Add to ORKGQuantum compiling and qubit manipulations can be efficiently solved by using deep reinforcement learning algorithms. The advantages range from lower computational time to real-time programming We review examples such as STIRAP and single qubits operation
In this paper, we present implementations of an annealing-based and a gate-based quantum computing a...
We demonstrate how quantum machine learning might play a vital role in achieving moderate speedups i...
Quantum error correction is widely thought to be the key to fault-tolerant quantum computation. Howe...
Quantum compiling and qubit manipulations can be efficiently solved by using deep reinforcement lear...
Quantum compilers are characterized by a trade-off between the length of the sequences, the precompi...
Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Com...
Machine learning techniques provide a remarkable tool for advancing scientific research, and this ar...
We use reinforcement learning techniques to optimize the Quantum Approximate Optimization Algorithm ...
The ability to prepare a physical system in a desired quantum state is central to many areas of phys...
Reinforcement learning is one of the fastest growing areas in machine learning, and has obtained gre...
Accurate and efficient preparation of quantum state is a core issue in building a quantum computer. ...
With advent of quantum internet, it becomes crucial to find novel ways to connect distributed quantu...
Machine learning with artificial neural networks is revolutionizing science. The most advanced chall...
With the advent of real-world quantum computing, the idea that parametrized quantum computations can...
Abstract Deep reinforcement learning is an emerging machine-learning approach that can teach a compu...
In this paper, we present implementations of an annealing-based and a gate-based quantum computing a...
We demonstrate how quantum machine learning might play a vital role in achieving moderate speedups i...
Quantum error correction is widely thought to be the key to fault-tolerant quantum computation. Howe...
Quantum compiling and qubit manipulations can be efficiently solved by using deep reinforcement lear...
Quantum compilers are characterized by a trade-off between the length of the sequences, the precompi...
Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Com...
Machine learning techniques provide a remarkable tool for advancing scientific research, and this ar...
We use reinforcement learning techniques to optimize the Quantum Approximate Optimization Algorithm ...
The ability to prepare a physical system in a desired quantum state is central to many areas of phys...
Reinforcement learning is one of the fastest growing areas in machine learning, and has obtained gre...
Accurate and efficient preparation of quantum state is a core issue in building a quantum computer. ...
With advent of quantum internet, it becomes crucial to find novel ways to connect distributed quantu...
Machine learning with artificial neural networks is revolutionizing science. The most advanced chall...
With the advent of real-world quantum computing, the idea that parametrized quantum computations can...
Abstract Deep reinforcement learning is an emerging machine-learning approach that can teach a compu...
In this paper, we present implementations of an annealing-based and a gate-based quantum computing a...
We demonstrate how quantum machine learning might play a vital role in achieving moderate speedups i...
Quantum error correction is widely thought to be the key to fault-tolerant quantum computation. Howe...
