Optimizing Quantum Systems–An Overview

He Jianhao Li Lüzhou

András Czégel

Jincheng Zhang

Quantum Topology Optimization (QTO) is a novel optimization technique based on the principles of quantum topology, offering a promising avenue for addressing complex optimization problems. This paper explores the application of QTO to optimize quantum systems, highlighting its unique capabilities and potential advantages over conventional optimization methods. We present a core mechanism utilizing quantum topology to dynamically adjust system parameters, leading to improved performance. The paper details the methodology, provides a conceptual overview of the underlying physics, and offers a preliminary assessment of the method's effectiveness through theoretical analysis and simulation. The core claim is that QTO provides a more precise and robust optimization approach, particularly for systems exhibiting intricate topological structures.

Stefanie Günther, N. Anders Petersson, Jonathan L. DuBois

Quantum optimal control can be used to shape the control pulses for realizing unitary and non-unitary transformations of quantum states. These control pulses provide the fundamental interface between the quantum compiler and the quantum hardware. Most current software for quantum optimal control (e.g. Qutip [1] or Krotov [2]) is restricted to run on shared memory platforms, limiting their applicability to smaller quantum systems, in particular if interactions with the environment are taken into account. This paper gives an overview of the open-source code Quandary, which is designed to solve quantum control problems in larger open quantum systems modelled by Lindblad’s master equation. Implemented in C++, Quandary uses the message passing paradigm for distributed memory computers that enables scalability to large numbers of compute cores. Accompanied by numerical examples, this paper presents an overview on existing theoretical developments for open optimal quantum control realizing state-to-state transfer, unitary gate optimization as well as state-preparation, and presents the numerical tools and implementation aspect as realized in Quandary, for deployment on modern high-performance computing platforms.

Alberto Moraglio, Francisco Chicano

Jincheng Zhang

This paper introduces a novel dynamic quantum self-organization optimization framework. Traditional optimization methods often struggle to effectively address complex systems due to the inherent challenges of quantum mechanics. We propose a framework that utilizes quantum mechanics to automatically identify and optimize complex systems, leveraging a combination of quantum phenomena and optimization principles. The framework focuses on identifying optimal configurations within a system through iterative quantum self-organization, offering a fundamentally new approach to optimization challenges. The core claim of this work is that this framework provides a significantly more robust and effective method for optimizing complex systems compared to conventional techniques. We present a detailed overview of the proposed framework, its mathematical foundations, and preliminary results demonstrating its effectiveness in a specific, albeit simplified, case study.