ML system used to create complex and accurate simulation of the universe
The speed and accuracy of the project, called the Deep Density Displacement Model, or #D3M for short, wasn't the biggest surprise to the researchers. The real shock was that D3M could accurately simulate how the universe would look if certain parameters were tweaked β such as how much of the cosmos is dark matter β even though the model had never received any training data where those parameters varied.
Link: https://phys.org/news/2019-06-ai-universe-sim-fast-accurateand.html
#Physics #DL #simulation
The speed and accuracy of the project, called the Deep Density Displacement Model, or #D3M for short, wasn't the biggest surprise to the researchers. The real shock was that D3M could accurately simulate how the universe would look if certain parameters were tweaked β such as how much of the cosmos is dark matter β even though the model had never received any training data where those parameters varied.
Link: https://phys.org/news/2019-06-ai-universe-sim-fast-accurateand.html
#Physics #DL #simulation
phys.org
The first AI universe sim is fast and accurateβand its creators don't know how it works
For the first time, astrophysicists have used artificial intelligence techniques to generate complex 3-D simulations of the universe. The results are so fast, accurate and robust that even the creators ...
Applying machine learning optimization methods to the production of a quantum gas
#DeepMind developed machine learning techniques to optimise the production of a Bose-Einstein condensate, a quantum-mechanical state of matter that can be used to test predictions of theories of many-body physics.
ArXiV: https://arxiv.org/abs/1908.08495
#Physics #DL #BEC
#DeepMind developed machine learning techniques to optimise the production of a Bose-Einstein condensate, a quantum-mechanical state of matter that can be used to test predictions of theories of many-body physics.
ArXiV: https://arxiv.org/abs/1908.08495
#Physics #DL #BEC
ββTensorFlow Quantum
A Software Framework for Quantum Machine Learning
Introduce TensorFlow Quantum (TFQ), an open source library for the rapid prototyping of hybrid quantum-classical models for classical or quantum data.
TFQ provides the tools necessary for bringing the quantum computing and ML research communities together to control and model natural or artificial quantum systems; e.g. Noisy Intermediate Scale Quantum (NISQ) processors with ~50-100 qubits.
A quantum model has the ability to represent and generalize data with a quantum mechanical origin. However, to understand quantum models, two concepts must be introduced β quantum data and hybrid quantum-classical models.
Quantum data exhibits superposition and entanglement, leading to joint probability distributions that could require an exponential amount of classical computational resources to represent or store. Quantum data, which can be generated/simulated on quantum processors/sensors/networks include the simulation of chemicals and quantum matter, quantum control, quantum communication networks, quantum metrology, and much more.
Quantum models cannot use quantum processors alone β NISQ processors will need to work in concert with classical processors to become effective. As TensorFlow already supports heterogeneous computing across CPUs, GPUs, and TPUs, it is a natural platform for experimenting with hybrid quantum-classical algorithms.
To build and train such a model, the researcher can do the following:
β prepare a quantum dataset
β evaluate a quantum NN model
- sample or Average
β evaluate a classical NN model
β evaluate Ρost function
β evaluate gradients & update parameters
blog post: https://ai.googleblog.com/2020/03/announcing-tensorflow-quantum-open.html
paper: https://arxiv.org/abs/2003.02989
#tfq #tensorflow #quantum #physics #ml
A Software Framework for Quantum Machine Learning
Introduce TensorFlow Quantum (TFQ), an open source library for the rapid prototyping of hybrid quantum-classical models for classical or quantum data.
TFQ provides the tools necessary for bringing the quantum computing and ML research communities together to control and model natural or artificial quantum systems; e.g. Noisy Intermediate Scale Quantum (NISQ) processors with ~50-100 qubits.
A quantum model has the ability to represent and generalize data with a quantum mechanical origin. However, to understand quantum models, two concepts must be introduced β quantum data and hybrid quantum-classical models.
Quantum data exhibits superposition and entanglement, leading to joint probability distributions that could require an exponential amount of classical computational resources to represent or store. Quantum data, which can be generated/simulated on quantum processors/sensors/networks include the simulation of chemicals and quantum matter, quantum control, quantum communication networks, quantum metrology, and much more.
Quantum models cannot use quantum processors alone β NISQ processors will need to work in concert with classical processors to become effective. As TensorFlow already supports heterogeneous computing across CPUs, GPUs, and TPUs, it is a natural platform for experimenting with hybrid quantum-classical algorithms.
To build and train such a model, the researcher can do the following:
β prepare a quantum dataset
β evaluate a quantum NN model
- sample or Average
β evaluate a classical NN model
β evaluate Ρost function
β evaluate gradients & update parameters
blog post: https://ai.googleblog.com/2020/03/announcing-tensorflow-quantum-open.html
paper: https://arxiv.org/abs/2003.02989
#tfq #tensorflow #quantum #physics #ml
