Adaptive strategies for graph state growth in the presence of monitored errors

Abstract

Graph states (or cluster states) are the entanglement resource that enables one-way quantum computing. They can be grown by projective measurements on the component qubits. Such measurements typically carry a significant failure probability. Moreover, they may generate imperfect entanglement. Here we describe strategies to adapt growth operations in order to cancel incurred errors. Nascent states that initially deviate from the ideal graph states evolve toward the desired high fidelity resource without impractical overheads. Our analysis extends the diagrammatic language of graph states to include characteristics such as tilted vertices, weighted edges, and partial fusion, which arise from experimental imperfections. The strategies we present are relevant to parity projection schemes such as optical `path erasure' with distributed matter qubits.

4 pages, 4 figures. Typos corrected, nicer figures, neater notation and better read

References in corpus (6)

• Multi-party entanglement in graph states
• Resource-efficient linear optical quantum computation
• Robust creation of entanglement between ions in spatially separate cavities
• Brokered Graph State Quantum Computing
• Ground state approximation for strongly interacting systems in arbitrary dimension
• Potential and limits to cluster state quantum computing using probabilistic gates

Cited by in corpus (5)

• Novel schemes for measurement-based quantum computation
• Prospects for measurement-based quantum computing with solid state spins
• Effect of frequency mismatched photons in quantum information processing
• Atomic cluster state build up with macroscopic heralding
• Efficient growth of complex graph states via imperfect path erasure