Reinforcement learning cuts gate count for 4-qubit states

A new reinforcement learning technique constructs disentangling circuits for states with up to 16 qubits, a key step toward more efficient quantum computing. Researchers from the University of Maryland, École Polytechnique Fédérale de Lausanne (EPFL), and the Max Planck Institute for the Physics of Complex Systems developed the approach. The work demonstrates that by accessing only two-qubit reduced density matrices, an agent can learn to control multiqubit entanglement without complete knowledge of the quantum state, making it directly applicable to current noisy intermediate-scale quantum (NISQ) devices. This method offers a largely unexplored paradigm in quantum interactive dynamics with potential for advances in quantum state preparation and compression. Once trained, it provides circuits from different initial states without further optimization. Analyzing optimal disentangling protocols, the researchers report a general circuit to prepare an arbitrary 4-qubit state using at most 5 two-qubit (10 CNOT) gates. Observations comprising all symmetrized two-qubit reduced density matrices enable a new approach to controlling multiqubit entanglement, bypassing the need for complete…