23,784 research outputs found
The role of learning on industrial simulation design and analysis
The capability of modeling real-world system operations has turned simulation into an indispensable problemsolving methodology for business system design and analysis. Today, simulation supports decisions ranging
from sourcing to operations to finance, starting at the strategic level and proceeding towards tactical and
operational levels of decision-making. In such a dynamic setting, the practice of simulation goes beyond
being a static problem-solving exercise and requires integration with learning. This article discusses the role
of learning in simulation design and analysis motivated by the needs of industrial problems and describes
how selected tools of statistical learning can be utilized for this purpose
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
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