Various industries have praised Quantum computing’s transformative potential, but the practicality of its applications for finite-sized problems remains a question. Google Quantum AI’s collaborative research aims to pinpoint problems where quantum computers outperform classical ones and design practical quantum algorithms. Recent endeavors include:
Studying enzyme chemistry.
Exploring sustainable alternatives for lithium-ion batteries.
Modeling materials for inertial confinement fusion experiments.
While practical quantum computers are not yet available, their ongoing work informs the hardware specifications required to run efficient quantum algorithms for these applications eventually.
Collaborating with Boehringer Ingelheim and Columbia University, Google Quantum AI explored the application of quantum computing in understanding the complex electronic structure of the enzyme family Cytochrome P450. These enzymes play a crucial role in drug metabolism. By comparing classical and quantum methods, they demonstrated that a quantum computer’s higher accuracy is essential for accurately resolving the intricate chemistry in this system. The study revealed that quantum advantage becomes increasingly pronounced with larger system sizes, ultimately indicating the need for several million physical qubits to achieve quantum advantage for this problem.
Lithium-ion batteries are essential for various applications but often rely on cobalt, which has environmental and ethical concerns. Researchers explored lithium nickel oxide (LNO) as a cobalt alternative. Understanding LNO’s properties is crucial. A paper titled “Fault-tolerant quantum simulation of materials using Bloch orbitals,” in collaboration with BASF, QSimulate, and Macquarie University developed quantum simulation techniques for periodic atomic structures like LNO. Their study found quantum computers could efficiently calculate LNO’s energies but currently require an impractical number of qubits, with hopes for future improvements.
Researchers explore quantum simulations for inertial confinement fusion experiments at extreme conditions. It focuses on calculating the stopping power in warm, dense matter, which is crucial for reactor efficiency. The quantum algorithm shows promise, with estimated resource requirements falling between previous applications. Although uncertainty remains, it outperforms classical alternatives that rely on mean-field methods, which introduce systematic errors in simulating such complex systems.
Researchers present a growing array of concrete applications for future error-corrected quantum computers in simulating physical systems, showcasing their potential to solve complex problems. Unlike static ground-state problems, quantum dynamics involves the evolution of quantum systems over time, aligning with the inherently dynamic nature of quantum computers. Collaborative research reveals that quantum algorithms can surpass approximate classical calculations in efficiency and accuracy. Developing these algorithms now ensures readiness for error-corrected quantum computers and dispels hyperbolic claims about their capabilities.
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The post Google Quantum AI Presents 3 Case Studies to Explore Quantum Computing Applications Related to Pharmacology, Chemistry, and Nuclear Energy appeared first on MarkTechPost.
Various industries have praised Quantum computing’s transformative potential, but the practicality of its applications for finite-sized problems remains a question. Google Quantum AI’s collaborative research aims to pinpoint problems where quantum computers outperform classical ones and design practical quantum algorithms. Recent endeavors include: While practical quantum computers are not yet available, their ongoing work informs
The post Google Quantum AI Presents 3 Case Studies to Explore Quantum Computing Applications Related to Pharmacology, Chemistry, and Nuclear Energy appeared first on MarkTechPost. Read More AI Shorts, Applications, Artificial Intelligence, Editors Pick, Staff, Tech News, Technology, Uncategorized, Convolutional Neural Network