Making Quantum Computations Behave

Robert L. Kosut, HERSCHEL A. RABITZ

In this paper we promote the use of adaptive control for deducing the external control fields required to make a quantum computer behave as desired in an uncertain environment. Adaptive systems can be classified as indirect or direct. In an indirect adaptive control system, data is used to first. determine parameters in a system model. Then the model is used to determine the control parameters in order to achieve a desired goal.

Craig S. Lent

Bose-Einstein Condensates, Theresa Hecht, Prof Dr +1 more

Wafa Elmannai, Varun Pande, Ajay Shrestha +1 more

Over the last decade, many researchers have studied the implementation of quantum systems and the states of the quantum systems. Most of the focus has been on the quantum states strategy. Researchers have found it to be more advanced than classical strategies. Complementary quantum Observables interaction and information are the most important features that are refereed by Observables. This shows us how uniquely these features can define a packet of information in mathematical terms as a state. Also, it provides technological information that indeed captures the properties of quantum systems. In this paper, we demonstrate how to implement certain logical methods in quantum computing to obtain more accurate information than classical computing systems provide. This new methodology can make the computation more efficient. First, we examine the photons and consider the various spin states, and then we consider the direction of the spinning photons to handle more information. Next, we use the unique property of photons to behave both as a wave and a particle and exploit it by passing the photon through mirrors and beam splitters to demonstrate and achieve results that are not possible in the classical world.

Ran Raz

Mehdi Fazilat, Nadjet Zioui

The increasing convergence of quantum computing (QC) and advanced robotic control is giving rise to a new class of intelligent automation systems. Utilizing quantum-inspired computational principles, contemporary robotic platforms demonstrate greater optimization efficiency, superior perceptual processing, and improved decision-making performance. This survey outlines the principal quantum-oriented control methodologies utilized in robotics, with particular emphasis on approaches that incorporate Quantum Neural Networks (QNN), Quantum Machine Learning (QML), and Quantum-behaved Particle Swarm Optimization (QPSO). The investigation systematically reviews recent advances in quantum-assisted adaptive control, trajectory planning, and optimization frameworks, spotlighting their contributions to improved robustness, precision, and energy efficiency. By analyzing research in quantum reinforcement learning, distributed Noisy Intermediate-Scale Quantum (NISQ) computing, and hybrid quantum-classical approaches, this review clarifies how quantum principles are transforming both theoretical and practical aspects of robotic control. Likewise, several real-world implementations ranging from industrial manipulators to autonomous and cognitive robotic systems demonstrate the growing feasibility of quantum-enhanced decision-making. The review concludes that while remarkable progress has been made in quantum-inspired control for robotics, most existing studies remain constrained to simulations with restricted experimental validation on physical platforms. Addressing this gap will be essential in future research to transition from theoretical and simulated models toward hardware-level implementations, enabling the realization of scalable, fault-tolerant quantum control architectures.