arXiv subjects

Shikhar Singh

Publications and source records attributed to Shikhar Singh.

10 recordsLinked to original sources

arXiv2026

Controlled Parity of Cooper Pair Tunneling in a Hybrid Superconducting Qubit

Superconducting quantum circuits derive their nonlinearity from the Josephson energy-phase relation. Besides the fundamental $\cosϕ$ term, this relation can also contain higher Fourier harmonics $\cos(kϕ)$ corresponding to correlated tunneling of $k$ Cooper pairs. The parity of the dominant tunneling process, i.e.~whether an odd or even number of Cooper pairs tunnel, results in qualitatively different properties, and controlling this opens up a wide range of applications in superconducting technology. However, access to even-dominated regimes has remained challenging and has so far relied on complex multi-junction or all-hybrid architectures. Here, we demonstrate a simple "harmonic parity qubit" (HPQ); an element that combines two aluminum-oxide tunnel junctions in parallel to a gate-tunable InAs/Al nanowire junction forming a SQUID, and use spectroscopy versus flux to reconstruct its energy-phase relation at 85 gate voltage points. At half flux quantum, the odd harmonics of the Josephson potential can be suppressed by up to two orders of magnitude relative to the even harmonics, producing a double-well potential dominated by even harmonics with minima near $\pmπ/2$. The ability to control harmonic parity enables supercurrent carried by pairs of Cooper pairs and provides a new building block for Fourier engineering in superconducting circuits.

quant-ph↗

arXiv2026

Observation of coherent flux-charge interaction in a gate-tunable fluxonium

Interactions that mix conjugate variables, such as the flux through a circuit element and the charge across it, lie outside the reach of the elementary couplings of superconducting circuits. Capacitors connect charge to charge, and inductors connect flux to flux, while no two-terminal element couples flux to charge directly. A native flux-charge coupling would thus serve as a circuit primitive in its own right, opening direct routes to non-reciprocity, protected modes, and unconventional readout. In this work, we demonstrate a flux-charge coupling by harnessing a voltage-tunable Josephson junction with parametrically modulated critical current, which mediates the interaction between a classical charge variable and a quantum flux operator. Relying on parity-selection rules in a hybrid superconducting-semiconductor fluxonium, we isolate the flux-charge coupling from other parasitic capacitive contributions and perform cross-quadrature-activated coherent control of states. Critically, we realize a flux-charge coupling that scales linearly with driving amplitude while keeping the transition energy first-order-insensitive to gate voltage. Such unconventional interaction broadens the toolbox of superconducting circuits with a critical missing component that enables the coherent coupling of conjugate variables.

quant-ph↗

arXiv2026

Junction-Intrinsic Dissipation in Hybrid Superconductor-Semiconductor Gatemon Qubits

Superconducting transmon qubits based on hybrid superconductor-semiconductor Josephson junctions (gatemons) offer gate tunability, but their relaxation times remain well below those of state-of-the-art transmons, and the origin of this discrepancy is not fully understood. Here, we co-fabricate gatemons and SIS-junction transmons with nominally identical circuit layouts, gate dielectrics, and control lines, so that the Josephson element is the only intentional distinction. Across multiple chips, transmons in this architecture reach relaxation times in the tens of microseconds, whereas gatemons saturate in the few-microsecond range. Using the transmons as on-chip references, we construct a loss budget including Purcell decay, spontaneous emission through the control line, and internal dielectric loss, and find that the corresponding T1 limits exceed all measured gatemon values by more than an order of magnitude. Temperature-dependent T1 measurements follow a common quasiparticle-activation model and yield similar superconducting gaps for S-Sm-S and SIS junctions, indicating that the reduced gatemon coherence is dominated by additional temperature-independent, junction-intrinsic dissipation.

quant-ph↗

arXiv2024

Gatemon Qubit Revisited for Improved Reliability and Stability

The development of quantum circuits based on hybrid superconductor-semiconductor Josephson junctions holds promise for exploring their mesoscopic physics and for building novel superconducting devices. The gate-tunable superconducting transmon qubit (gatemon) is the paradigmatic example of such a superconducting circuit. However, gatemons typically suffer from unstable and hysteretic qubit frequencies with respect to the applied gate voltage and reduced coherence times. Here we develop methods for characterizing these challenges in gatemons and deploy these methods to compare the impact of shunt capacitor designs on gatemon performance. Our results indicate a strong frequency- and design-dependent behavior of the qubit stability, hysteresis, and dephasing times. Moreover, we achieve highly reliable tuning of the qubit frequency with 1 MHz precision over a range of several GHz, along with improved stability in grounded gatemons compared to gatemons with a floating capacitor design.

cond-mat.mes-hall↗

arXiv2022

VIPHY: Probing "Visible" Physical Commonsense Knowledge

In recent years, vision-language models (VLMs) have shown remarkable performance on visual reasoning tasks (e.g. attributes, location). While such tasks measure the requisite knowledge to ground and reason over a given visual instance, they do not, however, measure the ability of VLMs to retain and generalize such knowledge. In this work, we evaluate their ability to acquire "visible" physical knowledge -- the information that is easily accessible from images of static scenes, particularly across the dimensions of object color, size and space. We build an automatic pipeline to derive a comprehensive knowledge resource for calibrating and probing these models. Our results indicate a severe gap between model and human performance across all three tasks. Furthermore, our caption pretrained baseline (CapBERT) significantly outperforms VLMs on both size and spatial tasks -- highlighting that despite sufficient access to ground language with visual modality, they struggle to retain such knowledge. The dataset and code are available at https://github.com/Axe--/ViPhy .

cs.CL↗

arXiv2021

Using Graph Neural Networks to model the performance of Deep Neural Networks

With the unprecedented proliferation of machine learning software, there is an ever-increasing need to generate efficient code for such applications. State-of-the-art deep-learning compilers like TVM and Halide incorporate a learning-based performance model to search the space of valid implementations of a given deep learning algorithm. For a given application, the model generates a performance metric such as the run time without executing the application on hardware. Such models speed up the compilation process by obviating the need to benchmark an enormous number of candidate implementations, referred to as schedules, on hardware. Existing performance models employ feed-forward networks, recurrent networks, or decision tree ensembles to estimate the performance of different implementations of a neural network. Graphs present a natural and intuitive way to model deep-learning networks where each node represents a computational stage or operation. Incorporating the inherent graph structure of these workloads in the performance model can enable a better representation and learning of inter-stage interactions. The accuracy of a performance model has direct implications on the efficiency of the search strategy, making it a crucial component of this class of deep-learning compilers. In this work, we develop a novel performance model that adopts a graph representation. In our model, each stage of computation represents a node characterized by features that capture the operations performed by the stage. The interaction between nodes is achieved using graph convolutions. Experimental evaluation shows a 7:75x and 12x reduction in prediction error compared to the Halide and TVM models, respectively.

cs.LG↗

arXiv2021

Distributed Symbolic Execution using Test-Depth Partitioning

Symbolic execution is a classic technique for systematic bug finding, which has seen many applications in recent years but remains hard to scale. Recent work introduced ranged symbolic execution to distribute the symbolic execution task among different workers with minimal communication overhead using test inputs. However, each worker was restricted to perform only a depth-first search. This paper introduces a new approach to ranging, called test-depth partitioning, that allows the workers to employ different search strategies without compromising the completeness of the overall search. Experimental results show that the proposed approach provides a more flexible ranging solution for distributed symbolic execution. The core idea behind test-depth partitioning is to use a test-depth pair to define a region in the execution space. Such a pair represents a partial path or a prefix, and it obviates the need for complete tests to determine boundaries as was the case in the previous ranging scheme. Moreover, different workers have the freedom to select the search strategy of choice without affecting the functioning of the overall system. Test-depth partitioning is implemented using KLEE, a well-known symbolic execution tool. The preliminary results show that the proposed scheme can prove to be an efficient tool to speed up symbolic execution.

cs.SE↗

arXiv2021

COM2SENSE: A Commonsense Reasoning Benchmark with Complementary Sentences

Commonsense reasoning is intuitive for humans but has been a long-term challenge for artificial intelligence (AI). Recent advancements in pretrained language models have shown promising results on several commonsense benchmark datasets. However, the reliability and comprehensiveness of these benchmarks towards assessing model's commonsense reasoning ability remains unclear. To this end, we introduce a new commonsense reasoning benchmark dataset comprising natural language true/false statements, with each sample paired with its complementary counterpart, resulting in 4k sentence pairs. We propose a pairwise accuracy metric to reliably measure an agent's ability to perform commonsense reasoning over a given situation. The dataset is crowdsourced and enhanced with an adversarial model-in-the-loop setup to incentivize challenging samples. To facilitate a systematic analysis of commonsense capabilities, we design our dataset along the dimensions of knowledge domains, reasoning scenarios and numeracy. Experimental results demonstrate that our strongest baseline (UnifiedQA-3B), after fine-tuning, achieves ~71% standard accuracy and ~51% pairwise accuracy, well below human performance (~95% for both metrics). The dataset is available at https://github.com/PlusLabNLP/Com2Sense.

cs.CL↗

arXiv2021

EventPlus: A Temporal Event Understanding Pipeline

We present EventPlus, a temporal event understanding pipeline that integrates various state-of-the-art event understanding components including event trigger and type detection, event argument detection, event duration and temporal relation extraction. Event information, especially event temporal knowledge, is a type of common sense knowledge that helps people understand how stories evolve and provides predictive hints for future events. EventPlus as the first comprehensive temporal event understanding pipeline provides a convenient tool for users to quickly obtain annotations about events and their temporal information for any user-provided document. Furthermore, we show EventPlus can be easily adapted to other domains (e.g., biomedical domain). We make EventPlus publicly available to facilitate event-related information extraction and downstream applications.

cs.CL↗

arXiv2020

MELINDA: A Multimodal Dataset for Biomedical Experiment Method Classification

We introduce a new dataset, MELINDA, for Multimodal biomEdicaL experImeNt methoD clAssification. The dataset is collected in a fully automated distant supervision manner, where the labels are obtained from an existing curated database, and the actual contents are extracted from papers associated with each of the records in the database. We benchmark various state-of-the-art NLP and computer vision models, including unimodal models which only take either caption texts or images as inputs, and multimodal models. Extensive experiments and analysis show that multimodal models, despite outperforming unimodal ones, still need improvements especially on a less-supervised way of grounding visual concepts with languages, and better transferability to low resource domains. We release our dataset and the benchmarks to facilitate future research in multimodal learning, especially to motivate targeted improvements for applications in scientific domains.

cs.CL↗