Application of a Resource Theory for Magic States to Fault-Tolerant Quantum Computing.
By Mark Howard, Earl T. Campbell. Physical Review Letters, 2016
410 citations recorded by the source corpus.
Motivated by their necessity for most fault-tolerant quantum computation schemes, we formulate a resource theory for magic states. First, we show that robustness of magic is a well-behaved magic monotone that operationally quantifies the classical simulation overhead for a Gottesman-Knill-type scheme using ancillary magic states. Our framework subsequently finds immediate application in the task of synthesizing non-Clifford gates using magic states. When magic states are interspersed with Clifford gates, Pauli measurements, and stabilizer ancillas-the most general synthesis scenario-then the class of synthesizable unitaries is hard to characterize. Our techniques can place nontrivial lower bounds on the number of magic states required for implementing a given target unitary. Guided by these results, we have found new and optimal examples of such synthesis.
Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition
View Paper: https://www.semanticscholar.org/paper/ad3410516a38a7656ea2c38472a1a872413f726e
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In Artificial Intelligence
- Andreas J. Winter
- Zi-Wen Liu
- Craig Gidney
- Earl T. Campbell
- Sergey Bravyi
- David Gosset
- Dan E. Browne
- Mark Howard
In Computer Vision and Pattern Recognition
Citations in the corpus
- Halving the cost of quantum addition (2017, background): “In [14] it is proven that a Toffoli gate requires at least 4 T gates, though the specific nature of the proof does not eliminate the possibility that n Toffolis could be implemented with fewer than 4n T gates for some large n.”
- Many-Body Quantum Magic (2020, methodology): “[7] Mark Howard and Earl Campbell, “Application of a resource theory for magic states to fault-tolerant quantum computing,” Phys.”
- Simulation of quantum circuits by low-rank stabilizer decompositions (2018, methodology): “s of simulation methods uses Monte Carlo sampling over quasiprobability distributions, where the distribution can be over either a discrete phase space [20,47,56], over the class of stabilizer states [32] or over stabilizer operations [7]. These quasiprobability methods are a natural method for simu”