arXiv subjects

Kumar Ghosh

Publications and source records attributed to Kumar Ghosh.

At least 19 recordsLinked to original sources

arXiv2026

Self-calibrating thermal interferometry of vortex parity in a two-dimensional chiral superconductor

A chiral superconductor carries chiral Majorana modes along its boundary, and the integer that counts them fixes everything that follows, yet that integer has never been measured together with a local parity observable on one object. Proximitized one-dimensional wires read fermion parity rapidly but diagnose bulk topology through a separate protocol. Here we show that a reconfigurable domain wall between regions of opposite Chern number in an intrinsic two-dimensional chiral superconductor performs both functions. Opened to its contacts the wall is a ballistic channel whose quantized thermal conductance counts its Majorana modes; closed, the same wall is a Fabry--P\'erot resonator whose spectrum shifts by half a level spacing when the parity of the enclosed vortices changes, giving a two-level heat conductance. We derive the exact transmission, the elastic heat full counting statistics, and a theorem showing that linear-response heat scattering of a fixed quadratic problem resolves vortex parity but not the fusion channel of well-separated cores. An outside vortex hybridized with the wall is an intrinsic false positive; temperature, geometry and a finite-bias mean--noise test separates it. Rhombohedral-graphene parameters place submicron loops in the resolved regime at millikelvin temperatures, where chiral-domain reconfiguration and noise thermometry are both established.

cond-mat.supr-con↗

arXiv2026

Thermal Hall tomography of chiral superconductivity in rhombohedral graphene

A chiral superconductor carries chiral Majorana modes along its edges, and a single integer, the Bogoliubov--de Gennes Chern number, counts them. Thirty years of candidate materials have not yielded a measurement of that integer, because the magnetic signatures usually invoked are not topologically protected. Rhombohedral graphene makes the question both urgent and answerable: magnetic imaging resolves rewritable time-reversal-breaking domains inside the superconducting phase, while quantum oscillations reveal a normal state too intricate to reconstruct pocket by pocket. We show that the low-temperature thermal Hall conductance returns the integer directly, with no such reconstruction. For band-projected pairing it equals the pairing-vortex winding enclosed by the occupied regions of momentum space. Splitting the intravalley Hamiltonian into symmetric and antisymmetric parts isolates the trigonal warping and finite Cooper pair momentum of the real material: the antisymmetric part is topologically inert, direct Chern calculations across $525$ parameter points show the invariant preserved, and one inequality marks where a Bogoliubov Fermi surface removes quantization. The plateau $\kappa_{xy}/T=(\pi^2k_B^2/6h)\,C_{\rm BdG}$ then reads out the integer, its sign reverses with the imaged domain, a written domain wall should carry $2|C_{\rm BdG}|$ Majorana channels, and the thermometry required already resolves single thermal quanta in encapsulated graphene at millikelvin temperatures.

cond-mat.mes-hall↗

arXiv2026

Dimensional and Spin Interpolation for the O$(n)$ Model: From Exact Anchors to RG-Improved Critical Exponents

We develop a two-axis interpolation framework for the O$(n)$ universality family, treating the spatial dimension $D$ and the spin-component number $n$ as independent continuous parameters connecting exact limiting solutions. On the spatial axis, anchoring between the Onsager solution at $D=2$ and mean-field theory at $D\to\infty$ yields a closed-form prediction for the 3D Ising critical coupling that agrees well with Monte Carlo benchmarks $K_c = 0.2204$ (benchmark: $0.22165$) with no adjustable parameters. Wilson--Fisher-constrained polynomial interpolation gives $\nu=2/3$, $\beta=31/96$, and $\eta=35/864$ at $D=3$ (benchmarks: $0.6299$, $0.3265$, $0.0362$), and reproduces conformal-bootstrap results across $3 \le D < 4$. On the spin axis, we establish a necessary compatibility criterion: two-anchor interpolation succeeds only for observables that vary monotonically between the anchor values. The critical coupling $K_c(n)$ violates this criterion because the Heisenberg value falls below the spherical limit, whereas the correlation-length exponent $\nu(n)$ satisfies it. A perturbative $1/n^2$ expansion yields $\nu(3) = 0.7493$ (benchmark: $0.7112$), and propagation through exact scaling relations gives $\beta(3) = 0.3797$ (benchmark: $0.3689$) and $\gamma(3) = 1.489$ (benchmark: $1.396$), without introducing additional parameters. The framework naturally extends to non-integer spin, producing the prediction $\nu(2.5) = 0.7143$ for the O$(2.5)$ universality class. These results establish dimensional and spin interpolation as a unified and predictive approach to critical phenomena, while clarifying the structural conditions under which interpolation succeeds.

cond-mat.stat-mech↗

arXiv2026

Parity Anomaly of Preformed Pairs Governs the Thermal Hall Effect above $T_c$

A large negative thermal Hall signal has been reported across multiple cuprate families in the pseudogap phase where the superconducting order parameter has vanished, with a magnitude that no existing microscopic theory reproduces without free parameters. Competing proposals based on chiral phonons, spinons, or loop currents each require undetermined coupling constants and do not predict the temperature dependence in terms of an independently measured spectroscopic gap. We show that the parity anomaly of $(2+1)$-dimensional quantum field theory resolves this long-standing puzzle: the preformed-pair pseudogap $\Delta_{\rm pg}(T)$ enters the parity-odd fermion determinant identically to a condensate mass, yielding the exact parameter-free formula $\kappa_{xy}/T = (\pi^2 k_B^2/6h)\,C\,\tanh[\Delta_{\rm pg}(T)/(2k_BT)]$, where $C$ is the Chern number of the chiral pairing channel and $\Delta_{\rm pg}(T)$ is directly measurable by ARPES or STM. Coleman-Hill non-renormalization protects the result against higher-loop corrections, and two independent numerical tests, Wilson-loop flux threading and DMRG on $p+ip$ cylinders, confirm the anomaly correlation length to $0.2\%$ accuracy with no power-law finite-size corrections. The theory predicts thermal Hall onset at $T^*$ rather than $T_c$, provides a falsifiable logarithmic-derivative test against ARPES data, and yields a concrete quantitative target for magic-angle twisted bilayer graphene.

cond-mat.supr-con↗

arXiv2026

From Dirac Cones to Semions: An Exact Finite-Size Theory of Parity-Anomaly Transport in Chiral Spin Liquids

Chiral spin liquids realize a topological state whose universal response is a fractional spin Hall conductance $\nu_s$. The three quantities that determine this response, the integer Chern number of the fractionalized spinons, the level of the emergent Chern--Simons gauge field, and the physically measured spin pump, are related but distinct, and their relation is often stated only schematically. Here we derive it from a single object: the parity-odd determinant of a gapped Dirac cone on a spatial cylinder, resummed exactly to all orders in the compact holonomy. This determinant fixes the map from spinon topology to measurable response, and proves that finite-size corrections to the topological pump are strictly exponential, with no universal $1/L$ term. We test the resulting predictions on the kagome chiral spin liquid at three independent levels: the exact one-loop field theory, a parton band-structure calculation ($C=-1$, converging exponentially over cylinders four to twelve sites wide), and an interacting density-matrix renormalization group flux pump on the explicitly chiral $J$--$J_\chi$ Hamiltonian ($\nu_s=-0.500\pm0.011$). All three agree with the analytic prediction without adjustable parameters, providing a fully quantitative bridge between microscopic topology and observable fractional response.

cond-mat.str-el↗

arXiv2026

Hybrid Quantum-Classical Machine Learning Algorithms for Multi-Output Time-Series Forecasting at Utility Scale

Multi-output time-series forecasting in energy systems is challenging because of nonlinear dynamics, multi-scale seasonality, and strong dependencies across correlated series. In this work, we investigate two hybrid quantum-classical frameworks for multi-stream time-series forecasting on a real Smart Meter dataset comprising 103 household electricity consumption time-series, with experiments executed on the $ibm\_marrakesh$ superconducting quantum processor. The first model, Kernelized Quantum Reservoir Computing with Repeated Measurement (KQRC-RM), combines coupled quantum reservoirs, ancilla-assisted repeated measurement, and kernelized readouts to model temporal dynamics and cross-stream correlations jointly. For a 3-stream time-series input and output, the KQRC-RM model using 114 qubits achieves an MAE of 0.0811 on MPS simulator (36.92\% improvement over its classical analog) whereas performance degrades to an MAE of 0.1524 on hardware. The second, a Projected Quantum Kernel Gaussian Process (QGP), replaces fidelity-based kernels with projected kernels constructed from local reduced-state statistics. Using a topology-aware 100-qubit QGP model to predict 100 multi-output time-series values, we observe 49\% of time-series outputs achieve high-accuracy predictions (MAE $<0.15$), with an average MAE of $0.082$ for this low-error group. The medium-error regime (MAE $0.15$-$0.35$) has an average MAE of $0.229$, while the high-error regime (MAE $>0.35$) has an average MAE of $0.664$. Overall, this reduces the average MAE relative to the classical GP baseline by 62.01\% on MPS simulator and 40.37\% on hardware. Together, these results demonstrate the feasibility of hybrid quantum machine learning for multi-input, multi-output time-series forecasting at the 100+ qubit scale on NISQ devices.

quant-ph↗

arXiv2026

Quantum simulation of interlayer charge ordering in Kagome frustrated-magnet

The interplay between interlayer coupling and quantum fluctuations governs charge ordering and defect dynamics in Kagome systems, yet these parameters are intrinsically entangled in existing Kagome metals and artificial spin-ice platforms, preventing their independent control. Here we realize a bilayer Kagome frustrated-magnet simulator comprising 1,536 connected spins on D-Wave quantum annealer, in which the effective quantum drive, $\Gamma_{\rm eff}$, and interlayer exchange, $J_{\perp}$, are independently programmable. We observe an interlayer-driven transition from ferroelectric to antiferroelectric Ice-II charge order at a critical coupling $(J_\perp/J_1)^*\approx0.04$, a phenomenon absent in single-layer geometries. Monte Carlo calculations show the transition persists in the classical limit, allowing the experimentally observed critical coupling to quantify the quantum renormalization induced by fluctuations. Applying resulting phase diagram to Kagome charge-density-wave materials places KV$_3$Sb$_5$ and RbV$_3$Sb$_5$ deep within the ordered antiferroelectric regime, while locating CsV$_3$Sb$_5$ near the phase boundary, providing a natural explanation for its metastable $2\times2\times4$ stacking order. We further show that restricting charge-correlation measurements to ice-rule configurations resolves a systematic underestimation of ordering in conventional analyses, enabling direct reinterpretation of resonant X-ray, XMCD, STM and anomalous Hall experiments. Finally, we demonstrate that the same charge-sector reorganization framework explains near-degenerate plateau states in the metallic Kagome spin-ice HoAgGe and yields experimentally testable predictions for nanomagnetic, Kagome-metal and van der Waals frustrated systems. These results establish programmable quantum annealers as scalable simulators of emergent charge order and monopole physics in frustrated quantum matter.

cond-mat.str-el↗

arXiv2026

Universal Quantum Suppression in Frustrated Ising Magnets across the Quasi-1D to 2D Crossover via Quantum Annealing

Quantum magnets in the $M\mathrm{Nb_2O_6}$ and BaCo$_2$V$_2$O$_8$ families realise frustrated transverse-field Ising models whose competing ferromagnetic and antiferromagnetic couplings generate a sign problem provably intractable for quantum Monte Carlo at any system size, leaving their quantum phase boundaries numerically Inaccessible. Using a D-Wave Advantage2 quantum annealer at $L\leq27$ (729 spins), we obtain the large-$L$ critical points for this model family, measuring quantum-driven transitions at ${g_c^{\mathrm{QPU}}}\in\{0.286,\,0.210,\,0.156,\,0.093\}$ for $\alpha\in\{1.0,\,0.7,\,0.5,\,0.3\}$, where the analytically exact classical threshold is ${g_c^{\mathrm{class}}}(\alpha)=2\alpha/3$. The suppression ratio $r(\alpha)$ exhibits a sharp two-regime structure: the three quasi-1D geometries ($\alpha\leq0.7$) are mutually consistent with a universal plateau $\bar{r}=0.450$ ($\chi^2/\mathrm{dof}=1.10$, $p=0.33$), demonstrating that quantum fluctuations destroy approximately $55\%$ of the classical FM stability window independently of coupling anisotropy, while $r$ steps down to the 2D limit above the empirical crossover scale $\alpha^*\approx0.7$. Inner Binder cumulant pairs, which converge fastest to the thermodynamic limit, resolve $r(1.0)\approx0.412$ and a step $\Delta r=0.038\pm0.015$ from the quasi-1D plateau. A four-point linear fit $r(\alpha)=0.494-0.063\,\alpha$ summarises both regimes; its $\alpha\to0$ intercept recovers the exact 1D result of Pfeuty within 1.7 standard deviations, and its slope is a lower bound on the true crossover amplitude concentrated in $\alpha\in[\alpha^*,1]$. Two sequential blind predictions, confirmed at $0.2\sigma$ and $0.7\sigma$ before each measurement, validate the crossover law. All four geometries show a direct ferromagnet-to-paramagnet transition, complete quantum ergodicity ($f_{\rm uniq}=1.000$), and null valence-bond solid order.

cond-mat.str-el↗

arXiv2026

Breaking concentration barriers for quantum extreme learning on digital quantum processors

Reservoir computing leverages rich, non-linear dynamics to process temporal data. Quantum variants promise enhanced expressivity from high-dimensional Hilbert spaces, yet their practical applicability is hindered by hardware noise and concentration effects that can erase input-output distinguishability at large system sizes. In this work, we present and experimentally demonstrate a Quantum Extreme Learning Machine (QELM) tailored to state-of-the-art superconducting platforms, employing up to 124 qubits and circuits with more than 5,000 two-qubit gates on IBM Quantum computers. We introduce a practical multi-objective hyperparameter tuning strategy that jointly monitors observable variability, capacity, and task performance to identify noise-robust operating points. In addition, we develop a local eigentask analysis that enables computationally efficient feature selection and effective information retrieval. We report evidence of a regime of optimality that is identifiable at small scales and transferable across tasks and larger systems, and we achieve performances competitive with leading classical baselines on representative benchmarks for time-series forecasting and satellite image classification. Together, our results establish a viable and robust framework for large-scale, pre-fault-tolerant quantum machine learning and provide a foundation for extending reservoir-based methods to more expressive architectures and real-world scenarios.

quant-ph↗

arXiv2025

Exploring Replica Symmetry Breaking and Topological Collapse in Spin Glasses with Quantum Annealing

Replica symmetry breaking (RSB) underlies the complex organization of disordered systems, yet quantitative validation beyond $N \sim 100$ spins has remained computationally challenging. We use quantum annealing to access ground states of the Sherrington-Kirkpatrick model up to $N = 4000$ spins, enabling the most extensive test of Parisi's Nobel Prize-winning RSB solution to date. Five independent observables confirm RSB predictions: ground-state energies converge to Parisi's value with characteristic $N^{-2/3}$ corrections, energy fluctuations scale as $N^{-3/4}$ ($\gamma = 0.739 \pm 0.036$), the chaos exponent $\theta = 0.51 \pm 0.02$ ($R^2 = 0.989$) confirms mean-field universality, the overlap distribution exhibits hierarchical structure ($\sigma_q = 0.19$), and the complexity remains invariant under 36\% network dilution. Beyond a critical threshold $0.8 < D_c < 0.9$, the hierarchy collapses discontinuously through a cooperative avalanche that converts the entire system to vacancies within a narrow parameter window $\Delta D = 0.1$. These findings establish quantum computation as a tool for probing emergent many-body phenomena and uncover the topological foundations of complexity in disordered systems, with implications for neural networks, optimization, and materials science.

cond-mat.dis-nn↗

arXiv2024

Mitigating exponential concentration in covariant quantum kernels for subspace and real-world data

Fidelity quantum kernels have shown promise in classification tasks, particularly when a group structure in the data can be identified and exploited through a covariant feature map. In fact, there exist classification problems on which covariant kernels provide a provable advantage, thus establishing a separation between quantum and classical learners. However, their practical application poses two challenges: on one side, the group structure may be unknown and approximate in real-world data, and on the other side, scaling to the `utility' regime (above 100 qubits) is affected by exponential concentration. In this work, we address said challenges by applying fidelity kernels to real-world data with unknown structure, related to the scheduling of a fleet of electric vehicles, and to synthetic data generated from the union of subspaces, which is then close to many relevant real-world datasets. Furthermore, we propose a novel error mitigation strategy specifically tailored for fidelity kernels, called Bit Flip Tolerance (BFT), to alleviate the exponential concentration in our utility-scale experiments. Our multiclass classification reaches accuracies comparable to classical SVCs up to 156 qubits, thus constituting the largest experimental demonstration of quantum machine learning on IBM devices to date. For the real-world data experiments, the effect of the proposed BFT becomes manifest on 40+ qubits, where mitigated accuracies reach 80%, in line with classical, compared to 33% without BFT. Through the union-of-subspace synthetic dataset with 156 qubits, we demonstrate a mitigated accuracy of 80%, compared to 83% of classical models, and 37% of unmitigated quantum, using a test set of limited size.

quant-ph↗

arXiv2024

Quantum multi-output Gaussian Processes based Machine Learning for Line Parameter Estimation in Electrical Grids

Gaussian process (GP) is a powerful modeling method with applications in machine learning for various engineering and non-engineering fields. Despite numerous benefits of modeling using GPs, the computational complexity associated with GPs demanding immense resources make their practical usage highly challenging. In this article, we develop a quantum version of multi-output Gaussian Process (QGP) by implementing a well-known quantum algorithm called HHL, to perform the Kernel matrix inversion within the Gaussian Process. To reduce the large circuit depth of HHL a circuit optimization technique called Approximate Quantum Compiling (AQC) has been implemented. We further showcase the application of QGP for a real-world problem to estimate line parameters of an electrical grid. Using AQC, up to 13-qubit HHL circuit has been implemented for a 32x32 kernel matrix inversion on IBM Quantum hardware for demonstrating QGP based line parameter estimation experimentally. Finally, we compare its performance against noise-less quantum simulators and classical computation results.

quant-ph↗

arXiv2024

Towards Less Greedy Quantum Coalition Structure Generation in Induced Subgraph Games

The transition to 100% renewable energy requires new techniques for managing energy networks, such as dividing them into sensible subsets of prosumers called micro-grids. Doing so in an optimal manner is a difficult optimization problem, as it can be abstracted to the Coalition Structure Generation problem in Induced Subgraph Games, a NP-complete problem which requires dividing an undirected, complete, weighted graph into subgraphs in a way that maximizes the sum of their internal weights. Recently, Venkatesh et al. (arXiv:2212.11372) published a Quantum Annealing (QA)-based iterative algorithm called GCS-Q, which they claim to be the best currently existing solver for the problem in terms of runtime complexity. As this algorithm makes the application of QA to the problem seem promising, but is a greedy one, this work proposes several less greedy QA-based approaches and investigates whether any of them can outperform GCS-Q in terms of solution quality. While we find that this is not the case yet on D-Wave hardware, most of them do when using the classical QBSolv software as a solver. Especially an algorithm we call 4-split iterative R-QUBO shows potential here, finding all optima in our dataset while scaling favorably with the problem size in terms of runtime. Thus, it appears to be interesting for future research on quantum approaches to the problem, assuming QA hardware will become more noise-resilient over time.

quant-ph↗

arXiv2024

Bridging the Gap to Next Generation Power System Planning and Operation with Quantum Computation

Innovative solutions and developments are being inspected to tackle rising electrical power demand to be supplied by clean forms of energy. The integration of renewable energy generations, varying nature loads, importance of active role of distribution system and consumer participation in grid operation has changed the landscape of classical power grids. Implementation of smarter applications to plan, monitor, operate the grid safely are deemed paramount for efficient, secure and reliable functioning of grid. Although sophisticated computations to process gigantic volume of data to produce useful information in a time critical manner is the paradigm of future grid operations, it brings along the burden of computational complexity. Advancements in quantum technologies holds promising solution for dealing with demanding computational complexity of power system related applications. In this article, we lay out clear motivations for seeking quantum solutions for solving computational burden challenges associated with power system applications. Next we present an overview of quantum solutions for various power system related applications available in current literature and suggest future topics for research. We further highlight challenges with existing quantum solutions for exploiting full quantum capabilities. Additionally, this paper serves as a bridge for power engineers to the quantum world by outlining essential quantum computation fundamentals for enabling smoother transition to future of power system computations.

quant-ph↗

arXiv2024

A Machine Learning Approach to Boost the Vehicle-2-Grid Scheduling

Electric Vehicles (EVs) are emerging as battery energy storage systems (BESSs) of increasing importance for different power grid services. However, the unique characteristics of EVs makes them more difficult to operate than dedicated BESSs. In this work, we apply a data-driven learning approach to leverage EVs as a BESS to provide capacity-related services to the grid. The approach uses machine learning to predict how to charge and discharge EVs while satisfying their operational constraints. As a paradigm application, we use flexible energy commercialization in the wholesale markets, but the approach can be applied to a broader range of capacity-related grid services. We evaluate the proposed approach numerically and show that when the number of EVs is large, we can obtain comparable objective values to CPLEX and approximate dynamic programming, but with shorter run times. These reduced run times are important because they allow us to (re)optimize frequently to adapt to the time-varying system conditions.

math.OC↗

arXiv2024

Quantum Optimization for the Future Energy Grid: Summary and Quantum Utility Prospects

In this project summary paper, we summarize the key results and use-cases explored in the German Federal Ministry of Education and Research (BMBF) funded project "Q-GRID" which aims to assess potential quantum utility optimization applications in the electrical grid. The project focuses on two layers of optimization problems relevant to decentralized energy generation and transmission as well as novel energy transportation/exchange methods such as Peer-2-Peer energy trading and microgrid formation. For select energy grid optimization problems, we demonstrate exponential classical optimizer runtime scaling even for small problem instances, and present initial findings that variational quantum algorithms such as QAOA and hybrid quantum annealing solvers may provide more favourable runtime scaling to obtain similar solution quality. These initial results suggest that quantum computing may be a key enabling technology in the future energy transition insofar that they may be able to solve business problems which are already challenging at small problem instance sizes.

quant-ph↗

arXiv2023

Incentivising Demand Side Response through Discount Scheduling using Hybrid Quantum Optimization

Demand Side Response (DSR) is a strategy that enables consumers to actively participate in managing electricity demand. It aims to alleviate strain on the grid during high demand and promote a more balanced and efficient use of (renewable) electricity resources. We implement DSR through discount scheduling, which involves offering discrete price incentives to consumers to adjust their electricity consumption patterns to times when their local energy mix consists of more renewable energy. Since we tailor the discounts to individual customers' consumption, the Discount Scheduling Problem (DSP) becomes a large combinatorial optimization task. Consequently, we adopt a hybrid quantum computing approach, using D-Wave's Leap Hybrid Cloud. We benchmark Leap against Gurobi, a classical Mixed Integer optimizer in terms of solution quality at fixed runtime and fairness in terms of discount allocation. Furthermore, we propose a large-scale decomposition algorithm/heuristic for the DSP, applied with either quantum or classical computers running the subroutines, which significantly reduces the problem size while maintaining solution quality. Using synthetic data generated from real-world data, we observe that the classical decomposition method obtains the best overall \newp{solution quality for problem sizes up to 3200 consumers, however, the hybrid quantum approach provides more evenly distributed discounts across consumers.

quant-ph↗

arXiv2023

Conditions for a quadratic quantum speedup in nonlinear transforms with applications to energy contract pricing

Computing nonlinear functions over multilinear forms is a general problem with applications in risk analysis. For instance in the domain of energy economics, accurate and timely risk management demands for efficient simulation of millions of scenarios, largely benefiting from computational speedups. We develop a novel hybrid quantum-classical algorithm based on polynomial approximation of nonlinear functions, computed through Quantum Hadamard Products, and we rigorously assess the conditions for its end-to-end speedup for different implementation variants against classical algorithms. In our setting, a quadratic quantum speedup, up to polylogarithmic factors, can be proven only when forms are bilinear and approximating polynomials have second degree, if efficient loading unitaries are available for the input data sets. We also enhance the bidirectional encoding, that allows tuning the balance between circuit depth and width, proposing an improved version that can be exploited for the calculation of inner products. Lastly, we exploit the dynamic circuit capabilities, recently introduced on IBM Quantum devices, to reduce the average depth of the Quantum Hadamard Product circuit. A proof of principle is implemented and validated on IBM Quantum systems.

quant-ph↗