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Found 13 papers, 8 papers with code
Training neural networks with end-to-end optical backpropagation
Optics is an exciting route for the next generation of computing hardware for machine learning, promising several orders of magnitude enhancement in both computational speed and energy efficiency.
Hybrid training of optical neural networks
Optical neural networks are emerging as a promising type of machine learning hardware capable of energy-efficient, parallel computation.
Autoregressive neural-network wavefunctions for ab initio quantum chemistry
In recent years, neural network quantum states (NNQS) have emerged as powerful tools for the study of quantum many-body systems.
Aligning an optical interferometer with beam divergence control and continuous action space
Reinforcement learning is finding its way to real-world problem application, transferring from simulated environments to physical setups.
Adaptation of Quadruped Robot Locomotion with Meta-Learning
Animals have remarkable abilities to adapt locomotion to different terrains and tasks.
Comprehensive model and performance optimization of phase-only spatial light modulators
Several spurious effects are known to degrade the performance of phase-only spatial light modulators.
Optics Instrumentation and Detectors
Reinforcement Learning Enhanced Quantum-inspired Algorithm for Combinatorial Optimization
Quantum hardware and quantum-inspired algorithms are becoming increasingly popular for combinatorial optimization.
Backpropagation through nonlinear units for all-optical training of neural networks
Backpropagation through nonlinear neurons is an outstanding challenge to the field of optical neural networks and the major conceptual barrier to all-optical training schemes.
Emerging Technologies Signal Processing Optics
Quantum-inspired annealers as Boltzmann generators for machine learning and statistical physics
Quantum simulators and processors are rapidly improving nowadays, but they are still not able to solve complex and multidimensional tasks of practical value.
Exploratory Combinatorial Optimization with Reinforcement Learning
Our approach of exploratory combinatorial optimization (ECO-DQN) is, in principle, applicable to any combinatorial problem that can be defined on a graph.
Experimental quantum homodyne tomography via machine learning
Complete characterization of states and processes that occur within quantum devices is crucial for understanding and testing their potential to outperform classical technologies for communications and computing.
Versatile Digital GHz Phase Lock for External Cavity Diode Lasers
We present a versatile, inexpensive and simple optical phase lock for applications in atomic physics experiments.
Quantum Physics Instrumentation and Detectors
Diluted maximum-likelihood algorithm for quantum tomography
We propose a refined iterative likelihood-maximization algorithm for reconstructing a quantum state from a set of tomographic measurements.
Quantum Physics
