REVIEW 10 cited by
Dynamic parameterized quantum circuits: expressive and barren-plateau free
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
Dynamic parameterized quantum circuits: expressive and barren-plateau free
read the original abstract
Classical optimization of parameterized quantum circuits is a widely studied methodology for the preparation of complex quantum states, as well as the solution of machine learning and optimization problems. However, it is well known that many proposed parameterized quantum circuit architectures suffer from drawbacks which limit their utility, such as their classical simulability or the hardness of optimization due to a problem known as "barren plateaus". We propose and study a class of dynamic parameterized quantum circuit architectures. These are parameterized circuits containing intermediate measurements and feedforward operations. In particular, we show that these architectures: 1. Provably do not suffer from barren plateaus. 2. Are expressive enough to describe arbitrarily deep unitary quantum circuits. 3. Are competitive with state of the art methods for preparing ground states and facilitating the representation of nontrivial thermal states. These features make the proposed architectures promising candidates for a variety of applications.
Forward citations
Cited by 10 Pith papers
-
Rigorous error bounds for dissipative thermal state preparation from weak system-bath coupling
The unitary contribution from weak system-bath coupling in collision-model thermal state preparation tightens the fixed-point error bound, scaling rigorously as J² where J is the coupling strength.
-
Characterizing and Benchmarking Dynamic Quantum Circuits
Dynamarq is a new scalable benchmarking framework that defines structural features for dynamic quantum circuits and uses statistical models to predict hardware fidelity with transferable parameters.
-
Challenges in Barren Plateau Mitigation with Dynamic Parameterized Quantum Circuits
Adding non-unitary gadgets to variational quantum circuits can create an illusion of trainability — the cost varies, but most parameters remain exponentially hard to train.
-
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.
-
Separation of Statistical Complexity and Trainability in Variational Quantum Circuits
Statistical complexity measures reach random-like behavior in finite-depth variational circuits before trainability degrades due to locality effects in studied models.
-
Quantum Tilted Loss in Variational Optimization: Theory and Applications
QTL unifies expectation-value minimization with CVaR and Gibbs heuristics under one tunable operator, amplifying gradients in structured cases while preserving global minima and shifting the bottleneck to measurement ...
-
QMC-Net: Data-Aware Quantum Representations for Remote Sensing Image Classification
QMC-Net maps per-band statistics to customized quantum circuit hyperparameters and achieves 93.80% and 99.34% accuracy on EuroSAT and SAT-6, outperforming classical and monolithic quantum baselines.
-
Learning Encodings by Maximizing State Distinguishability: Variational Quantum Error Correction
VarQEC uses a distinguishability loss as a machine-learning objective to variationally discover resource-efficient encoding circuits optimized for given noise models.
-
Efficient Quantum Gibbs Sampling with Local Circuits
Introduces local-circuit approximations to quasilocal dissipative processes for efficient, provably convergent quantum Gibbs sampling at high temperatures.
-
Optimal quantum reservoir learning in proximity to universality
A tunable mixing parameter p in random quantum circuits controls the transition from classically simulable to expressive quantum reservoir dynamics via entanglement and nonstabilizer content.
discussion (0)
Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.