Quantum Neuromorphic Platform for Quantum State Preparation

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.

Opportunities in Quantum Reservoir Computing and Extreme Learning Machines

“…Beyond detection schemes, another example of a T Q is the preparation of desired quantum states [ 48,49 ] in ancilla systems interacting with different quantum substrates. In ref.…”

“…In ref. [48], for instance anti‐bunched and cat states have been reported while ref. [49] addresses maximally entangled states, NOON, W, cluster, and discorded states.…”

“…[ 43–45 ] In the context of QELM, both fermionic and bosonic setups have been proposed for instance in entanglement detection [ 46 ] or light field phase estimation. [ 47 ] Inspired by these neuromorphic approaches other quantum tasks have been reported such as quantum states preparation [ 48,49 ] or reconstruction. [ 50 ] Proof‐of‐principle experiments are also ongoing.…”

Atom‐Mediated Phonon Blockade and Controlled‐Z Gate in Superconducting Circuit System

“…However, some problems exist in these two mechanisms, for example, in the single phonon resonance mechanism, the strong phonon blockade can only be generated with the strong nonlinearity or strong coupling strength to phonons; in destructive interference mechanism, the emission correlation may rapidly oscillate. [ 7 ] In addition, most of the researches about blockade only concentrate on the generation of single excitation state without discussing their potential application. For example, how to perform quantum gate with the single excitation in the same system.…”

Realising and Compressing Quantum Circuits With Quantum Reservoir Computing

“…As it is easier to engineer a fixed and random network than a well controlled one, reservoir computing has been successfully implemented in a variety of physical systems [20][21][22][23]. Recently, the reservoir computing concept was brought to the quantum domain [24], using networks of quantum nodes [25,26] and the performance of specific non-classical tasks [27] including quantum state preparation [28] and tomography [29]. While these examples operate with quantum systems, they work with classical data either in the input or output and are far from being quantum computers, which should be able to implement unitary transformations (at least approximately) of a quantum state.…”