5,085 research outputs found
Experimental Bayesian Quantum Phase Estimation on a Silicon Photonic Chip
Quantum phase estimation is a fundamental subroutine in many quantum
algorithms, including Shor's factorization algorithm and quantum simulation.
However, so far results have cast doubt on its practicability for near-term,
non-fault tolerant, quantum devices. Here we report experimental results
demonstrating that this intuition need not be true. We implement a recently
proposed adaptive Bayesian approach to quantum phase estimation and use it to
simulate molecular energies on a Silicon quantum photonic device. The approach
is verified to be well suited for pre-threshold quantum processors by
investigating its superior robustness to noise and decoherence compared to the
iterative phase estimation algorithm. This shows a promising route to unlock
the power of quantum phase estimation much sooner than previously believed
Theoretical Bounds in Minimax Decentralized Hypothesis Testing
Minimax decentralized detection is studied under two scenarios: with and
without a fusion center when the source of uncertainty is the Bayesian prior.
When there is no fusion center, the constraints in the network design are
determined. Both for a single decision maker and multiple decision makers, the
maximum loss in detection performance due to minimax decision making is
obtained. In the presence of a fusion center, the maximum loss of detection
performance between with- and without fusion center networks is derived
assuming that both networks are minimax robust. The results are finally
generalized.Comment: Submitted to IEEE Trans. on Signal Processin
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