Quantum Ai
Transformer-Powered Conditional-GQE Generates Quantum Circuits for Near-Perfect Combinatorial Optimization up to 10 Qubits
The conditional Generative Quantum Eigensolver (conditional-GQE) employs an encoder-decoder Transformer to generate context-aware quantum circuits for combinatorial optimization problems. Trained on problems up to 10 qubits, it achieves nearly perfect performance on unseen instances via high express…
Quantum Transformer Enables Potential Speedup for LLM Inference via Fault-Tolerant Quantum Computing
Researchers develop quantum subroutines for transformer components including self-attention, residual connections with layer normalization, and feed-forward networks, using efficient quantum implementations of Hadamard products and element-wise matrix functions. The algorithm outputs an amplitude-en…
Adiabatic Quantum SVMs Achieve Order-of-Magnitude Training Speedup with Comparable Accuracy
Adiabatic quantum computers train support vector machines by leveraging their optimization capabilities for quadratic unconstrained binary optimization problems. The quantum approach demonstrates time complexity an order of magnitude better than classical methods and matches Scikit-learn accuracies …
Quantum GANs with Variational Circuits Outperform Classical Counterparts in Small Molecule Generation
Hybrid quantum-classical GANs replace GAN components with variational quantum circuits (VQCs), demonstrating quantum advantages in de novo small molecule discovery for drug design. VQCs in the noise generator produce molecules with superior physicochemical properties and goal-directed benchmark perf…