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.
Title resolution pending
9 Pith papers cite this work, alongside 3 external citations. Polarity classification is still indexing.
9
Pith papers citing it
3
external citations · external index
citation-role summary
background 1
citation-polarity summary
fields
quant-ph 9roles
background 1polarities
background 1representative citing papers
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.
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.
Statistical complexity measures reach random-like behavior in finite-depth variational circuits before trainability degrades due to locality effects in studied models.
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 variance.
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.
VarQEC uses a distinguishability loss as a machine-learning objective to variationally discover resource-efficient encoding circuits optimized for given noise models.
Introduces local-circuit approximations to quasilocal dissipative processes for efficient, provably convergent quantum Gibbs sampling at high temperatures.
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.
citing papers explorer
-
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.
-
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 variance.
-
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.