Related papers: Machine-Learning-Enhanced Quantum Optical Storage …
A hybrid interface of solid state single-photon sources and atomic quantum memories is a long sought-after goal in photonic quantum technologies. Here we demonstrate deterministic storage and retrieval of photons from a semiconductor…
In the last few years, quantum computing and machine learning fostered rapid developments in their respective areas of application, introducing new perspectives on how information processing systems can be realized and programmed. The…
We propose a quantum memory based on the pre-created long-lived macroscopic quantum coherence. It is shown that the proposed approach provides new physical properties and methods for retrieval of the signal light fields and improvement of…
The absorption of broadband photons in atomic ensembles requires either an effective broadening of the atomic transition linewidth, or an off-resonance Raman interaction. Here we propose a scheme for a quantum memory capable of storing and…
Machine Learning algorithms based on Brain-inspired Hyperdimensional(HD) computing imitate cognition by exploiting statistical properties of high-dimensional vector spaces. It is a promising solution for achieving high energy efficiency in…
Photonic qubits memories are essential ingredients of numerous quantum networking protocols. The ideal situation features quantum computing nodes that are efficiently connected to quantum communication channels via quantum interfaces. The…
The strong demand for quantum memory, a key building block of quantum network, has inspired new methodologies and led to experimental progress for quantum storage. The use of quantum memory for spatial multimode or image storage could…
Quantum machine learning algorithms have emerged to be a promising alternative to their classical counterparts as they leverage the power of quantum computers. Such algorithms have been developed to solve problems like electronic structure…
Quantum memories represent one of the main ingredients of future quantum communication networks. Their certification is therefore a key challenge. Here we develop efficient certification methods for quantum memories. Considering a…
Quantum memories are indispensible for establishing a long-distance quantum network. High-dimensional quantum memories enable a higher channel capacity compared to a quantum memory working in a two-dimensional space, and have a lower…
Quantum processors enable computational speedups for machine learning through parallel manipulation of high-dimensional vectors. Early demonstrations of quantum machine learning have focused on processing information with qubits. In such…
A new protocol of the optical quantum memory based on the resonant interactions of the multi atomic system with a cavity light mode is proposed. The quantum memory is realized using a controllable inversion of the inhomogeneous broadening…
Quantum memory is a key element for quantum repeaters and linear optical quantum computers. In addition to memory, repeaters and computers also require manipulating quantum states by means of unitary transformations, which is generally…
Machine Learning algorithms are extensively used in an increasing number of systems, applications, technologies, and products, both in industry and in society as a whole. They enable computing devices to learn from previous experience and…
Quantum machine learning is a rapidly advancing discipline that leverages the features of quantum mechanics to enhance the performance of computational tasks. Quantum reservoir processing, which allows efficient optimization of a single…
The use of multiplexed atomic quantum memories (MAQM) can significantly enhance the efficiency to establish entanglement in a quantum network. In the previous experiments, individual elements of a quantum network, such as the generation,…
Quantum memories could benefit many devices in quantum information processing. For a quantum to be useful in real-world applications, the quantum memory must have a high recall efficiency. Here we demonstrate an efficient (up to 80%)…
Continuous-time stochastic processes pervade everyday experience, and the simulation of models of these processes is of great utility. Classical models of systems operating in continuous-time must typically track an unbounded amount of…
Quantum communication networks are emerging as a promising technology that could constitute a key building block in future communication networks in the 6G era and beyond. These networks have an inherent feature of parallelism that allows…
Image classification, a pivotal task in multiple industries, faces computational challenges due to the burgeoning volume of visual data. This research addresses these challenges by introducing two quantum machine learning models that…