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Kumar Ghosh

Publications and source records attributed to Kumar Ghosh.

At least 19 recordsLinked to original sources
arXiv2026

Gravitational anomaly spectroscopy of a Kitaev spin liquid

The chiral central charge $c$ measures the gravitational anomaly of a two-dimensional topological phase. It fixes the heat current carried along the edge. So far, it has been accessed through thermal transport, where phonons complicate the signal, or through wave-function probes that need a pure ground state. Here we show that $c$ is a spectroscopic quantity of the edge. A weak modulation of the edge couplings that travel along the edge is absorbed according to the Virasoro algebra. Normalized by the response to a static change of the same couplings, the absorbed weights obey $D_n=2n\langle L_0\rangle+\frac{c}{12}(n^3-n)$, so that $\mathcal{C}\equiv2D_2-4D_1=c$ in any state of the ideal edge theory, thermal or not. The central charge thus appears in transition weights, although it cancels from excitation gaps. The ratio of line weights with and without a vison in the bulk, $Λ=4h_σ/c$, needs no calibration and fixes the conformal spin $h_σ$ of the non-Abelian Ising anyon. For the Kitaev honeycomb spin liquid we find $\mathcal{C}=0.49999$ and $h_σ=0.06250$ on 7168 spins. Calibrating with the shift of the measured line itself gives $c$ within about $1\%$ on 96 spins and within $1\%$ under $20\%$ random coupling errors. Interacting edge Majoranas in a fixed flux sector, treated with DMRG, keep $\mathcal{C}$ within $1.5\%$ while the edge velocity changes by $\pm13\%$. An exact simulation of a quantum-processor protocol, including the native Floquet circuit, returns $c$ within $3.5\%$ on 96 to 600 spins.

cond-mat.str-el↗
arXiv2026

Holonomy Separation and Anyonic Hair in AdS$_3$

Three-dimensional Einstein gravity and Chern-Simons-Higgs matter both admit Chern-Simons descriptions, but their holonomies encode different physics. We prove that charge conjugation reverses the vortex winding $n\to-n$ while leaving the metric and the gravitational connections pointwise unchanged, so every closed gravitational holonomy character, the BTZ charges, the Bañados data and the leading geometric mixed-state hierarchy are exactly even in $n$. On a fixed marked Euclidean replica filling the same change of variables gives $Z_q[n;\mathcal J_+]=Z_q[-n;\mathcal J_+]$, so the uncharged Rényi hierarchy, the Faulkner-Lewkowycz-Maldacena one-loop correction, the renormalized generalized entropy and the quantum extremal surfaces coincide for the two orientations. The complementary sector is fixed exactly: with an Aharonov-Bohm angle $μ$ conjugate to the entangling-region charge, $Z_q[n;μ]=Z_q[-n;-μ]$, so the symmetry-resolved spectrum obeys $\mathcal Z_q(\mathcal Q;n)=\mathcal Z_q(-\mathcal Q;-n)$ and every odd charged cumulant reverses with the winding. The orientation is therefore stored entirely in the charge asymmetry of the entanglement spectrum, and is read at topological order by a linked charged matter line. The pair is a bulk logical qubit on which the neutral gravitational-replica algebra acts as a multiple of the identity. Exact BTZ cross sections show that the purification plateau and the entanglement wedge disconnection transition are orientation blind, the latter closing with the cutoff-independent jump $(c/3)\log(1+\sqrt2)$. Numerical Bogomolny solutions verify odd flux and even energy and spin; used as linearized charge-matching data they give the even asymptotic shifts $δ\widehat M=16πGv^2|n|$ and $|δ\widehat j|=16πGv^2n^2/(mL)$ and, conditionally, a finite continuous winding envelope.

hep-th↗
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--Pérot 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 $κ_{xy}/T=(π^2k_B^2/6h)\,C_{\rm BdG}$ 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

Reading Topological Hair from Black-Hole Entanglement

Black-hole hair can affect boundary mixed-state entanglement through local stress-energy and through topological information invisible to the classical metric. We separate these channels for a Nielsen--Olesen vortex on a nonrotating BTZ black hole. A superselection theorem shows that Shannon sector entropy cancels from the Markov gap, while standard $U(1)$ symmetry-resolved reflected entropy in a thermal $U(1)_k$ CFT is equipartitioned at leading order, excluding a universal $\log|n|$ term in the imbalance-resolved gap. We therefore define a Wilson-threaded reflected moment in a probe $U(1)_k$ Chern--Simons completion coupled to the compact vortex-flux class. In a reflected-replica sector with linking number $ν$, its normalised phase is $2πκpnν/k$, where $κ\in\mathbb Z$ is the mixed topological coupling; for $\gcd(κν,k)=1$, a discrete Fourier transform reconstructs $n\bmod k$. The RT connectivity transition switches the specified linked contour on or off, whereas the independent scale $\ell_\star r_+/L^2=1.128378\ldots$ determines the direction of the vortex-induced shift of that transition. The charged-moment modulus and the ordinary Markov gap remain geometric observables and are computed from a horizon-anchored Einstein-Abelian-Higgs solution with a complete first-variation kernel including the motion of the entanglement-wedge cross-section endpoints. This phase and modulus separation distinguishes topological hair from gravitational dressing without assigning an unsupported winding-dependent entropy.

hep-th↗
arXiv2026

Parity anomaly governs the thermal Hall response of chiral superconductivity in rhombohedral graphene above and below $T_c$

Chiral superconductivity has recently been confirmed in rhombohedral tetra- and pentalayer graphene near the BCS--BEC crossover, but no theory tells the experimentalist what thermal Hall response to expect, or why any signal should persist above the phase-coherence temperature $T_c$. We show that the parity anomaly of $(2{+}1)$-dimensional field theory fixes the answer exactly, at all temperatures, with no free parameters: $κ_{xy}/T = (π^2 k_B^2/6h)\,C_{\rm BdG}\,\tanh[Δ(T)/(2k_BT)]$, where $C_{\rm BdG}$ is the BdG Chern number and $Δ(T)$ is the fermionic excitation gap. The BCS--BEC two-gap relation $Δ^2 = Δ_{\rm sc}^2 + Δ_{\rm pg}^2$ makes the same formula govern both the condensate and pseudogap regimes, so the signal onsets at the pair-formation temperature $T^{*}$ rather than at $T_c$. Coleman--Hill non-renormalization and the $c_1 = 0$ theorem protect this result against interactions and finite-size artefacts. Three independent numerical validations confirm the topological input at machine precision (FHS Chern numbers, Wilson-loop $c_1 = 0$ test) and at the many-body level (DMRG on 28 converged ground states, including the real-space $p+ip$ signature $\arg\mathcal{A}_y - \arg\mathcal{A}_x = -π/2$ recovered to $10^{-14}$). The theory delivers an immediate falsifiable test that requires no new experimental apparatus: the sign of $κ_{xy}/T$ below $T_c$ must equal the sign of the anomalous Hall resistance $R_{xy}$ already measured above $T_c$, and four further predictions accessible by dilution-refrigerator nano-calorimetry on existing devices.

cond-mat.supr-con↗
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 $ν=2/3$, $β=31/96$, and $η=35/864$ 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 $ν(n)$ satisfies it. A perturbative $1/n^2$ expansion yields $ν(3) = 0.7493$ (benchmark: $0.7112$), and propagation through exact scaling relations gives $β(3) = 0.3797$ (benchmark: $0.3689$) and $γ(3) = 1.489$ (benchmark: $1.396$), without introducing additional parameters. The framework naturally extends to non-integer spin, producing the prediction $ν(2.5) = 0.7143$ 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 $Δ_{\rm pg}(T)$ enters the parity-odd fermion determinant identically to a condensate mass, yielding the exact parameter-free formula $κ_{xy}/T = (π^2 k_B^2/6h)\,C\,\tanh[Δ_{\rm pg}(T)/(2k_BT)]$, where $C$ is the Chern number of the chiral pairing channel and $Δ_{\rm pg}(T)$ 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 $ν_s$. 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 $J$--$J_χ$ Hamiltonian ($ν_s=-0.500\pm0.011$). 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

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, $Γ_{\rm eff}$, 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

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

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 $α\in\{1.0,\,0.7,\,0.5,\,0.3\}$, where the analytically exact classical threshold is ${g_c^{\mathrm{class}}}(α)=2α/3$. The suppression ratio $r(α)$ exhibits a sharp two-regime structure: the three quasi-1D geometries ($α\leq0.7$) are mutually consistent with a universal plateau $\bar{r}=0.450$ ($χ^2/\mathrm{dof}=1.10$, $p=0.33$), 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 $α^*\approx0.7$. Inner Binder cumulant pairs, which converge fastest to the thermodynamic limit, resolve $r(1.0)\approx0.412$ and a step $Δr=0.038\pm0.015$ from the quasi-1D plateau. A four-point linear fit $r(α)=0.494-0.063\,α$ summarises both regimes; its $α\to0$ 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 $α\in[α^*,1]$. Two sequential blind predictions, confirmed at $0.2σ$ and $0.7σ$ 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 $N^{-3/4}$ ($γ= 0.739 \pm 0.036$), the chaos exponent $θ= 0.51 \pm 0.02$ ($R^2 = 0.989$) confirms mean-field universality, the overlap distribution exhibits hierarchical structure ($σ_q = 0.19$), 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 $ΔD = 0.1$. 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

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↗