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Rigetti has successfully validated a qubit-efficient optimization algorithm on its 9-qubit superconducting processor that maps classical variables into an entangled state using fewer physical qubits than traditional methods. This approach establishes a tunable trade-off between qubit count and circuit depth, demonstrating parameter concentration to reduce classical overhead while scaling continuous optimization on near-term hardware. The results provide a structural framework for addressing combinatorial problems constrained by current physical qubit limits.
Rigetti Researchers Demonstrate Qubit-Efficient Optimization Algorithm on Superconducting Hardware
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. The post Rigetti Researchers Demonstrate Qubit-Efficient Optimization Algorithm on Superconducting Hardware appeared first on Quantum Computing Report.
Researchers at Rigetti Computing (NASDAQ: RGTI) have developed and experimentally validated a qubit-efficient quantum optimization algorithm that reduces physical qubit requirements for solving large-scale…
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