Abstract
We develop a scheme of quantum reservoir state preparation, based on a quantum neural network framework, which takes classical optical excitation as input and provides desired quantum states as output. We theoretically demonstrate the broad potential of our scheme by explicitly preparing a range of intriguing quantum states, including single-photon states, Schrödinger's cat states, and two-mode entangled states. This scheme can be used as a compact quantum state preparation device for emerging quantum technologies.
Highlights
We develop a scheme of quantum reservoir state preparation, based on a quantum neural network framework, which takes classical optical excitation as input and provides desired quantum states as output
We theoretically demonstrate the broad potential of our scheme by explicitly preparing a range of intriguing quantum states, including single-photon states, Schrödinger’s cat states, and two-mode entangled states
This scheme can be used as a compact quantum state preparation device for emerging quantum technologies
Summary
We develop a scheme of quantum reservoir state preparation, based on a quantum neural network framework, which takes classical optical excitation as input and provides desired quantum states as output. While the applications of reservoir computing largely focused on classical tasks (even with quantum reservoirs [16]), the idea was recently brought fully into the quantum world in the form of quantum reservoir processing [17] It was shown as an efficient platform for quantum entanglement recognizing tasks and for performing complex quantum measurements (e.g., entropy, purity, and negativity). Performing these tasks involves processing the quantum nature of the input states, the output is still classical data obtained through measurements on the reservoir. To obtain the final output quantum state, the emitted field is processed with a suitable linear optical setup
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