MPS generative model trained to sample Heston model paths for quantum path-dependent option pricing.
Encoding of matrix product states into quantum circuits of one-and two-qubit gates
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Trapped-ion quantum fine-tuning of AI models shows linear energy scaling and 24% better classification error than classical logistic regression or SVM baselines, with a projected energy break-even at 34 qubits.
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Time series generation for option pricing on quantum computers using tensor network
MPS generative model trained to sample Heston model paths for quantum path-dependent option pricing.
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Measuring Accuracy and Energy-to-Solution of Quantum Fine-Tuning of Foundational AI Models
Trapped-ion quantum fine-tuning of AI models shows linear energy scaling and 24% better classification error than classical logistic regression or SVM baselines, with a projected energy break-even at 34 qubits.