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Found 6 papers, 3 papers with code
Quantum Multiple Kernel Learning
Our method leverages the power of deterministic quantum computing with one qubit (DQC1) to estimate the combined kernel for a set of classically intractable individual quantum kernels.
Reinforcement Learning via Replica Stacking of Quantum Measurements for the Training of Quantum Boltzmann Machines
The experimental results show that our technique is a promising method for harnessing the power of quantum sampling in reinforcement learning tasks.
Free energy-based reinforcement learning using a quantum processor
The experimental results show that our technique is a promising method for harnessing the power of quantum sampling in reinforcement learning tasks.
Learning in Quantum Control: High-Dimensional Global Optimization for Noisy Quantum Dynamics
Quantum control is valuable for various quantum technologies such as high-fidelity gates for universal quantum computing, adaptive quantum-enhanced metrology, and ultra-cold atom manipulation.
Designing High-Fidelity Single-Shot Three-Qubit Gates: A Machine Learning Approach
Three-qubit quantum gates are key ingredients for quantum error correction and quantum information processing.
High-Fidelity Single-Shot Toffoli Gate via Quantum Control
A single-shot Toffoli, or controlled-controlled-NOT, gate is desirable for classical and quantum information processing.
Quantum Physics Computational Physics
