Rigetti researchers have developed a qubit-efficient quantum optimization algorithm, detailed in Physical Review Applied, that uses fewer physical qubits than classical variables by mapping them into an entangled quantum state. This method, validated on a 9-qubit Rigetti superconducting processor using Sherrington-Kirkpatrick spin-glass models, offers a tunable trade-off between qubit count and circuit depth. The algorithm demonstrates parameter concentration, allowing for reuse of optimal circuit parameters and providing a framework to scale continuous optimization on near-term and early fault-tolerant quantum hardware.
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