Reinforcement Learning Generation of 4-Qubits Entangled States

TL;DR

This paper presents a reinforcement learning algorithm that constructs 4-qubit entangled states, covering all major entanglement classes, with potential applications in quantum experiments and understanding the universe.

Contribution

It introduces a novel AI-based method using Q-learning and a graphical tool (SLG) to generate and analyze 4-qubit entangled states across all key classes.

Findings

Successfully generated states for all nine entanglement families.

Synthesized quantum circuits are optimal for the chosen gate set.

The SLG tool helps understand entanglement features and gate roles.

Abstract

We have devised an artificial intelligence algorithm with machine reinforcement learning (Q-learning) to construct remarkable entangled states with 4 qubits. This way, the algorithm is able to generate representative states for some of the 49 true SLOCC classes of the four-qubit entanglement states. In particular, it is possible to reach at least one true SLOCC class for each of the nine entanglement families. The quantum circuits synthesized by the algorithm may be useful for the experimental realization of these important classes of entangled states and to draw conclusions about the intrinsic properties of our universe. We introduce a graphical tool called the state-link graph (SLG) to represent the construction of the Quality matrix (Q-matrix) used by the algorithm to build a given objective state belonging to the corresponding entanglement class. This allows us to discover the necessary connections between specific entanglement features and the role of certain quantum gates that the algorithm needs to include in the quantum gate set of actions. The quantum circuits found are optimal by construction with respect to the quantum gate-set chosen. These SLGs make the algorithm simple, intuitive and a useful resource for the automated construction of entangled states with a low number of qubits.