Abstract
The concurrent rise of artificial intelligence and quantum information poses an opportunity for creating interdisciplinary technologies like quantum neural networks. Quantum reservoir processing, introduced here, is a platform for quantum information processing developed on the principle of reservoir computing that is a form of an artificial neural network. A quantum reservoir processor can perform qualitative tasks like recognizing quantum states that are entangled as well as quantitative tasks like estimating a nonlinear function of an input quantum state (e.g., entropy, purity, or logarithmic negativity). In this way, experimental schemes that require measurements of multiple observables can be simplified to measurement of one observable on a trained quantum reservoir processor.
Highlights
Quantum neural networks are emerging technologies that combine the features of artificial neural networks and quantum information technologies.[1,2,3] While neural networks are biologically inspired computing systems that learn from example to perform complex tasks in the area of “big data” and machine learning,[4,5,6,7] quantum information technologies exploit quantum effects for practical applications like quantum computation, quantum cryptography, and long-distance quantum communications
We find that the quantum reservoir processor (QRP) recognizes the entanglement of the same class of states as the training set, but is able to make predictions on states beyond the training class, including bipartite bound entangled states
We have presented a quantum reservoir processing platform for recognition of quantum entanglement and estimation of nonlinear functions of the input state
Summary
Quantum neural networks are emerging technologies that combine the features of artificial neural networks and quantum information technologies.[1,2,3] While neural networks are biologically inspired computing systems that learn from example to perform complex tasks in the area of “big data” and machine learning,[4,5,6,7] quantum information technologies exploit quantum effects for practical applications like quantum computation, quantum cryptography, and long-distance quantum communications. The interaction between these two promising fields led to many advances. They achieve this by using feedback connections not present in more traditional feedforward neural networks to generate an internal temporal dynamic behavior
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