Source-linked AI summary
Quantum Circuits Architecture
Giulio Chiribella, Giacomo Mauro D'Ariano, Paolo Perinotti
TL;DR
High-precision quantum information tasks can be reduced to executing transformations that depend on unknown transformations, while circuit architecture optimization remains a central challenge. The paper represents variable-subcircuit boards as quantum combs and reduces architecture design to convex optimization over a positive operator with linear constraints, simplifying cloning and storing-retrieving applications.
Problem
High-precision applications require executing transformations that depend on unknown parameters, while desired transformations may not be achievable exactly and circuit boards must therefore be optimized by a figure of merit.
Method
Quantum combs represent circuit boards with variable subcircuit slots and reduce architecture optimization to searching for a positive Choi operator subject to linear constraints.
Results
The method provides applications to optimal universal cloning of unknown unitary transformations and storage-retrieval of undisclosed unitaries, with optimal storing-retrieving using entanglement at storage and purely classical retrieval.
Takeaways & Limitations
Optimizing the comb operator can automatically determine whether circuit slots should be connected causally, in parallel, or in a combination, while enabling repeated execution without a quantum memory in the entangled storing scenario.
Abstract
from arXiv · showhide
We present a method for optimizing quantum circuits architecture. The method is based on the notion of "quantum comb", which describes a circuit board in which one can insert variable subcircuits. The method allows one to efficiently address novel kinds of quantum information processing tasks, such as storing-retrieving, and cloning of channels.