Abstract In this paper, we investigate the use of variational quantum algorithms for simulating the thermodynamic properties of dinuclear metal complexes. Our study highlights the potential of quantum computing to transform advanced simulations and provide insights into the physical behavior of quantum systems. The results demonstrate the effectiveness of variational quantum algorithms in simulating thermal states and exploring the thermodynamic properties of low-dimensional molecular magnetic systems. The findings from this research contribute to broadening our understanding of quantum systems and pave the way for future advancements in materials science through quantum computing.
Abstract In this paper, we investigate the use of variational quantum algorithms for simulating the thermodynamic properties of dinuclear metal complexes. Our study highlights the potential of quantum computing to transform advanced simulations and provide insights into the physical behavior of quantum systems. The results demonstrate the effectiveness of variational quantum algorithms in simulating thermal states and exploring the thermodynamic properties of low-dimensional molecular magnetic systems. The findings from this research contribute to broadening our understanding of quantum systems and pave the way for future advancements in materials science through quantum computing.
Abstract {The urgent demand for sustainable energy storage devices necessitates the development of novel conceptual and technological paradigms that extend beyond the constraints imposed by classical thermodynamic limits. in order to present the material innovations required to enable viable room-temperature quantum technologies, this Review outlines the potential of dinuclear Cu(II) complexes as promising model platforms for the development of room-temperature quantum batteries.} We address how the quantum properties of these dinuclear materials can be harnessed to store and extract energy, and how they can be handled and engineered by the management of the intra-molecular magnetic coupling and external thermodynamic conditions. {Through macroscopic magnetometric mappings, we present how the maximum extractable work (ergotropy) can be directly quantified without invasive quantum tomography. Furthermore, we review the operational simulation of these materials using quantum computing approaches using the Variational Quantum Thermalizer (VQT) algorithm on Noisy Intermediate-Scale Quantum (NISQ) devices. Finally, we provide an outlook on future research directions, highlighting the critical material engineering and quantum error mitigation strategies required to transition these coordination compounds from theoretical models to functional macroscopic solid-state quantum energy storage devices at room temperature.}
Understanding the thermodynamic properties of quantum systems is essential for developing energy-efficient quantum technologies. In this regard, this work explores the application of quantum computational methods to study the quantum properties and work extraction processes in a dinuclear quantum battery model. Our results demonstrate that variational quantum algorithms can reproduce key trends in experimental data, making it possible to analyze the effectiveness of the presented protocol in noisy environments and providing insights into the feasibility of quantum batteries in near-term devices. We have shown that the presence of a noisy environment hinders the accuracy of the evaluation of the amount of energy stored in the system. Additionally, we analyze the work extraction precision, revealing that although the system can store energy at room temperature, the protocol is highly precise only at low temperatures, and its accuracy at ambient conditions remains limited, compromising its usability.
This study investigates the thermal properties of the repulsive Fermi-Hubbard model with chemical potential using variational quantum algorithms, crucial in comprehending particle behavior within lattices at high temperatures in condensed matter systems. Conventional computational methods encounter challenges, especially in managing chemical potential, prompting exploration into Hamiltonian approaches. Despite the promise of quantum algorithms, their efficacy is hampered by coherence limitations when simulating extended imaginary time evolution sequences. To overcome such constraints, this research focuses on optimizing variational quantum algorithms to probe the thermal properties of the Fermi-Hubbard model. Physics-inspired circuit designs are tailored to alleviate coherence constraints, facilitating a more comprehensive exploration of materials at elevated temperatures. Our study demonstrates the potential of variational algorithms in simulating the thermal properties of the Fermi-Hubbard model while acknowledging limitations stemming from error sources in quantum devices and encountering barren plateaus.
This paper explores the potential of algorithmic quantum simulation to accelerate the study and analysis of complex, multi-dimensional dynamical systems. We introduce a novel quantum algorithm designed to efficiently explore the state space of these systems, offering a significant advantage over classical simulation techniques, particularly for systems with intricate dynamics and high dimensionality. The core mechanism focuses on utilizing quantum properties to overcome computational bottlenecks and enable more rapid simulations. We outline the algorithm's architecture, key quantum operations, and potential applications within diverse fields such as physics, chemistry, and materials science. The paper concludes with a discussion of future research directions and the expected impact of this approach on the field of quantum computation.
Quantum simulation, utilizing quantum computers to model complex quantum systems, offers transformative capabilities in chemistry and materials science. This review explores both digital and analog quantum simulation methodologies, highlighting their advances, applications, and future directions. Digital methods such as the Variational Quantum Eigensolver (VQE) and Quantum Approximate Optimization Algorithm (QAOA) excel in calculating molecular properties and optimizing material designs, leveraging quantum bits to surpass classical computing limitations. Analog techniques simulate quantum systems through physical analogies, offering insights into emergent quantum behaviors in systems like cold atoms and trapped ions. In East Africa, quantum simulation research is burgeoning, with institutions like the African Institute for Mathematical Sciences (AIMS) and the University of Nairobi leading initiatives. Challenges such as limited quantum hardware access and skill gaps necessitate robust infrastructure and educational programs. Future directions envision regional centers of excellence, collaborative ecosystems, and innovative applications addressing local challenges. Quantum simulation stands poised to revolutionize scientific inquiry, technological innovation, and regional development, positioning East Africa at the forefront of quantum research's transformative impact on society. Keywords: Transformative Potential, Quantum Simulation, Chemistry, Materials Science.
Quantum simulation is one of the brightest near term quantum computing applications and it promises the possibility to simulate complex quantum systems that cannot be simulated by classic computers. The current paper introduces a detailed discussion of quantum simulation methods that are being run with the Cirq framework by Google, along with a detailed discussion in terms of their application in various fields. We explore the principles of quantum simulation theory and create reproducible codes of molecular modeling, materials science, financial analysis. We use variational quantum algorithms, specifically Variational Quantum Eigensolver (VQE) to simulate molecular structures and quantum chemistry. We also consider quantum solution to materials science problems and illustrate quantum Monte Carlo solution of financial problems. The results of the experiment, which are achieved by means of simulation, indicate that our Cirq implementations can simulate target systems with high accuracy, but the practical quantum advantage depends on the developments in hardware. Our results point out the existing capacities and limitations of quantum simulation on near term quantum devices, as well as point out future directions of research in this emerging field.