arXiv subjects

Avirup Chakraborty

Publications and source records attributed to Avirup Chakraborty.

7 recordsLinked to original sources

arXiv2026

Travelling Dark State Polariton as a Viable Quantum Memory in a Solid-State Medium

We theorize a quantum memory based on the dark-state polariton field, formed by the superposition of atomic and photonic states of a travelling probe laser pulse under the application of standing wave modes of a dominant control laser pulse using a lambda-level scheme Electromagnetically Induced Transparency in a solid medium. We show how an enhancement in the storage time for the pulse is achieved by eliminating pulse broadening due to diffusion. At last, we propose an experiment that can help realize the storage of a probe pulse in the hyperfine levels $^3\text{H}_4 \leftrightarrow ^1\text{D}_2$ of $\text{Pr}^{3+}:\text{Y}_2\text{SiO}_5$, cryogenically cooled at $4.5\text{ K}$. We also discuss multiple applications the storage of the quantum states the probe pulse with a prolonged time interval must have.

quant-ph↗

arXiv2026

Quantum Random Access Memory Implementation Using Photon-Photon Interaction in Rydberg Atomic Ensemble

Quantum random access memory (qRAM) is crucial for overcoming data-loading bottlenecks in quantum machine learning; however, current physical implementations face severe scalability constraints. Traditional fanout designs demand exponential decoherence-prone gates, while bucket-brigade schemes require highly error-prone active switches. Motivated by these limitations, we propose a scalable qRAM architecture that fundamentally replaces active nodes with phase-encoded quantum walkers. Our methodology maps a discrete-time quantum walk onto a cavity quantum electrodynamics framework utilizing an electromagnetically induced transparency (EIT)-based Rydberg atomic ensemble. Inside hollow-core waveguides, strong Rydberg dipole-dipole interactions and a solenoidal magnetic field create a robust routing operator. This operator imparts precise, polarization-dependent phase shifts, steering circularly polarized probe pulses to target memory cells. Our results demonstrate that operating within a strong control field regime suppresses emergent spatial attenuation, ensuring cumulative transmission probabilities for highly scaled memory addresses. Ultimately, this parallelized architecture successfully optimizes spatial resources to static gates and temporal complexity to an optimal logarithmic scale of $\mathcal{O}(n\log(n+m))$ by requiring $\mathcal{O}(n+m)$ physical walkers, establishing a practical, fault-tolerant hardware pathway for advanced quantum computation implementations.

quant-ph↗

arXiv2026

Quantum-RAM Implementation Using Multiple Interacting Rydberg-Blockaded EIT Systems

We propose a novel theoretical architecture for implementing a quantum random access memory (qRAM) based on quantum random walks in a Rydberg blockaded atomic ensemble utilizing multilevel Electromagnetically Induced Transparency (EIT). Unlike previous approaches that rely on geometric phase gates in solid-state or trapped ion systems, our scheme harnesses the strong, coherent dipole dipole interactions between Rydberg atoms to achieve high-fidelity phase control of photonic qubits without the need for cryogenic temperatures. By generating conditional phase shifts through cross-phase modulation in multiple lambda-type EIT systems, we realize the controlled unitary operations requisite for an efficient qRAM. In the proposed architecture, Zeeman splitting is used to engineer a set of parallel lambda systems in a cavity, where pairs of magnetic sublevels of the ground state are coupled to highly excited Rydberg states via circularly polarized laser pulses. These Rydberg excited EIT systems serve as the elementary phase gates that form the nodes of a binary tree enabling quantum random walking. Address and data qubits are encoded into distinct probe fields and coherently mapped into the metastable atomic states, where their interactions within the EIT medium generate conditional phases required for state-selective routing. The system uses $n+m$ layers of cold alkali atoms to form an $n$-level binary tree of Rydberg nodes connected to $2^n$ cavity-trapped memory atoms, operated by $n+m$ laser pulses acting as quantum walkers and address units. Our scheme offers a scalable, reducing operational complexity to $\mathcal{O}(n)$ and highly coherent pathway toward photonic qRAM, exploiting collective Rydberg interactions to realize programmable, parallel entangling operations in an atomic ensemble.

quant-ph↗

arXiv2026

Understanding Carbon Trade Dynamics: A European Union Emissions Trading System Perspective

The European Union Emissions Trading System (EU ETS), the world's first and largest cap-and-trade carbon market, is a cornerstone of EU climate policy. This study provides a comprehensive empirical analysis of the EU carbon market's efficiency, price dynamics, and structural network from 2010 to 2020. First, we identify significant price clustering and short-term return predictability using an AR-GARCH model, achieving around 60 percent directional accuracy and a 80 percent hit rate within forecasted confidence intervals. These observed patterns motivate a deeper exploration of market structure. Second, leveraging this insight, a weighted network analysis of inter-country transactions uncovers a concentrated market where a few registries dominate high-value flows and exert disproportionate influence. Finally, building upon the network findings, country-specific log-log regressions of price on traded quantity reveal heterogeneous and sometimes counter-intuitive elasticities; in several cases, positive elasticities exceed unity, indicating that trading volumes rise with prices, a deviation from conventional demand behavior that highlights potential inefficiencies driven by speculation, strategic behavior, or policy distortions. Collectively, these results point to persistent inefficiencies within the EU ETS, including partial predictability, asymmetric market power, and anomalous price-volume relationships, implying that while the system has driven decarbonization, its trading and pricing mechanisms remain imperfect.

stat.AP↗

arXiv2025

Quantitative Rule-Based Strategy modeling in Classic Indian Rummy: A Metric Optimization Approach

The 13-card variant of Classic Indian Rummy is a sequential game of incomplete information that requires probabilistic reasoning and combinatorial decision-making. This paper proposes a rule-based framework for strategic play, driven by a new hand-evaluation metric termed MinDist. The metric modifies the MinScore metric by quantifying the edit distance between a hand and the nearest valid configuration, thereby capturing structural proximity to completion. We design a computationally efficient algorithm derived from the MinScore algorithm, leveraging dynamic pruning and pattern caching to exactly calculate this metric during play. Opponent hand-modeling is also incorporated within a two-player zero-sum simulation framework, and the resulting strategies are evaluated using statistical hypothesis testing. Empirical results show significant improvement in win rates for MinDist-based agents over traditional heuristics, providing a formal and interpretable step toward algorithmic Rummy strategy design.

cs.AI↗

arXiv2025

Trick or Treat? Free-ranging dogs use human behavioural cues for foraging

Animals that display behavioural flexibility and adaptability thrive in urban environments, due to their ability to exploit novel anthropogenic resources. Since humans are an important component of such urban environments, animals that apply heterospecific learning in their decision-making are more likely to succeed as urban adapters. Free-ranging dogs, that have been living in human-dominated environments for centuries, are excellent urban adapters. In this study, we sought to understand the role and extent of human behavioural cues in decision-making during foraging by free-ranging dogs. We investigated whether these dogs were more attracted to items that humans appeared to be eating. When presented with a real and a fake biscuit, the dogs showed a clear preference for the food item. Between two identical biscuits, they chose the one that had been bitten by a human. However, when a fake biscuit was bitten and presented with a real one, the dogs failed to choose one over the other, suggesting a strong influence of the human-provided cue of biting over the natural cue of the smell of the food item. The dogs displayed left-bias during food choice across experimental conditions. These results demonstrate that dog foraging choices in urban environments are a mix of heterospecific learning and independent decision-making, highlighting an important facet behind their success in anthropogenic habitats. This also underscores the high level of dependence that free-ranging dogs have on humans in the urban habitat, not only as a source of food, but as an integral part of their ecological niche.

q-bio.OT↗

arXiv2025

Adapting Skill Ratings to Luck-Based Hidden-Information Games

Rating systems play a crucial role in evaluating player skill across competitive environments. The Elo rating system, originally designed for deterministic and information-complete games such as chess, has been widely adopted and modified in various domains. However, the traditional Elo rating system only considers game outcomes for rating calculation and assumes uniform initial states across players. This raises important methodological challenges in skill modelling for popular partially randomized incomplete-information games such as Rummy. In this paper, we examine the limitations of conventional Elo ratings when applied to luck-driven environments and propose a modified Elo framework specifically tailored for Rummy. Our approach incorporates score-based performance metrics and explicitly models the influence of initial hand quality to disentangle skill from luck. Through extensive simulations involving 270,000 games across six strategies of varying sophistication, we demonstrate that our proposed system achieves stable convergence, superior discriminative power, and enhanced predictive accuracy compared to traditional Elo formulations. The framework maintains computational simplicity while effectively capturing the interplay of skill, strategy, and randomness, with broad applicability to other stochastic competitive environments.

cs.GT↗