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100 1 _ |a Demidik, Maria
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245 _ _ |a Expressive equivalence of classical and quantum restricted Boltzmann machines
260 _ _ |a London
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520 _ _ |a The development of generative models for quantum machine learning has faced challenges such as trainability and scalability. A notable example is the quantum restricted Boltzmann machine (QRBM), where non-commuting Hamiltonians make gradient evaluation computationally demanding, even on fault-tolerant devices. In this work, we propose a semi-quantum restricted Boltzmann machine (sqRBM), a model designed to overcome difficulties associated with QRBMs. The sqRBM Hamiltonian commutes in the visible subspace while remaining non-commuting in the hidden subspace, enabling us to derive closed-form expressions for output probabilities and gradients. Our analysis shows that, for learning a given distribution, a classical model requires three times more hidden units than an sqRBM. Numerical simulations with up to 100 units validate this prediction. With reduced resource demands, sqRBMs provide a feasible framework for early quantum generative models.
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700 1 _ |a Tüysüz, Cenk
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700 1 _ |a Piatkowski, Nico
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700 1 _ |a Grossi, Michele
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700 1 _ |a Jansen, Karl
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787 0 _ |a Demidik, Maria et.al.
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