Quantization of Materials Science

28 Quantization of Materials Science

28.1 Research and development of quantum computers in materials science

Quantum computing can help materials science in a variety of ways, including:

• Modeling the behavior of materials at the atomic and molecular level: This helps scientists understand the properties of materials and how to manipulate them. For example, quantum computers can be used to simulate the behavior of new materials, such as quantum materials, which have properties not found in traditional materials.

• Design new materials with desired properties: Quantum computers can be used to design new materials with specific properties, such as high strength, electrical conductivity, or thermal stability. This could lead to the development of new materials for a variety of applications such as energy storage, electronics and medicine.

• Finding new ways to synthesize materials: Quantum computers can be used to find new ways to synthesize materials, such as optimizing chemical reaction conditions. This could lead to the development of more efficient and cost-effective methods of producing materials.

• Improved understanding of existing materials: Quantum computers can be used to improve understanding of existing materials, for example by providing insights into their structures and properties. This could lead to the development of new ways to use these materials.

Overall, quantum computing has the potential to revolutionize the field of materials science, allowing scientists to design, synthesize and understand materials in ways that are currently impossible.

There are studies that are using quantum computers to discover new materials science. Here are some relevant papers:

1. "A Quantum Computing-driven Aid for New Material Design", author: Kenson Wesley R, Dr. Reena Monica P, published in 2023. This paper explores how the Vellore Institute of Technology in India uses a variational quantum eigensolver (VQE) to find the ground state energies of several molecules such as GeO2, SiO2, SiGe, ZrO2 and LiH. In addition, they developed a database containing data on the Hamiltonian quantities and ground state energies of elements and molecules.

2. "Advances and opportunities in materials science for scalable quantum computing", author: Vincenzo Lordi & John M. Nichol, published in 2021. This paper introduces advances and opportunities in materials science using quantum computing at Lawrence Liverpool National Laboratory in the United States and discusses materials science advances and obstacles for some major quantum computing platforms.

These are just a few examples of the many ways quantum computing is being used in materials science today. As quantum computers become more powerful, we expect to see even more exciting applications of this technology in the coming years.

28.2 How quantum computers could help the development of new batteries

The highly conductive new materials mentioned above refer to new battery development. Quantum computing can be used to simulate complex chemical reactions, which can help scientists design new battery materials and structures. These new batteries have the potential to have longer life, higher energy density and faster charging.

Some people may ask whether an iPhone or Apple Watch can last for more than a week without charging? This depends on a variety of factors, including the battery's energy density, power consumption and usage. If new batteries achieve higher energy density and lower power consumption, it's possible that an iPhone or Apple Watch won't have to be recharged for more than a week. In addition, the rapid charging and long range of electric vehicles also depend on the development of new batteries.

Here are some potential applications of quantum computing in battery development:

• Designing new battery materials: Quantum computing can be used to simulate complex chemical reactions, which can help scientists design new battery materials, such as those with higher energy density, longer cycle life, or faster charging speeds.

• Optimizing battery structure: Quantum computing can be used to optimize battery structure, such as the shape, size and spacing of electrodes. This improves battery performance and efficiency.

• Develop new battery charging technologies: Quantum computing can be used to develop new battery charging technologies, such as faster and safer charging methods.

These are just a few of the potential applications of quantum computing in battery development. As quantum computing technology continues to develop, we can expect to see more groundbreaking applications. The following is a paper from the Canadian company Xanadu Optical Quantum Computer:

"Simulating key properties of lithium-ion batteries with a fault-tolerant quantum computer", published in 2022. The paper explores how Toronto-based Xanadu Photonics Quantum Computers uses her qubitization-based quantum phase estimation algorithm to simulate key properties of lithium-ion batteries. The Canadian government has also funded Xanadu to cooperate with the European Volkswagen car manufacturer to explore new materials for electric vehicle batteries.

28.3 Quantum Batteries

"Quantum battery" and "using quantum computing to discover new battery materials" are two different concepts.

"Quantum battery" is a battery based on quantum effects, and its working principle is different from traditional batteries. Quantum batteries can store and release energy using effects such as quantum tunneling or quantum entanglement.

"Using Quantum Computing to Discover New Battery Materials" is an approach to designing and developing new battery materials through quantum computing. Quantum computing can be used to simulate the migration process of lithium ions in the electrode, electrochemical reactions in the electrolyte, etc., thereby designing new battery materials with excellent properties such as higher energy density, longer cycle life, and faster charge and discharge speeds. .

Specifically, the main differences between the two are as follows:

Quantum battery

• Features: Use quantum effects to store and release energy

• Advantages: higher energy density, longer cycle life, faster charge and discharge speed

• Disadvantages: Technical difficulty and high cost

Using quantum computing to discover new battery materials

• Features: Design and develop new battery materials through quantum computing

• Advantages: New battery materials with excellent properties can be discovered

• Disadvantages: Quantum computing technology is not mature enough

Overall, quantum batteries and using quantum computing to discover new battery materials are two different approaches that can be used to improve battery performance. Quantum batteries are a new technology and are still in the research stage. Using quantum computing to discover new battery materials is a more realistic way to improve the performance of existing batteries.

The 2022 paper "Quantum battery based on superabsorption" by Ueki and other scholars at the University of Tsukuba, Japan, believes that a quantum battery is a device that uses quantum effects to charge, and proposes a quantum battery whose charger system consists of N qubits. Take advantage of a collective effect called superabsorption. Importantly, the coupling strength between the quantum battery and charger system can be enhanced due to entanglement.

Adelaide University in Australia uses the principles of quantum mechanics to enhance the capabilities of quantum batteries. Their research also demonstrates the concept of super absorption, which is key to underpinning quantum batteries. This research, published in the journal Science Advances, takes a key step towards the realization of quantum batteries. To demonstrate the concept of superabsorption, the team built wafer-like microcavities of different sizes containing organic molecules. Each micro-resonant cavity is charged using laser.

Although the research at Australia's Adelaide University is no longer theoretical and has entered the experimental stage, it may still take several years or decades to turn quantum batteries into a commercial product. However, companies or research in the field of quantum computing have received a lot of investment in recent years. If the same investment funds can be obtained for quantum batteries, the time for its commercialization can definitely be shortened.

LFO microcavity schematic and experimental device at Adelaide University in Australia. (A) Microcavity composed of Lumogen-F orange (LFO) dispersed in a polystyrene (PS) matrix between distributed Bragg reflectors (DBR). (B) Normalized absorption (red) and photoluminescence (blue) spectra of a 1% concentration LFO film with the molecular structure shown in the inset. We operate near peak absorption/photoluminescence. (C) Angle-dependent reflectance of a 1% cavity, fitted to the cavity mode shown by the blue dashed line. (D) Laser pump pulse excites LFO molecules. The energy of the molecule is then measured with a probe pulse delayed by time t, from which we can determine the peak energy density (Emax), rise time () and peak charging power (Pmax). (E) Experimental setup for ultrafast transient reflectivity measurements. The output of a non-collinear optical parametric amplifier (NOPA) is split to produce a pump pulse (dark green) and a detection pulse (light green). A mechanical chopper is used to modulate the pump pulses to produce alternating pump detect pulses and detect only pulses.