger devices become available, parametrized quantum circuits could heuristically outperform our analytic expectations.
Forward citations
Cited by 39 Pith papers
-
A hardware-efficient variational ansatz with an exact diagonal metric for real- and imaginary-time evolution and Haar sampling
A hardware-efficient binary-tree ansatz has a closed-form diagonal Fubini–Study metric, enabling metric-aware VQE and time evolution without auxiliary circuits, with linear-in-k pruning for sparse sectors.
-
Loss Behavior in Supervised Learning with Entangled States
Using maximally entangled training data exponentially flattens the loss landscape of highly expressive quantum models, limiting the loss improvement achievable in a fixed-size neighborhood.
-
Trainability of Parametrised Linear Combinations of Unitaries
Sums of trainable parametrised circuits remain trainable, with explicit variance formulas for LCU states under Haar-random assumptions.
-
Machine learning for sample-based quantum diagonalization: generative configuration recovery and the classical-simulability frontier
A critical review plus small exact-FCI experiments concludes that sample-based quantum diagonalization has not beaten classical selected CI and maps where, if anywhere, a quantum or generative advantage could survive.
-
Stacking the Deck: Tunable Trainability in Stacked LCUs
Stacked LCUs of fermionic Gaussian unitaries give variance Ω(1/(n k^{3l})) against classical simulation O(k^{2l} n^3) and quantum gate count O(l k n^2), with layers l as the single dial.
-
Concentration-Free Quantum Kernel Learning in the Rydberg Blockade
A Rydberg blockade based quantum kernel is claimed to avoid exponential concentration while remaining classically hard to simulate.
-
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.
-
LCQNN: Linear Combination of Quantum Neural Networks
LCQNN combines several trainable unitaries through a learned superposition on control qubits, yielding gradient variance bounds that scale polynomially with local system size rather than exponentially with total qubit count.
-
Quantum Recurrent Embedding Neural Network
A quantum recurrent embedding neural network is proven to avoid barren plateaus via a dynamical Lie algebra decomposition, with applications to Hamiltonian and topological phase classification.
-
State-space gradient descent and metastability in quantum systems
A state-space gradient descent algorithm with ancilla-based non-unitary updates prepares ground or metastable states of quantum Hamiltonians and is claimed to avoid barren plateaus.
-
Regularizing quantum loss landscapes by noise injection
Noise injection into each parameterized Pauli gate exponentially suppresses high-frequency Fourier components of a quantum loss function, smoothing the landscape and improving optimization quality in numerical tests.
-
Statistical models of barren plateaus and anti-concentration of Pauli observables
In statistical models of barren plateaus, any two Pauli observables have non-zero regions whose overlap is exponentially smaller than each region, a phenomenon the paper calls anti-concentration.
-
Offline recovery of magic and entanglement from noisy Pauli product states
Classical purification of noisy Pauli-product states recovers magic and entanglement, with a noise floor that depends on when those resources are generated and on which circuit state is chosen.
-
Variational optical phase learning on a continuous-variable quantum compiler
An experimental continuous-variable quantum compiler learns an optical phase with two-mode squeezed light, and increasing the squeezing sharpens the cost landscape, improving precision and training speed.
-
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...
-
Advantages of density in tensor network geometries for gradient based training
Densely connected tensor network geometries train to lower infidelity than sparse ones on random quantum states, and a new leaf-contraction trick reduces memory while improving training.
-
Opportunities and limitations of explaining quantum machine learning
The paper introduces two new explanation methods for quantum machine learning models (Taylor-∞ and QLRP) and reviews the field.
-
Polynomially efficient quantum enabled variational Monte Carlo for training neural-network quantum states for physico-chemical applications
A variational Monte Carlo algorithm that trains a restricted Boltzmann machine quantum state by sampling a fitted Ising surrogate with a Trotterized quantum circuit, demonstrated on small spin and molecular systems.
-
Limitations of Quantum Approximate Optimization in Solving Generic Higher-Order Constraint-Satisfaction Problems
Numerical simulations of random Max-kXOR with k=3 to 10 show the classical MF-AOA benchmark matches or outperforms the QAOA on average, and reaching high approximation ratios would require very large circuit depths.
-
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.
-
Thermalization from quantum entanglement: jet simulations in the massive Schwinger model
In the massive Schwinger model with back-to-back external sources, the central region approaches a thermal state at late times, with temperature estimates from local observables, entanglement entropy, and density-matr...
-
Out of Tune: Demystifying Noise-Effects on Quantum Fourier Models
Noise, especially decoherent gate errors, systematically reduces Fourier coefficient magnitudes, expressibility, and entangling capability of quantum Fourier models, with circuit architecture and encoding modulating t...
-
Protein folding with an all-to-all trapped-ion quantum computer
BF-DCQO on IonQ's trapped-ion processors solves dense HUBO instances (protein folding up to 33 qubits, MAX 4-SAT and spin-glasses at 36 qubits) when followed by classical post-processing.
-
TabularQGAN: A quantum generative model for tabular data synthesis
A quantum GAN with one-hot-preserving Givens rotations generates synthetic tabular data that matches real data better (SDMetrics similarity) than CTGAN and CopulaGAN on three- to four-feature subsets of two public dat...
-
Pauli Propagation: A Computational Framework for Simulating Quantum Systems
Pauli propagation, a classical method that evolves Pauli operators through quantum circuits, is presented as a unified algorithmic framework together with the Julia package PauliPropagation.jl that implements it.
-
Bayesian Quantum Orthogonal Neural Networks for Anomaly Detection
Bayesian training of orthogonal quantum neural networks improves calibration for 3D anomaly detection, and an 8-qubit hardware test shows the pipeline tolerates device noise.
-
Branch-and-bound digitized counterdiabatic quantum optimization
A branch-and-bound wrapper around digitized counterdiabatic quantum optimization finds better or equal solutions to higher-order binary problems than simulated annealing, using fewer measured energy evaluations.
-
Quantum Neural Networks for Cloud Cover Parameterizations in Climate Models
Quantum neural networks predict cloud cover as accurately as similarly sized classical neural networks on coarse-grained storm-resolving climate data, while both outperform a fitted Xu-Randall baseline.
-
Addressing the Readout Problem in Quantum Differential Equation Algorithms with Quantum Scientific Machine Learning
Quantum neural networks can classify shock and turbulent flow solutions encoded as quantum states, with accuracy strongly dependent on Fourier versus real-space basis choice.
-
Architectural Patterns for Designing Quantum Artificial Intelligence Systems
A systematic mapping study identifies ten architectural patterns, seven for the quantum-classical split and three for middleware, that describe how quantum components can be integrated into AI inference systems.
-
Quantum reinforcement learning in dynamic environments
A quantum hybrid RL agent with a dissipation mechanism outlearns a classical agent in a Gridworld with a suddenly changing reward path, for suitable dissipation values.
-
Quantum Classifiers with Trainable Kernel
A trainable quantum feature map plus a support-vector-based quantum SVM improves simulated IRIS classification accuracy and distinguishability over least-squares QSVM.
-
Opportunities and challenges of quantum computing for climate modelling
This position paper maps quantum algorithms to four climate modeling tasks and concludes that near-term QML parameterizations are promising but computationally prohibitive at scale.
-
A hybrid learning agent for episodic learning tasks with unknown target distance
An episode-length-doubling rule adapted from Boyer's quantum search lets the hybrid QRL agent find a first reward in grid mazes without knowing the target distance, and it outperforms classical agents in several wall ...
-
Perspectives on Utilization of Measurements in Quantum Algorithms
A survey that categorizes quantum measurement uses into static circuits, dynamic circuits, and challenge-solving techniques, and argues measurements deserve more attention in algorithm design.
-
How quantum computing can enhance biomarker discovery
A review argues that quantum computing, particularly quantum machine learning, could enhance biomarker discovery for small, high-dimensional, and noisy healthcare datasets.
-
An Introduction to Variational Quantum Eigensolver Applied to Chemistry
A pedagogical review of VQE for molecular ground-state chemistry, with an asymptotic complexity estimate of O(N^9/epsilon^2) for UCC/UCCG and O(k N^7/epsilon^2) for k-UpCCG.
-
Quantum Machine Learning: A Hands-on Tutorial for Machine Learning Practitioners and Researchers
A structured tutorial that introduces quantum machine learning concepts, algorithms, theory, and PennyLane code to classical ML practitioners.
-
Artificial intelligence for representing and characterizing quantum systems
A review organizes AI-based quantum system characterization into ML, deep learning, and language model paradigms, covering property prediction and implicit state reconstruction.
Discussion (0). Continue with ORCID to comment.