arXiv subjects

Yash Vardhan Tomar

Publications and source records attributed to Yash Vardhan Tomar.

5 recordsLinked to original sources

arXiv2026

Placing Degree Scales After LayerNorm

Graph neural networks (GNNs) are widely used to learn node-selection policies on graphs, and most stack graph attention (GAT) blocks with LayerNorm. On degree-sensitive tasks, LayerNorm tends to remove the degree signal these models need to rank nodes. Much recent work addresses this by redesigning normalizers or aggregators, which changes what these components compute but does not ask where, relative to LayerNorm, a degree scale should be applied. In this paper, we show that the answer follows from a single algebraic fact about LayerNorm. When a positive per-node scale is applied before LayerNorm, LayerNorm divides it out, and it never reaches the model's output. Applied after LayerNorm, the same scale comes through and reaches the score head as magnitude. From this placement rule we derive PostDeg, a parameter-free inverse-degree scale that we add as the single change to a fixed GAT backbone. PostDeg multiplies each node's normalized representation by an inverse function of its degree, and we compare it against controls in the same position. At every evaluation size, PostDeg improves over the LayerNorm backbone on influence maximization, network dismantling, and maximum independent set, and these controls show where the improvement comes from. The same scale before LayerNorm stays at the backbone, as the absorption identity predicts, and a constant scale after LayerNorm stays there too on all but the most heavy-tailed graphs, so the effect needs both the position after LayerNorm and a degree-dependent scale. The exact form of the scale matters much less, so we recommend PostDeg, which needs no tuning.

cs.LG↗

arXiv2026

What Must a Fairness Audit Report When Demographic Data Is Incomplete?

Fairness audits are a key component of responsible machine-learning deployment. Yet what such an audit must disclose, when the protected labels it depends on are incomplete, remains unsettled. In this work, we focused on the rates a fairness audit publishes and on what an oversight reader needs beside them. We paired every published rate with a matched baseline drawn from the same audit, one hiding protected labels and one varying only the run seed. Across ACS/Folktables tasks, missingness settings that kept some protected labels moved the selected mitigation less than an ordinary rerun did. At zero protected-label access, candidates collapsed to empirical risk minimization, so the apparent exception there reflected the candidate set's composition. Equalized-odds threshold optimization most often regressed an intersectional subgroup, but that rate fell back to its baseline once we kept only the configurations an auditor would accept. Any accuracy it lost fell on the population as heavily as on the worst-off cell, so the mechanism is levelling down. The one effect that survived was a change in which cell is worst-off. Overall, our results highlight that a published audit rate should be reported with the baseline needed to interpret it, the candidate set it came from, and its intersectional effects, before it is treated as evidence about a deployed model.

cs.LG↗

arXiv2026

SymQNet: Amortized Acquisition for Low-Latency Adaptive Hamiltonian Learning

Adaptive Hamiltonian learning is central to calibrating and characterizing quantum devices. In an adaptive controller, choosing the next experiment is itself a computation. Bayesian design rules are recomputed after every posterior update, and that step can take seconds. Across hundreds of shots, those seconds become a significant wall-clock cost for adaptivity. We introduce SymQNet, an amortized reinforcement-learning approach for low-latency adaptive Hamiltonian learning. SymQNet learns a posterior-conditioned acquisition policy offline, then uses a fast policy forward pass online while retaining Bayesian posterior feedback. On transverse-field Ising benchmarks, SymQNet substantially reduces acquisition latency relative to bounded Fisher-information search and bounded two-step Bayesian active learning by disagreement (BALD). At five qubits, it reduces acquisition-only decision latency by 47.1x and 72.6x relative to these online baselines; at twelve qubits, full simulated steps take 1.02 seconds for SymQNet versus 13.27 seconds for bounded two-step BALD. Overall, we show that learned acquisition can make adaptive Hamiltonian learning practical for repeated low-latency workloads.

cs.LG↗

arXiv2026

Graph Reinforcement Learning for Calibration-Aware Quantum Circuit Routing

Quantum circuit routing is a key step in compiling programs for noisy intermediate-scale quantum processors, particularly superconducting devices whose sparse fixed coupling makes routing a central compilation cost. Routes that appear efficient by standard overhead metrics such as SWAP count, routed two-qubit count, and depth can still lose fidelity when they pass through poorly calibrated couplers. We study a calibration-aware graph reinforcement-learning router that uses same-day calibration data from superconducting IBM Heron r2 processors to choose hardware-edge SWAPs. We train the policy with proximal policy optimization and evaluate it with exact simulated fidelity across nine Munich Quantum Toolkit (MQT) Bench circuits and three calibration snapshots. Across these evaluations, pooled mean exact fidelity is 0.727, compared with 0.440 for SWAP-based bidirectional heuristic search (SABRE)-best20 and 0.481 for target-aware SABRE. We observe that fidelity gains come with higher routed two-qubit counts and are concentrated in 5 qubit and 8 qubit circuit families; under the fixed tree action graph, all 10 qubit families favor SABRE-best20. Overall, our results show that calibration-aware learned routing can improve fidelity beyond gate-count-driven compilation, by roughly 0.25 to 0.29 in absolute mean fidelity over the SABRE-family baselines.

quant-ph↗

arXiv2026

How Should a Simulation-to-Reality Transfer Budget Be Spent?

Simulation-to-reality transfer, often called sim-to-real transfer, is a central challenge in robot learning. Yet, the tradeoff between measuring a system more accurately and training over a broader range of simulated dynamics is still poorly understood. In this work, we focused on the allocation of real-robot measurement time between system identification and domain randomization. We studied this tradeoff in a controlled sim-to-sim pendulum setting, where a hidden-parameter model stands in for the physical robot, and the experiment sweeps identification rollouts against the width of the randomization distribution. Across the reality gaps and noise levels we tested, the measurement budget did most of the work. A small number of identification rollouts closed most of the transfer gap, and once any real data was available, policies performed best when trained at the estimated parameters rather than over a widened randomization band. Broad randomization that contained the true system still did not substitute for measurement. These results hold in a benign regime where the dynamics are identifiable and only two parameters are unknown, so structural model mismatch remains the setting where randomization breadth may become more valuable. Overall, our results suggest that sim-to-real pipelines should first measure the parameters they can and reserve randomization for the uncertainty that remains.

cs.RO↗