arXiv subjects

Junwoo Jung

Publications and source records attributed to Junwoo Jung.

6 recordsLinked to original sources

arXiv2026

Decay versus dephasing in Rydberg analog optimization: exchange rate, mechanism, and schedule design

Numerical studies of noisy Rydberg-atom optimization almost universally compress decoherence into a single scalar, silently pricing spontaneous decay ($Γ$) and dephasing ($γ$) alike. We treat the two as independent axes, mapping a quantum-annealing heuristic for unit-disk maximum independent set on 20 random $N=10$ graphs across the $(Γ,γ)$ plane, with the annealing time re-optimized at every point. The mean approximation ratio does collapse onto one scalar, but onto $u=κΓ+γ$ with $κ=8.05\pm0.5$ (stat) $\pm1.1$ (syst); the isotropic $Γ+γ$ fails by a factor of 30 in residual. First-order perturbation theory reproduces $κ$ from noiseless propagation alone and gives the mechanism: the objective is diagonal in the basis of the dephasing operator, so dephasing cannot change the answer once the drive is off, and a single driven atom already has $κ\simeq8.5$. The exchange rate is thus a property of the protocol as much as of the platform: the ramp-down fraction moves it between 2.3 and 16.3. At a fixed schedule it is stable across system sizes, interaction strengths, and estimators. Because the whole cost model is noiseless, a schedule can be tuned to a device's channel mixture without any noisy simulation: jointly tuning the drive ramp-down and the sweep's detuning ramp recovers about a third of the Markovian damage at no hardware cost, and the ramp the noise-aware objective selects is not the one noiseless optimization would choose. Per unit rate decay is eightfold the dearer channel, but the measured $T_1$ enters weighted by its branching ratio to the ground state ($b\approx0.4$ for the calibrated device), which leaves the two Lindblad channels comparably costly at a present-day operating point. One-parameter noise models remain serviceable, provided the parameter is $u$.

quant-ph↗

arXiv2026

Shots-to-Approximate-Solution Scaling in Neutral-Atom Quantum Optimization

Whether neutral-atom quantum optimization protocols exhibit genuine concentration toward low-energy solution structure remains an open question. Here, we introduce a shots-to-approximate-solution metric, STS(r), where r denotes the approximation ratio, and evaluate it using postprocessed outputs modeled by a degeneracy-weighted shell distribution governed by a single effective parameter, $β$, that quantifies concentration toward near-optimal independent sets. To extract the genuine concentration effect in the quantum data, we apply identical postprocessing to both experimental bitstrings and randomly generated bitstrings with matched excitation density, thereby constructing an excitation-matched random baseline. Experiments on programmable Rydberg-atom arrays with system sizes up to 125 sites show that quantum annealing consistently exceeds the random baseline, demonstrating enhanced concentration toward low-energy solution structure beyond what can be attributed solely to excitation density. The results further reveal two distinct target-dependent regimes. For near-exact targets with $r \approx 1$, the required shot count grows exponentially with system size and is reduced at the same exponential level by quantum annealing within the shell-model description. By contrast, for relaxed targets, the shot cost becomes effectively constant, and the corresponding quantum enhancement diminishes, with the classical postprocessing heuristic alone reaching the target in order-unity attempts. Together, these results establish an operational method for quantifying quantum optimization performance and clarify the regimes under which quantum approaches can yield practical benefits.

quant-ph↗

arXiv2026

The Identification of Asymmetric Barred Galaxies in Illustris TNG-50

Most barred galaxies exhibit symmetric structures. However, recent studies have shown that a subset of barred galaxies exhibit lopsided morphologies. To quantify their occurrence and investigate their physical origins, we analyze barred galaxies in the IllustrisTNG TNG50 simulation. We select 519 clearly barred galaxies in their stellar mass maps out of 770 barred galaxies from the TNG50-1 catalog. We classify the bar morphologies into four subgroups - `Lopsided', `Perturbed', `Symmetric', and `Indeterminate' - and perform a comparative analysis of their physical properties. We find that galaxies hosting asymmetric bars (`Lopsided' and `Perturbed') tend to have higher gas densities around the bar region, enhanced star formation activity, and more recent bar-formation epochs than galaxies with symmetric bars. However, the factor that most consistently distinguishes the four subgroups is the stellar mass distribution of the host galaxy, and there appears to be no physical correlation with bar size. These findings suggest that asymmetric bars form preferentially in less massive galaxies and may evolve into symmetric bars over time through secular processes. However, this conclusion should be considered with caution, as the fraction of asymmetric bars in the TNG50 simulation is systematically higher than that observed in the local universe.

astro-ph.GA↗

arXiv2026

AlphaFree: Recommendation Free from Users, IDs, and GNNs

Can we design effective recommender systems free from users, IDs, and GNNs? Recommender systems are central to personalized content delivery across domains, with top-K item recommendation being a fundamental task to retrieve the most relevant items from historical interactions. Existing methods rely on entrenched design conventions, often adopted without reconsideration, such as storing per-user embeddings (user-dependent), initializing features from raw IDs (ID-dependent), and employing graph neural networks (GNN-dependent). These dependencies incur several limitations, including high memory costs, cold-start and over-smoothing issues, and poor generalization to unseen interactions. In this work, we propose AlphaFree, a novel recommendation method free from users, IDs, and GNNs. Our main ideas are to infer preferences on-the-fly without user embeddings (user-free), replace raw IDs with language representations (LRs) from pre-trained language models (ID-free), and capture collaborative signals through augmentation with similar items and contrastive learning, without GNNs (GNN-free). Extensive experiments on various real-world datasets show that AlphaFree consistently outperforms its competitors, achieving up to around 40% improvements over non-LR-based methods and up to 5.7% improvements over LR-based methods, while significantly reducing GPU memory usage by up to 69% under high-dimensional LRs.

cs.IR↗

arXiv2026

Quantum-Enhanced Deterministic Inference of $k$-Independent Set Instances on Neutral Atom Arrays

Noisy quantum annealing experiments on Rydberg atom arrays produce measurement outcomes that deviate from ideal distributions, complicating performance evaluation. To enable a data-driven benchmarking methodology for quantum devices that accounts for both solution quality and the classical computational cost of inference from noisy measurements, we introduce deterministic error mitigation (DEM), a shot-level inference procedure informed by experimentally characterized noise. We demonstrate this approach using the decision version of the $k$-independent set problem. Within a Hamming-shell framework, the DEM candidate volume is governed by the binary entropy of the bit-flip error rate, yielding an entropy-controlled classical postprocessing cost. Using experimental measurement data, DEM reduces postprocessing overhead relative to classical inference baselines. Numerical simulations and experimental results from neutral atom devices validate the predicted scaling with system size and error rate. These scalings indicate that one hour of classical computation on an Intel i9 processor corresponds to neutral atom experiments with up to $N=250-450$ atoms at effective error rates, enabling a direct, cost-based comparison between noisy quantum experiments and classical algorithms.

quant-ph↗

arXiv2023

Rydberg-atom graphs for quadratic unconstrained binary optimization problems

There is a growing interest in harnessing the potential of the Rydberg-atom system to address complex combinatorial optimization challenges. Here we present an experimental demonstration of how the quadratic unconstrained binary optimization (QUBO) problem can be effectively addressed using Rydberg-atom graphs. The Rydberg-atom graphs are configurations of neutral atoms organized into mathematical graphs, facilitated by programmable optical tweezers, and designed to exhibit many-body ground states that correspond to the maximum independent set (MIS) of their respective graphs. We have developed four elementary Rydberg-atom subgraph components, not only to eliminate the need of local control but also to be robust against interatomic distance errors, while serving as the building blocks sufficient for formulating generic QUBO graphs. To validate the feasibility of our approach, we have conducted a series of Rydberg-atom experiments selected to demonstrate proof-of-concept operations of these building blocks. These experiments illustrate how these components can be used to programmatically encode the QUBO problems to Rydberg-atom graphs and, by measuring their many-body ground states, how their QUBO solutions are determined subsequently.

quant-ph↗