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Bayesian network structure learning using quantum generative models
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DOI：10.1007/s42484-024-00217-5.png)
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Abstract
En 中文
Bayesian network structure learning (BNSL) is a popular NP-hard optimization problem in the classical machine learning community. Given data, the network structure is optimized under the constraints of a directed acyclic graph and network scores using a cost function representing the constraints. In this study, we present BNSL using quantum generative models (QGMs) as a novel quantum machine learning application. QGMs are based on a quantum circuit composed of Pauli Y-rotation gates and controlled Pauli X or Z gates for quantum entanglement. Two real datasets are used to verify the comparative performance compared to classical counterpart GMs based on a three-layer neural network. For the training stage of the models, a hybrid quantum-classical framework is used. Due to the constraint-based cost function, classical data encoding is unnecessary, and the QGMs are trained so as to realize the desired output probability in one measurement. Simulation results show that QGMs achieve a comparative or better performance. In addition, we find a significant speed-up of the QGM compared to classical counterpart GMs. We believe that a combination of constraint-based cost functions and QGMs is useful to achieve such speed-ups.
Keywords:
Quantum generative models
Quantum machine learning
Hybrid quantum-classical machine learning
Bayesian network structure learning
Neural networks
Journal
Q
IF:
4.4
Papers:
439
Citations:
796
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Cited Papers
Cost function dependent barren plateaus in shallow parametrized quantum circuits.png)
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NATURE COMMUNICATIONSIF15.7
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