aiqbench analysis
This paper proposes a quantum attention architecture that encodes Lorentz-invariant geometric features (via spacetime Clifford algebra grades) into pairwise qubit couplings for classifying hadronic top jets. The reported results are simulation-only, with 12 qubits and 144 trainable parameters, and they are benchmarked against a classical L-GATr-Slim baseline at matched size. The main takeaway for AI and quantum readers is that the gain comes mainly from explicit three-particle correlations, not from the higher-grade features, so the value of the physics-inspired design still needs validation on real hardware.
QGrAT: Quantum Grade-Aware Transformer for Jet Tagging
arXiv:2610.17351v1 Announce Type: cross Abstract: Lorentz symmetry and the three-prong structure of hadronic top decays provide physical guidance for designing compact quantum jet taggers. We introduce QGrAT (Quantum Grade-Aware Transformer), a quantum transformer that incorporates these structures into its interactions between constituent qubits. Geometric features constructed from the constituent four-momenta are organised by the grades of the spacetime Clifford algebra $C\ell(1,3)$, and their grade-specific inner products determine the pairwise attention couplings. For fixed constituents and co-transforming reference vectors, these couplings are Lorentz invariant, although the component-wise input encoding makes the complete classifier frame dependent. An auxiliary qubit introduces an explicit three-particle interaction through products of pairwise Minkowski inner products, while a trainable in-circuit readout converts the resulting quantum state into a classification score. We evaluate the architecture in state-vector simulation on the Top Tagging benchmarks. With ten constituents per jet, the configuration using grades $0\text{-}3$ and the three-particle ancilla requires twelve qubits and $144$ trainable parameters, reaching AUC $0.944 \pm 0.002$ and background rejection $1/\varepsilon_B = 35.7 \pm 2.4\ (96.8 \pm 7.7)$ at signal efficiency $\varepsilon_S = 0.50\ (0.30)$. This performance is competitive with the L-GATr-Slim baseline at matched constituent and parameter budgets. Adding the three-particle ancilla raises background rejection at $50\%$ signal efficiency from $19.9$ to $35.7$, whereas extending the representation to the tested grade-$4$ feature gives no consistent improvement. The study establishes a framework for incorporating spacetime geometry into quantum attention and identifies explicit three-particle correlations as the principal source of improved background rejection among the architectural variations tested.
Abstract:Lorentz symmetry and the three-prong structure of hadronic top decays provide physical guidance for designing compact quantum jet taggers. We introduce QGrAT (Quantum Grade-Aware Transformer), a quantum transformer that incorporates these structures into its interactions between constituent qubits. Geometric features constructed from the constituent four-momenta are organised by the grades of the spacetime Clifford algebra $C\ell(1,3)$, and their grade-specific inner products determine the pairwise attention couplings. For fixed constituents and co-transforming reference vectors, these couplings are Lorentz invariant, although the component-wise input encoding makes the complete classifier frame dependent. An auxiliary qubit introduces an explicit three-particle interaction through products of pairwise Minkowski inner products, while a trainable in-circuit readout converts the resulting quantum state into a classification score. We evaluate the architecture in state-vector simulation on the Top Tagging benchmarks. With ten constituents per jet, the configuration using grades $0\text{-}3$ and the three-particle ancilla requires twelve qubits and $144$ trainable parameters, reaching AUC $0.944 \pm 0.002$ and background rejection $1/\varepsilon_B = 35.7 \pm 2.4\ (96.8 \pm 7.7)$ at signal efficiency $\varepsilon_S = 0.50\ (0.30)$. This performance is competitive with the L-GATr-Slim baseline at matched constituent and parameter budgets. Adding the three-particle ancilla raises background rejection at $50\%$ signal efficiency from $19.9$ to $35.7$, whereas extending the representation to the tested grade-$4$ feature gives no consistent improvement. The study establishes a framework for incorporating spacetime geometry into quantum attention and identifies explicit three-particle correlations as the principal source of improved background rejection among the architectural variations tested.
arXiv:2610.17351 [hep-ph]
(or arXiv:2610.17351v1 [hep-ph] for this version)
https://doi.org/10.48550/arXiv.2610.17351 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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