Advances in Quantum Computing and Control

Seth Lloyd

Tzyh‐Jong Tarn, Narayan Ganesan

Due to the rapid advances made in Nano-Biotechnology, optical control of molecular dynamics and quantum computation, there is an increasing need to understand the fundamental structure, from the systems theoretical point of view, of the control and observation of quantum mechanical systems for designing advanced sensors and actuators. In this presentation we start with a discussion of the design, synthesis, and control of engineering problems and how the advances of technology helping to solve complex engineering problems. We proceed to discuss the recent trends in quantum computation and quantum computer. Then, we present the differences between the quantum control and measurement problems and the classical control and measurement problems. We conclude with a list of recent applications of quantum phenomena to build new sensors and other systems for information acquisition.

Mario Coccia, Saeed Roshani

Sara Mahmoudi Rashid

The integration of quantum computing with machine learning represents a significant frontier in computational research. This paper introduces an innovative approach that combines quantum control techniques with quantum machine learning (QML) to achieve notable advancements in performance and efficiency. The proposed methodology introduces a new quantum control framework designed to optimize quantum machine learning algorithms. This approach not only enhances computational speed but also improves the accuracy of quantum-enhanced support vector machines (SVMs) for classification tasks. The innovation lies in the application of advanced quantum control techniques, which are demonstrated to offer superior performance compared to existing methods. Additionally, the paper presents novel strategies for reducing power consumption in quantum computing systems. By incorporating dynamic quantum control, the proposed system achieves significant improvements in power efficiency, addressing one of the critical challenges in practical quantum computing applications. Benchmark tests across various datasets validate the effectiveness of the new method, showing advancements in processing efficiency and accuracy. The integration of machine learning creates new opportunities for advancements in research, potentially setting new standards in the field of quantum computing. These innovations underscore the potential for combining quantum control with machine learning to achieve groundbreaking improvements in computational capabilities and system efficiency, offering promising directions for future research and practical implementations.

Siddharth Kote, Dnyaneshwar Kshirsagar, Raj B. Kulkarni +3 more

Kip Nieman, Keshav Kasturi Rangan, Helen Durand

Quantum computing has advanced in recent years to the point that there are now some quantum computers and quantum simulators available to the public for use. In addition, quantum computing is beginning to receive attention within the process systems engineering community for directions such as machine learning and optimization. A logical next step for its evaluation within process systems engineering is for control, specifically for computing control actions to be applied to process systems. In this work, we provide some initial studies regarding the implementation of control on quantum computers, including the implementation of a single-input/single-output proportional control law on a quantum simulator with noise, evaluation of potential impacts of nondeterminism on theory for advanced control laws, and discussion of consequences of the way that entanglement works for next-generation manufacturing communication objectives.