Journal
NPJ QUANTUM INFORMATION
Volume 6, Issue 1, Pages -Publisher
NATURE PORTFOLIO
DOI: 10.1038/s41534-020-0268-2
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Funding
- program Rita Levi Montalcini for young researchers
- EPSRC via the 'UK Quantum Communications Hub' [EP/M013472/1, EP/T001011/1]
- European Union via the project 'Continuous Variable Quantum Communications' (CiViQ) [820466]
- EPSRC [1949704, EP/T001011/1, EP/M013472/1] Funding Source: UKRI
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A fundamental model of quantum computation is the programmable quantum gate array. This is a quantum processor that is fed by a program state that induces a corresponding quantum operation on input states. While being programmable, any finite-dimensional design of this model is known to be nonuniversal, meaning that the processor cannot perfectly simulate an arbitrary quantum channel over the input. Characterizing how close the simulation is and finding the optimal program state have been open questions for the past 20 years. Here, we answer these questions by showing that the search for the optimal program state is a convex optimization problem that can be solved via semidefinite programming and gradient-based methods commonly employed for machine learning. We apply this general result to different types of processors, from a shallow design based on quantum teleportation, to deeper schemes relying on port-based teleportation and parametric quantum circuits.
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