Success rates for linear optical generation of cluster states in coincidence basis
D. B. Uskov, P. M. Alsing, M. L. Fanto, L. Kaplan, A. M. Smith

TL;DR
This paper analyzes the success probabilities of linear optical methods for generating photonic cluster states, revealing that current approaches are suboptimal and proposing optimized schemes with higher success rates.
Contribution
The study provides a numerical optimization of fusion schemes, demonstrating that maximal success probabilities are higher than previously assumed for linear optical cluster state generation.
Findings
Optimal success probability for fusing n unentangled qubits is 1/2^(n-1)
Fusing m Bell pairs into a cluster has a success probability of 1/4^(m-1)
Current methods are significantly below the theoretical maximum success rates
Abstract
We report on theoretical research in photonic cluster-state computing. Finding optimal schemes of generating non-classical photonic states is of critical importance for this field as physically implementable photon-photon entangling operations are currently limited to measurement-assisted stochastic transformations. A critical parameter for assessing the efficiency of such transformations is the success probability of a desired measurement outcome. At present there are several experimental groups which are capable of generating multi-photon cluster states carrying more than eight qubits. Separate photonic qubits or small clusters can be fused into a single cluster state by a probabilistic optical CZ gate conditioned on simultaneous detection of all photons with 1/9 success probability of each gate. This design mechanically follows the original theoretical scheme of cluster state…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsNeural Networks and Reservoir Computing · Optical Network Technologies · Quantum Information and Cryptography
