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maximization algorithm » optimization algorithms (Expand Search), classification algorithm (Expand Search)
feature maximization » feature optimization (Expand Search), feature elimination (Expand Search)
process optimization » model optimization (Expand Search)
data feature » data figure (Expand Search), each feature (Expand Search), a feature (Expand Search)
wave process » same process (Expand Search), whole process (Expand Search), phase process (Expand Search)
binary data » primary data (Expand Search), dietary data (Expand Search)
binary wave » binary image (Expand Search)
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1
MCLP_quantum_annealer_V0.5
Published 2025“…Theoretical and applied experiments are conducted using four solvers: QBSolv, D-Wave Hybrid binary quadratic model 2, D-Wave Advantage system 4.1, and Gurobi. …”
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2
Contextual Dynamic Pricing with Strategic Buyers
Published 2024“…In this process, buyers can also strategically manipulate their feature data to obtain a lower price, incurring certain manipulation costs. …”
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3
Supplementary Material 8
Published 2025“…</li><li><b>Naïve bayes (NB): </b> A probabilistic classifier based on Bayes' theorem, suitable for predicting resistance phenotypes based on genomic features.</li><li><b>Linear discriminant Analysis (LDA) is a statistica</b>l approach that maximizes class separability. …”
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4
Image1_Applying the Hubbard-Stratonovich Transformation to Solve Scheduling Problems Under Inequality Constraints With Quantum Annealing.TIF
Published 2021“…Its current hardware implementation relies on D-Wave’s Quantum Processing Units, which are limited in terms of number of qubits and architecture while being restricted to solving quadratic unconstrained binary optimization (QUBO) problems. …”
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5
Image3_Applying the Hubbard-Stratonovich Transformation to Solve Scheduling Problems Under Inequality Constraints With Quantum Annealing.TIF
Published 2021“…Its current hardware implementation relies on D-Wave’s Quantum Processing Units, which are limited in terms of number of qubits and architecture while being restricted to solving quadratic unconstrained binary optimization (QUBO) problems. …”
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6
Image2_Applying the Hubbard-Stratonovich Transformation to Solve Scheduling Problems Under Inequality Constraints With Quantum Annealing.TIF
Published 2021“…Its current hardware implementation relies on D-Wave’s Quantum Processing Units, which are limited in terms of number of qubits and architecture while being restricted to solving quadratic unconstrained binary optimization (QUBO) problems. …”
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7
DataSheet1_Applying the Hubbard-Stratonovich Transformation to Solve Scheduling Problems Under Inequality Constraints With Quantum Annealing.pdf
Published 2021“…Its current hardware implementation relies on D-Wave’s Quantum Processing Units, which are limited in terms of number of qubits and architecture while being restricted to solving quadratic unconstrained binary optimization (QUBO) problems. …”
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8
Adaptive Inference for Change Points in High-Dimensional Data
Published 2021“…On the estimation front, we obtain the convergence rate of the maximizer of our test statistic standardized by sample size when there is one change-point in mean and <i>q</i> = 2, and propose to combine our tests with a wild binary segmentation algorithm to estimate the change-point number and locations when there are multiple change-points. …”