Shadow Pauli Flow: Characterising Determinism in MBQCs involving Pauli Measurements Article Swipe
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· 2022
· Open Access
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· DOI: https://doi.org/10.48550/arxiv.2207.09368
· OA: W4286224497
We introduce a new characterisation of determinism in Measurement-Based Quantum Computing (MBQC). The one-way model consists in performing local measurements over a large entangled state represented by a graph. The ability to perform an overall deterministic computation requires a correction strategy because of the non-determinism of each measurement. The existence of such a correction strategy depends on the underlying open graph, which is a description of the resource state together with the basis of the performed measurements. GFlow is a well-known graphical characterisation of robust determinism in MBQC when every measurement is performed in some specific planes of the Bloch sphere. While Pauli measurements are ubiquitous in MBQC, GFlow fails to be necessary for determinism when a measurement-based quantum computation involves Pauli measurements. Pauli Flow was designed as a generalisation of GFlow to handle MBQC with Pauli measurements, and guarantees robust determinism, however, it has been shown more recently that it fails to be a necessary condition. Our contribution is twofold. First, we demonstrate that Pauli flow is actually necessary for robust determinism in a weaker sense: given an open graph, i.e. a resource state, a deterministic computation can be driven iff it has a Pauli flow. However, the Pauli flows do not reflect all the possible correction strategies over a particular resource state, and properties like measurement order or computational depth are not necessarily reflected by a Pauli flow. Thus, to characterise determinism in full generality, we introduce a further extension called Shadow Pauli Flow that we prove necessary and sufficient for robust determinism: An MBQC is robustly deterministic if and only if its correction strategy is consistent with a Shadow Pauli flow. Furthermore, we show that Shadow Pauli flow can be computed in polynomial time.
