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Kaustav Chatterjee

Publications and source records attributed to Kaustav Chatterjee.

At least 19 recordsLinked to original sources

Finite-energy GKP-QPC architectures for photonic quantum memories and repeaters

Photonic quantum networks require error-correction architectures that remain useful with finite-energy bosonic states, pure-loss fiber transmission, and explicit resource accounting. In this light, we study a concatenated architecture in which each physical rail is a finitely squeezed Gottesman--Kitaev--Preskill (GKP) qubit transmitted through a pure-loss fiber segment, corrected by teleportation-based GKP error correction with finitely squeezed ancillae, and decoded by an outer quantum parity code (QPC). The GKP layer converts continuous homodyne syndromes into effective rail-level Pauli marginals, while the QPC layer suppresses the residual qubit-level errors. For the concatenated code family considered here, we find a finite-squeezing threshold of $5.06\,\mathrm{dB}$ at zero propagation loss. In the memory setting, the QPC layer lowers the squeezing at which repeated error correction becomes beneficial from $6.7\,\mathrm{dB}$ for bare GKP correction to $5.2\,\mathrm{dB}$ for QPC$(3,3)$ and $4.3\,\mathrm{dB}$ for QPC$(5,5)$, and improves the average-fidelity ratio by up to $75$--$90\%$ in the relevant intermediate-noise regime. In the repeater setting, avoiding pre-amplification gives larger secret-key fractions at moderate squeezing, but also produces an optimal squeezing because highly squeezed GKP peaks become sensitive to loss-induced inward displacement. Resource-normalized rates show that QPC concatenation can exceed the repeaterless PLOB benchmark by orders of magnitude and extend the communication reach, at short repeater spacing, to distances of order $10^4$km with $14$dB squeezing. However, QPC concatenation becomes detrimental when each elementary hop is too lossy. These results provide quantitative design rules for finite-squeezing GKP--QPC quantum memories and repeaters.

quant-ph

Assessing Risks of Hydro-Generator Shaft Fatigue from Data Center Load Oscillations

Large AI data center loads can introduce persistent sub-synchronous active-power oscillations that may impact nearby generators by exciting torsional modes and increasing shaft stress. This paper presents a model-based framework for evaluating hydro-generator shaft fatigue risk under oscillatory loading. An electromagnetic transient simulation model is developed using a two-mass turbine-generator shaft representation with parameters from real-world generation units and a configurable AI data center load. The risk assessment is performed in two stages. First, a network transfer function quantifies the propagation of load oscillations from the data center point of interconnection to the hydro-generator terminal. A plant transfer function then characterizes the resulting shaft torque amplification. A frequency-scan approach identifies resonance regions and evaluates torque amplification at individual forcing frequencies. Parametric studies show that amplification is strongly affected by generator-to-turbine inertia ratio and torsional damping. Lower inertia ratios shift torsional modes to lower frequencies and increase amplification, indicating that some Kaplan-type units may be more susceptible than comparable Francis or Pelton units. Reduced damping further increases resonant response and fatigue exposure. A simplified fatigue assessment based on S--N curves and the Goodman diagram relates simulated torque response to mechanical integrity. The resulting Goodman safety factor provides a practical metric for evaluating the impact of persistent AI data center oscillations on hydro-generator service life and supports interconnection studies, oscillation limits, and plant-level monitoring strategies.

eess.SY

Activating entanglement and EPR steering from continuous-variable resources using witness-based measures

We introduce a general witness-based framework for quantifying and operationally activating continuous-variable (CV) resources into discrete-variable (DV) bipartite entanglement or Einstein- Podolsky-Rosen (EPR) steering. For the three standard CV resource theories associated with Wigner negativity (WN), genuine non-Gaussianity (GNG), and standard non-Gaussianity (SNG), we define infinite families of bounded-witness monotones indexed by box constraints on the witness operators. For closed convex free sets, these monotones are faithful, strongly monotonic under free instruments, Lipschitz continuous, and convex. For closed nonconvex free sets, we show that faithfulness requires a two-copy lift and formulate the corresponding strong-monotonicity statement in the lifted theory. We further construct witness-dependent completely positive trace-preserving (CPTP) measure-and-prepare channels whose outputs are two-qubit Werner states. For the representative case n = m = 1, the optimal entanglement and EPR steering attainable within this witness-dependent activation family are exactly proportional to the underlying monotones. We illustrate the framework with odd-parity states, pure-loss single-photon states, and Gottesman- Kitaev-Preskill (GKP) states, and derive explicit lower bounds for pure-state GNG and SNG. More broadly, our results show that closed CV free sets admit witness-based quantifiers with a direct operational interpretation in terms of experimentally accessible DV correlations.

quant-ph

The Bott Metric: A Real-Space Bridge Between Topology and Quantum Metric

The Bott index has become an indispensable tool to probe the topology of quantum matter, particularly in systems lacking translational symmetry. Constructed from a plaquette operator, it retains the phase information while discarding the amplitude. Here we introduce and develop the Bott metric, which captures this complementary amplitude information and provides a measure of the underlying quantum metric of the system. We show that, in the thermodynamic limit, the Bott metric converges to the trace of the integrated quantum metric. Our framework provides a new route to reveal the quantum metric structure in non-periodic systems, which we illustrate using representative examples ranging from disordered to amorphous models. More broadly, our definition of the Bott metric unifies the notion of topological invariants and quantum metric under the same overarching plaquette operator construction.

cond-mat.dis-nn

All-optical quantum memory using bosonic quantum error correction codes

Reliable quantum memory is essential for scalable quantum networks and fault-tolerant photonic quantum computing. We present a quantitative analysis of an all-optical quantum memory architecture in which a Gottesman-Kitaev-Preskill (GKP) encoded qubit is stored in a fibre loop and periodically stabilized using teleportation-based error correction. By modelling fibre propagation as a pure-loss channel and representing each correction round as an effective logical map acting on the Bloch vector, we obtain a compact description of the full multi-round memory channel. We show that syndrome decoder optimization plays a crucial role in the experimentally relevant finite-squeezing regime. The optimal decoder deviates from standard square-grid GKP decoder in both tile-size and tile-shape, leading to significant improved logical performance. Using this optimized decoding strategy, we identify a squeezing-dependent optimal spacing between correction nodes that maximizes the memory lifetime. Remarkably, this optimal segment length is largely independent of the desired storage time, providing a simple and practical design rule for fibre-loop quantum memory. We further find a squeezing threshold of approximately 6.7 dB below which intermediate error correction becomes counterproductive, while above threshold the achievable storage time increases approximately exponentially with squeezing. For example, at 17 dB squeezing, storage times exceeding 400 ms can be achieved with logical infidelity below 1%. These results establish clear performance benchmarks and reveal the fundamental trade-off between photon loss, squeezing, and correction frequency in continuous-variable architectures. Our findings provide actionable design principles for near-term photonic quantum memory and clarify the path toward scalable all-optical fault-tolerant quantum storage.

quant-ph

Even Odd Splitting of the Gaussian Quantum Fisher Information: From Symplectic Geometry to Metrology

We introduce a canonical decomposition of the quantum Fisher information (QFI) for centered multimode Gaussian states into two additive pieces: an even part that captures changes in the symplectic spectrum and an odd part associated with correlation-generating dynamics. On the pure-state manifold, the even contribution vanishes identically, while the odd contribution coincides with the QFI derived from the natural metric on the Siegel upper half-space, revealing a direct geometric underpinning of pure-Gaussian metrology. This also provides a link between the graphical representation of pure Gaussian states and an explicit expression for the QFI in terms of graphical parameters. For evolutions completely generated by passive Gaussian unitaries (orthogonal symplectics), the odd QFI vanishes, while thermometric parameters contribute purely to the even sector with a simple spectral form; we also derive a state-dependent lower bound on the even QFI in terms of the purity-change rate. We extend the construction to the full QFI matrix, obtaining an additive even odd sector decomposition that clarifies when cross-parameter information vanishes. Applications to unitary sensing (beam splitter versus two-mode squeezing) and to Gaussian channels (loss and phase-insensitive amplification), including joint phase loss estimation, demonstrate how the decomposition cleanly separates resources associated with spectrum versus correlations. The framework supplies practical design rules for continuous-variable sensors and provides a geometric lens for benchmarking probes and channels in Gaussian quantum metrology.

quant-ph

Network Level Evaluation of Hangup Susceptibility of HRGCs using Deep Learning and Sensing Techniques: A Goal Towards Safer Future

Steep-profiled Highway Railway Grade Crossings (HRGCs) pose safety hazards to vehicles with low ground clearance, which may become stranded on the tracks, creating risks of train vehicle collisions. This research develops a framework for network level evaluation of hang-up susceptibility of HRGCs. Profile data from different crossings in Oklahoma were collected using both a walking profiler and the Pave3D8K Laser Imaging System. A hybrid deep learning model, combining Long Short Term Memory (LSTM) and Transformer architectures, was developed to reconstruct accurate HRGC profiles from Pave3D8K Laser Imaging System data. Vehicle dimension data from around 350 specialty vehicles were collected at various locations across Oklahoma to enable up-to-date statistical design dimensions. Hang-up susceptibility was analyzed using three vehicle dimension scenarios: (a) median dimension (median wheelbase and ground clearance), (b) 75-25 percentile dimension (75 percentile wheelbase, 25 percentile ground clearance), and (c) worst case dimension (maximum wheelbase and minimum ground clearance). Results indicate 70, 80, and 95 crossings at the highest hang-up risk levels under these scenarios, respectively. An ArcGIS database and a software interface were developed to support transportation agencies in mitigating crossing hazards. This framework advances safety evaluation by integrating next-generation sensing, deep learning, and infrastructure datasets into practical decision support tools.

cs.LG

Policy Gradient-Based EMT-in-the-Loop Learning to Mitigate Sub-Synchronous Control Interactions

This paper explores the development of learning-based tunable control gains using EMT-in-the-loop simulation framework (e.g., PSCAD interfaced with Python-based learning modules) to address critical sub-synchronous oscillations. Since sub-synchronous control interactions (SSCI) arise from the mis-tuning of control gains under specific grid configurations, effective mitigation strategies require adaptive re-tuning of these gains. Such adaptiveness can be achieved by employing a closed-loop, learning-based framework that considers the grid conditions responsible for such sub-synchronous oscillations. This paper addresses this need by adopting methodologies inspired by Markov decision process (MDP) based reinforcement learning (RL), with a particular emphasis on simpler deep policy gradient methods with additional SSCI-specific signal processing modules such as down-sampling, bandpass filtering, and oscillation energy dependent reward computations. Our experimentation in a real-world event setting demonstrates that the deep policy gradient based trained policy can adaptively compute gain settings in response to varying grid conditions and optimally suppress control interaction-induced oscillations.

eess.SY

Oscillation Analysis and Damping Control for a Proposed North American AC-DC Macrogrid

In recent years, several studies conducted by both industry and U.S. Department of Energy (DOE)-funded initiatives have proposed linking North America's Eastern and Western Interconnections (EI and WI) through a multiterminal DC (MTDC) macrogrid. These studies have explored the advantages and opportunities of the proposed configuration from the perspectives of capacity sharing and frequency support. However, the potential challenges of small-signal stability arising from this interconnection have not been thoroughly examined. To address this gap, detailed model-based simulation studies are performed in this paper to assess the risks of poorly damped inter-area oscillations in the proposed macrogrid. A custom-built dynamic model of the MTDC system is developed and integrated with industry-grade models of the EI and WI, incorporating high levels of inverter-based energy resources. Through model-based oscillation analysis, potential shifts in inter-area modes for both EI and WI, resulting from the MTDC integration are characterized, and modes with inadequate damping are identified. Furthermore, to mitigate the risks of unstable oscillations, supplementary damping controllers are designed for the MTDC system, leveraging wide-area feedback to modulate active power set points at selected converter stations. A frequency scanning approach is employed for data-driven model linearization and controller synthesis. The damping performance is evaluated under the designed operating conditions and selected contingency scenarios.

eess.SY

No-broadcasting of non-Gaussian states

Gaussian states are of fundamental importance in the physics of continuous-variable quantum systems. They are appealing for the experimental ease with which they can be produced, and for their compact and an elegant mathematical description. Nevertheless, many proposed quantum technologies require us to go beyond the realm of Gaussian states and introduce non-Gaussian elements. In terms of quantum resource theory, we can then recognize non-Gaussian states as resources and Gaussian operations and states as free, which can be used and prepared easily. Given such a structure of resource theory, the task of broadcasting the resource is to determine if the resource content of a state can be cloned in a meaningful way, which, if possible, provides a strong operation for manipulation of the resource. In this work, we prove that broadcasting of non-Gaussian states via Gaussian operations is not possible. For this, we first show that the relative entropy of non- Gaussianity is not super-additive, which rules it out as a prime candidate in the analysis of such no-go results. Our proof is then based on understanding fixed points of Gaussian operations and relates to the theory of control systems. The no-go theorem also states that if two initially uncorrelated systems interact by Gaussian dynamics and non-Gaussianity is created at one subsystem, then the non-Gaussianity of the other subsystem must be reduced. Further, keeping the set of free operations fixed to Gaussian operations, we can also comment on the broadcasting of Wigner negativity and genuine quantum non-Gaussianity.

quant-ph

Relativistic Quantum Otto Engine: Generalized efficiency bounds and frictional effects

This work investigates a relativistic quantum Otto engine with a harmonic oscillator as its working medium, analyzing how relativistic motion and nonadiabatic driving affect its performance and efficiency bounds. In the adiabatic regime, a closed-form analytical expression is derived for the generalized Carnot efficiency, which incorporates the effects of relativistic motion and reduces to the standard Carnot efficiency in the nonrelativistic limit. For nonadiabatic driving, we consider sudden compression and expansion work strokes and show that the maximum efficiency achievable by the engine is limited to 1/2, even in the ultra-relativistic limit. Going one step further, we also derive an analytical expression for the efficiency bound in the sudden-switch protocol, which can be regarded as the nonadiabatic counterpart of the generalized Carnot efficiency. Together, these results provide analytical bounds for the efficiency of relativistic quantum heat engines and constitute the first systematic study of the interplay between relativistic motion and frictional effects arising from nonadiabatic driving.

quant-ph

Relativistic Quantum Thermal Machine: Harnessing Relativistic Effects to Surpass Carnot Efficiency

We investigate a three-level maser quantum thermal machine in which the system-reservoir interaction is modeled via Unruh-DeWitt type coupling, with one or both reservoirs undergoing relativistic motion relative to the working medium. Motion induces Doppler reshaping of the reservoir spectra, modifying energy-exchange rates and enabling operation beyond the Carnot efficiency at finite power. We numerically analyze families of efficiency-power curves and extract the analytic form of a generalized Carnot bound, which recovers the Carnot limit. In addition, Doppler reshaping alters the boundaries between heat-engine and refrigerator operation, making it possible to extract positive work even in the absence of a temperature gradient. These findings establish relativistic motion as a genuine thermodynamic resource.

quant-ph

Identification of Sub/Super-Synchronous Control Interaction Paths Using Dissipative Energy Flow

Sub- and super-synchronous control interactions (SSCIs) are oscillations arising from adverse interactions between inverter-based resource (IBR) controls and the power network. SSCIs often involve multiple frequencies and propagate through complex, interconnected paths, making it difficult for model-based approaches to identify both the sources and the paths of oscillatory energy flow. This paper extends the Dissipative Energy Flow (DEF) method, originally developed for low-frequency electromechanical oscillations, to identify SSCI sources and dynamic interaction paths across multiple frequencies using three-phase voltage and current measurements. The approach operates in the dq frame using dynamic phasors, enabling mode-specific DEF computation from bandpass-filtered signals. An electromagnetic transient (EMT) case study on a meshed network with synchronous generator and type-3 wind farm resources under series-compensated conditions demonstrates the method's capability to distinguish frequency-dependent source and sink roles, including cases where the same resource acts as a source at one frequency and a sink at another. The results show DEF can provide a physics-based and automation-friendly tool for SSCI diagnosis in IBR-rich grids.

eess.SY

Managing Risks from Large Digital Loads Using Coordinated Grid-Forming Storage Network

Anticipated rapid growth of large digital load, driven by artificial intelligence (AI) data centers, is poised to increase uncertainty and large fluctuations in consumption, threatening the stability, reliability, and security of the energy infrastructure. Conventional measures taken by grid planners and operators to ensure stable and reliable integration of new resources are either cost-prohibitive (e.g., transmission upgrades) or ill-equipped (e.g., generation control) to resolve the unique challenges brought on by AI Data Centers (e.g., extreme load transients). In this work, we explore the feasibility of coordinating and managing available flexibility in the grid, in terms of grid-forming storage units, to ensure stable and reliable integration of AI Data Centers without the need for costly grid upgrades. Recently developed bi-layered coordinated control strategies -- involving fast-acting, local, autonomous, control at the storage to maintain transient safety in voltage and frequency at the point-of-interconnection, and a slower, coordinated (consensus) control to restore normal operating condition in the grid -- are used in the case studies. A comparison is drawn between broadly two scenarios: a network of coordinated, smaller, distributed storage vs. larger storage installations collocated with large digital loads. IEEE 68-bus network is used for the case studies, with large digital load profiles drawn from the MIT Supercloud Dataset.

eess.SY

Hybrid LSTM-Transformer Models for Profiling Highway-Railway Grade Crossings

Hump crossings, or high-profile Highway Railway Grade Crossings (HRGCs), pose safety risks to highway vehicles due to potential hang-ups. These crossings typically result from post-construction railway track maintenance activities or non-compliance with design guidelines for HRGC vertical alignments. Conventional methods for measuring HRGC profiles are costly, time-consuming, traffic-disruptive, and present safety challenges. To address these issues, this research employed advanced, cost-effective techniques and innovative modeling approaches for HRGC profile measurement. A novel hybrid deep learning framework combining Long Short-Term Memory (LSTM) and Transformer architectures was developed by utilizing instrumentation and ground truth data. Instrumentation data were gathered using a highway testing vehicle equipped with Inertial Measurement Unit (IMU) and Global Positioning System (GPS) sensors, while ground truth data were obtained via an industrial-standard walking profiler. Field data was collected at the Red Rock Railroad Corridor in Oklahoma. Three advanced deep learning models Transformer-LSTM sequential (model 1), LSTM-Transformer sequential (model 2), and LSTM-Transformer parallel (model 3) were evaluated to identify the most efficient architecture. Models 2 and 3 outperformed the others and were deployed to generate 2D/3D HRGC profiles. The deep learning models demonstrated significant potential to enhance highway and railroad safety by enabling rapid and accurate assessment of HRGC hang-up susceptibility.

cs.LG

Coordinated Frequency Regulation in Grid-Forming Storage Network via Safety-Consensus

Inverter-based storages are poised to play a prominent role in future power grids with massive renewable generation. Grid-forming inverters (GFMs) are emerging as a dominant technology with synchronous generators (SG)-like characteristics through primary control loops. Advanced secondary control schemes, e.g., consensus algorithms, allow GFM-interfaced storage units to participate in frequency regulations and restore nominal frequency following grid disturbances. However, it is imperative to ensure transient frequency excursions do not violate critical safety limits while the grid transitions from pre- to post-disturbance operating point. This paper presents a hierarchical safety-enforced consensus method -- combining a device-layer (decentralized) transient safety filter with a secondary-layer (distributed) consensus coordination -- to achieve three distinct objectives: limiting transient frequency excursions to safe limits, minimizing frequency deviations from nominal, and ensuring coordinated power sharing among GFM-storage units. The proposed hierarchical (two-layered) safety-consensus technique is illustrated using a GFM-interfaced storage network on an IEEE 68-bus system under multiple grid transient scenarios.

eess.SY

Frequency Control and Disturbance Containment Using Grid-Forming Embedded Storage Networks

The paper discusses fast frequency control in bulk power systems using embedded networks of grid-forming energy storage resources. Differing from their traditional roles of regulating reserves, the storage resources in this work operate as fast-acting grid assets shaping transient dynamics. The storage resources in the network are autonomously controlled using local measurements for distributed frequency support during disturbance events. Further, the grid-forming inverter systems interfacing with the storage resources, are augmented with fast-acting safety controls designed to contain frequency transients within a prescribed tolerance band. The control action, derived from the storage network, improves the frequency nadirs in the system and prevents the severity of a disturbance from propagating far from the source. The paper also presents sensitivity studies to evaluate the impacts of storage capacity and inverter controller parameters on the dynamic performance of frequency control and disturbance localization. The performance of the safety-constrained grid-forming control is also compared with the more common grid-following control. The results are illustrated through case studies on an IEEE test system.

eess.SY

Grid-Forming Storage Networks: Analytical Characterization of Damping and Design Insights

The paper presents a theoretical study on small-signal stability and damping in bulk power systems with multiple grid-forming inverter-based storage resources. A detailed analysis is presented, characterizing the impacts of inverter droop gains and storage size on the slower eigenvalues, particularly those concerning inter-area oscillation modes. From these parametric sensitivity studies, a set of necessary conditions are derived that the design of droop gain must satisfy to enhance damping performance. The analytical findings are structured into propositions highlighting potential design considerations for improving system stability. The findings are illustrated via numerical studies on an IEEE 68-bus grid-forming storage network.

eess.SY