REVIEW 12 cited by
Barren Plateaus in Variational Quantum Computing
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 computing offers a flexible computational paradigm with applications in diverse areas. However, a key obstacle to realizing their potential is the Barren Plateau (BP) phenomenon. When a model exhibits a BP, its parameter optimization landscape becomes exponentially flat and featureless as the problem size increases. Importantly, all the moving pieces of an algorithm -- choices of ansatz, initial state, observable, loss function and hardware noise -- can lead to BPs when ill-suited. Due to the significant impact of BPs on trainability, researchers have dedicated considerable effort to develop theoretical and heuristic methods to understand and mitigate their effects. As a result, the study of BPs has become a thriving area of research, influencing and cross-fertilizing other fields such as quantum optimal control, tensor networks, and learning theory. This article provides a comprehensive review of the current understanding of the BP phenomenon.
Forward citations
Cited by 12 Pith papers
-
DAGAF: A directed acyclic generative adversarial framework for joint structure learning and tabular data synthesis
A data-agnostic circuit harmonic matrix C factorises Fourier-coefficient statistics and quantum neural tangent kernels for a broad class of re-uploading parametrised quantum circuits.
-
Long Range Frequency Tuning for QML
Ternary grid initialization overcomes spectral gap suppression in trainable-frequency quantum circuits, achieving high accuracy on high-frequency synthetic benchmarks and real datasets where standard unary initializat...
-
Diagnosing quantum reservoirs at scale based on expressivity and coverage
Scalable ORS expressivity (top-K output probabilities vs Haar) plus effective feature rank jointly diagnose quantum-reservoir quality independent of Hilbert dimension and under hardware noise.
-
Scaling Quantum Machine Learning without Tricks: Full-Resolution and Diverse Image Generation
A single end-to-end quantum generator using an image-tailored circuit and learnable multimodal noise achieves state-of-the-art simulated FID scores on full MNIST and Fashion-MNIST without tricks.
-
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.
-
Robust quantum reservoir computers for forecasting chaotic dynamics: generalized synchronization and stability
Recurrence-free quantum reservoir computers have a constant, contractive Jacobian, which guarantees the echo state property and enables accurate inference of Lyapunov spectra and attractor dimensions.
-
Intelligence-Guided Adaptive Purification for DDoS-Resilient Quantum Networks: A CUDA-Q based Study
IDS-driven adaptive purification in a simulated 8-node quantum repeater chain restores fidelity-qualified entanglement delivery under SSDP-induced degradation (0.098 to 0.344 above-target; oracle 0.335).
-
From Qubits to Couplings: A Hybrid Quantum Machine Learning Framework for LHC Physics
A hybrid quantum-classical classifier on simulated HH→bbγγ events claims 95% CL limits of 1.9–2.1×SM, but the gain over XGBoost is 21–29%, not the advertised factor of two.
-
Multi-QIDA method for VQE state preparation in molecular systems
Multi-QIDA, a layered ansatz built from quantum mutual information of classical RCISD wavefunctions, outperforms the ladder hardware-efficient ansatz at matched CNOT count on five small molecular systems in noiseless ...
-
CleanQRL: Lightweight Single-file Implementations of Quantum Reinforcement Learning Algorithms
The paper introduces CleanQRL, a collection of single-file implementations of quantum reinforcement learning algorithms designed to make QRL research easier to replicate and compare.
-
Solving the compute crisis with physics-based ASICs
A coalition of academic and industry researchers argues that chips exploiting natural physical dynamics, rather than enforcing digital abstractions, could dramatically cut AI computing costs.
-
Deep Learning in Classical and Quantum Physics
A graduate-level lecture-note review of deep learning methods and their applications in classical and quantum physics, with hands-on examples.
Discussion (0). Sign in to comment.