The Role of Symmetry in Quantum Query-to-Communication Simulation
Sourav Chakraborty, Arkadev Chattopadhyay, Peter H{\o}yer, Nikhil S., Mande, Manaswi Paraashar, Ronald de Wolf

TL;DR
This paper investigates the quantum communication complexity of composed functions, showing that the known logarithmic overhead in the BCW simulation is sometimes necessary and sometimes avoidable, depending on the symmetry properties of the function.
Contribution
It proves the tightness of the BCW simulation overhead for certain functions and introduces new techniques for analyzing quantum communication complexity based on symmetry.
Findings
Logarithmic overhead is not required for symmetric functions.
Efficient distributed noisy amplitude amplification for OR function.
Log n overhead remains necessary for some transitive functions.
Abstract
Buhrman, Cleve and Wigderson (STOC'98) showed that for every Boolean function f : {-1,1}^n to {-1,1} and G in {AND_2, XOR_2}, the bounded-error quantum communication complexity of the composed function f o G equals O(Q(f) log n), where Q(f) denotes the bounded-error quantum query complexity of f. This is achieved by Alice running the optimal quantum query algorithm for f, using a round of O(log n) qubits of communication to implement each query. This is in contrast with the classical setting, where it is easy to show that R^{cc}(f o G) is at most 2R(f), where R^{cc} and R denote bounded-error communication and query complexity, respectively. We show that the O(log n) overhead is required for some functions in the quantum setting, and thus the BCW simulation is tight. We note here that prior to our work, the possibility of Q^{cc}(f o G) = O(Q(f)), for all f and all G in {AND_2, XOR_2},…
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
TopicsQuantum Computing Algorithms and Architecture · Complexity and Algorithms in Graphs · Stochastic Gradient Optimization Techniques
