Feature selection on quantum computers

Abstract

In machine learning, fewer features reduce model complexity. Carefully assessing the influence of each input feature on the model quality is therefore a crucial preprocessing step. We propose a novel feature selection algorithm based on a quadratic unconstrained binary optimization (QUBO) problem, which allows to select a specified number of features based on their importance and redundancy. In contrast to iterative or greedy methods, our direct approach yields higher-quality solutions. QUBO problems are particularly interesting because they can be solved on quantum hardware. To evaluate our proposed algorithm, we conduct a series of numerical experiments using a classical computer, a quantum gate computer, and a quantum annealer. Our evaluation compares our method to a range of standard methods on various benchmark data sets. We observe competitive performance.

A quantum feature selection framework via ground state preparation

“…However, this study did not offer a solution to the local optima problem. Another recent work [13] uses mutual information to address feature selection and also faces the challenge of local optima.…”

“…Quantum computing harnesses the principles of quantum mechanics, such as superposition and entanglement, to perform computations in parallel and exponentially faster than classical computers [9]. Recently, quantum computing has emerged as a promising paradigm for solving complex optimization problems [10], including feature selection [11][12][13][14][15]. Currently, there are quantum algorithms [16,17] that correspond the optimization problem to a Hamiltonian ground state finding problem, which is equivalent to finding a global minimum of a given optimization problem.…”

“…The problem of feature selection using quantum approaches is addressed in several papers [11][12][13]. These studies apply a quantum ground state preparation algorithm within classical feature selection algorithms to expedite the solution of the feature selection problem.…”

A novel feature selection method based on quantum support vector machine

“…Several studies have explored quantum feature selection approaches, including those based on a quadratic unconstrained binary optimization (QUBO) problem [7], Hamiltonian encoding and a ground state [8], quantum approximate optimization algorithm (QAOA) [5] and variational quantum optimization with black box binary optimization [6].…”

“…More recently, quantum computing has emerged as a promising platform for tackling computationally expensive combinatorial optimization tasks such as feature selection, offering innovative approaches to the challenges of dimensionality and data complexity [5][6][7][8]. This advancement complements the emergence of quantum machine learning, where quantum support vector machines (QSVM), demonstrate significant potential in leveraging quantum states for feature selection, transforming classical data into higher-dimensional Hilbert space for enhanced computational efficiency [9][10][11][12][13][14][15][16][17][18][19][20][21][22][23].…”