Unverified50% confidenceFactExact time
Quantum-JEPA adopts a sampled energy objective to optimize the quantum prior, so that the generated latent actions can both produce valid future predictions and cover multiple possible continuations.
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50%
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Long-term
Relevance
10/21/2026
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UnverifiedQuantum-JEPA uses a conditional quantum Born prior to learn the distribution of future representations, introducing a trainable quantum component into JEPA.75% similarUnverifiedThe core design of Quantum-JEPA is to use a quantum circuit to generate binary latent actions, where the bit strings of the measurement outputs are interpreted as latent actions, which are then mapped by a shared predictor to different future representations.74% similarUnverifiedQuantum algorithms such as Grover search and quantum amplitude estimation can be used to accelerate the value iteration/policy iteration solving process for MDPs.64% similarUnverifiedOn the controlled tasks, both the classical-prior and quantum-prior implementations outperformed the evaluated variational baselines in prediction validity and distribution fidelity.63% similarUnverifiedJEPA 借鉴了 BYOL 的动量编码器思想，同时吸收了 MAE 的掩码预测机制，但将重建目标替换为抽象嵌入63% similar
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