Search Results for author:
Found 12 papers, 8 papers with code
TensorFlow Quantum: A Software Framework for Quantum Machine Learning
We introduce TensorFlow Quantum (TFQ), an open source library for the rapid prototyping of hybrid quantum-classical models for classical or quantum data.
Quantum advantage in learning from experiments
Quantum technology has the potential to revolutionize how we acquire and process experimental data to learn about the physical world.
Learning to learn with quantum neural networks via classical neural networks
Quantum Neural Networks (QNNs) are a promising variational learning paradigm with applications to near-term quantum processors, however they still face some significant challenges.
A quantum algorithm to train neural networks using low-depth circuits
The question has remained open if near-term gate model quantum computers will offer a quantum advantage for practical applications in the pre-fault tolerance noise regime.
Quantum Physics Disordered Systems and Neural Networks
A Universal Training Algorithm for Quantum Deep Learning
MoMGrad leverages Baqprop to estimate gradients and thereby perform gradient descent on the parameter landscape; it can be thought of as the quantum-classical analogue of QDD.
Quantum Physics
Quantum Approximate Optimization of Non-Planar Graph Problems on a Planar Superconducting Processor
For problems defined on our hardware graph we obtain an approximation ratio that is independent of problem size and observe, for the first time, that performance increases with circuit depth.
Quantum Physics
For Fixed Control Parameters the Quantum Approximate Optimization Algorithm's Objective Function Value Concentrates for Typical Instances
For higher depth circuits the numerics also show concentration and we argue for this using the Law of Large Numbers.
Quantum Physics
Power of data in quantum machine learning
These constructions explain numerical results showing that with the help of data, classical machine learning models can be competitive with quantum models even if they are tailored to quantum problems.
Observation of separated dynamics of charge and spin in the Fermi-Hubbard model
Strongly correlated quantum systems give rise to many exotic physical phenomena, including high-temperature superconductivity.
Quantum Physics
Exponential suppression of bit or phase flip errors with repetitive error correction
QEC also requires that the errors are local and that performance is maintained over many rounds of error correction, two major outstanding experimental challenges.
Quantum Physics
Learning shallow quantum circuits
Despite fundamental interests in learning quantum circuits, the existence of a computationally efficient algorithm for learning shallow quantum circuits remains an open question.
