12 research outputs found
Variational quantum algorithm for unconstrained black box binary optimization: Application to feature selection
We introduce a variational quantum algorithm to solve unconstrained black box binary optimization problems, i.e., problems in which the objective function is given as black box. This is in contrast to the typical setting of quantum algorithms for optimization where a classical objective function is provided as a given Quadratic Unconstrained Binary Optimization problem and mapped to a sum of Pauli operators. Furthermore, we provide theoretical justification for our method based on convergence guarantees of quantum imaginary time evolution.
To investigate the performance of our algorithm and its potential advantages, we tackle a challenging real-world optimization problem: . This refers to the problem of selecting a subset of relevant features to use for constructing a predictive model such as fraud detection. Optimal feature selection---when formulated in terms of a generic loss function---offers little structure on which to build classical heuristics, thus resulting primarily in ‘greedy methods’. This leaves room for (near-term) quantum algorithms to be competitive to classical state-of-the-art approaches. We apply our quantum-optimization-based feature selection algorithm, termed VarQFS, to build a predictive model for a credit risk data set with and input features (qubits) and train the model using quantum hardware and tensor-network-based numerical simulations, respectively. We show that the quantum method produces competitive and in certain aspects even better performance compared to traditional feature selection techniques used in today's industry
Abstracts of the 33rd International Austrian Winter Symposium : Zell am See, Austria. 24-27 January 2018.
Update of inflammasome activation in microglia/macrophage in aging and aging‐related disease
Variational quantum algorithm for unconstrained black box binary optimization: Application to feature selection
We introduce a variational quantum algorithm to solve unconstrained black box
binary optimization problems, i.e., problems in which the objective function is
given as black box. This is in contrast to the typical setting of quantum
algorithms for optimization where a classical objective function is provided as
a given Quadratic Unconstrained Binary Optimization problem and mapped to a sum
of Pauli operators. Furthermore, we provide theoretical justification for our
method based on convergence guarantees of quantum imaginary time evolution. To
investigate the performance of our algorithm and its potential advantages, we
tackle a challenging real-world optimization problem: feature selection. This
refers to the problem of selecting a subset of relevant features to use for
constructing a predictive model such as fraud detection. Optimal feature
selection -- when formulated in terms of a generic loss function -- offers
little structure on which to build classical heuristics, thus resulting
primarily in 'greedy methods'. This leaves room for (near-term) quantum
algorithms to be competitive to classical state-of-the-art approaches. We apply
our quantum-optimization-based feature selection algorithm, termed VarQFS, to
build a predictive model for a credit risk data set with 20 and 59 input
features (qubits) and train the model using quantum hardware and
tensor-network-based numerical simulations, respectively. We show that the
quantum method produces competitive and in certain aspects even better
performance compared to traditional feature selection techniques used in
today's industry