QHA represents order-k token interactions in O(log k) quantum circuit depth, with an expressivity separation from classical self-attention and empirical gains on high-order parity and application tasks at reduced parameter count.
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Hybrid quantum-classical optimization for unit commitment uses Pauli-Correlation Encoding to solve multi-period schedules with up to 312 binary variables while satisfying load, ramping, and reserve constraints.
Random quantum circuits used as adversarial training data reduce successful attack rates on QML models for CIFAR-10 from 89.8% to 68.45% and for CINIC-10 from 94.23% to 78.68%.
citing papers explorer
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Higher-Order Token Interactions via Quantum Attention
QHA represents order-k token interactions in O(log k) quantum circuit depth, with an expressivity separation from classical self-attention and empirical gains on high-order parity and application tasks at reduced parameter count.
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Scaling Quantum Optimization for Unit Commitment via Pauli Correlation Encoding
Hybrid quantum-classical optimization for unit commitment uses Pauli-Correlation Encoding to solve multi-period schedules with up to 312 binary variables while satisfying load, ramping, and reserve constraints.
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Quantum Patches: Enhancing Robustness of Quantum Machine Learning Models
Random quantum circuits used as adversarial training data reduce successful attack rates on QML models for CIFAR-10 from 89.8% to 68.45% and for CINIC-10 from 94.23% to 78.68%.
- Quantum Adaptive Self-Attention for Quantum Transformer Models