GQSPI recasts binary hypothesis testing on Gaussian bosonic signals as a polynomial approximation problem, achieving O(1/d log d) decision error for circuit depth d and robustness to dephasing noise.
It can be observed that the general form of the polynomial𝑃involve all possible summations (and subtractions) of the conditional displacement gates applied
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
fields
quant-ph 1years
2026 1verdicts
UNVERDICTED 1representative citing papers
citing papers explorer
-
Robust Quantum Algorithmic Binary Decision-Making on Gaussian Signals
GQSPI recasts binary hypothesis testing on Gaussian bosonic signals as a polynomial approximation problem, achieving O(1/d log d) decision error for circuit depth d and robustness to dephasing noise.