IGO-QNN: Quantum Neural Network Architecture for Inductive Grover Oracularization

TL;DR

This paper introduces IGO-QNN, a quantum neural network that integrates Grover's algorithm into a trainable variational circuit, enabling quadratic speed-up in unstructured search problems without requiring explicit solution verifiers.

Contribution

The paper presents a novel quantum neural network architecture that incorporates Grover's search as a trainable component, broadening its application scope to problems lacking explicit solution verification.

Findings

Enables quadratic speed-up in unstructured search tasks.

Allows training of Grover's oracle from data examples.

Potential applications in deep reinforcement learning and computer vision.

Abstract

We propose a novel paradigm of integration of Grover's algorithm in a machine learning framework: the inductive Grover oracular quantum neural network (IGO-QNN). The model defines a variational quantum circuit with hidden layers of parameterized quantum neurons densely connected via entangle synapses to encode a dynamic Grover's search oracle that can be trained from a set of database-hit training examples. This widens the range of problem applications of Grover's unstructured search algorithm to include the vast majority of problems lacking analytic descriptions of solution verifiers, allowing for quadratic speed-up in unstructured search for the set of search problems with relationships between input and output spaces that are tractably underivable deductively. This generalization of Grover's oracularization may prove particularly effective in deep reinforcement learning, computer vision, and, more generally, as a feature vector classifier at the top of an existing model.