Quantum computing is computing beyond classical computing based on quantum
phenomena such as superposition and entanglement. While quantum computing is still …

We introduce the notion of reinforcement quantum annealing (RQA) scheme in which an
intelligent agent searches in the space of Hamiltonians and interacts with a quantum …

Recovering sparse signals from linear measurements has demonstrated outstanding utility
in a vast variety of real-world applications. Compressive sensing is the topic that studies the …

Quantum Approximate Optimization Algorithm (QAOA) is one of the leading candidates for
demonstrating the quantum advantage using near-term quantum computers. Unfortunately …

The quantum approximate optimization algorithm (QAOA) by Farhi et al. is a quantum
computational framework for solving quantum or classical optimization tasks. Here, we …

We present multi-qubit correction (MQC) as a novel postprocessing method for quantum
annealers that views the evolution in an open-system as a Gibbs sampler and reduces a set …

We leverage the idea of a statistical ensemble to improve the quality of quantum annealing
based binary compressive sensing. Since executing quantum machine instructions on a …

We show how to leverage quantum annealers (QAs) to better select candidates in greedy
algorithms. Unlike conventional greedy algorithms that employ problem-specific heuristics …

The ultimate goal of any sparse coding method is to accurately recover from a few noisy
linear measurements, an unknown sparse vector. Unfortunately, this estimation problem is …

Quantum computers, leveraging the principles of quantum mechanics, hold the potential to
surpass classical computers in numerous applications, with implications across various …