Google Quantum AI is conducting collaborative research to identify problems where quantum computers outperform classical ones and design practical quantum algorithms. Recent endeavors involve studying enzyme chemistry, exploring alternatives for lithium-ion batteries, and modeling materials for inertial confinement fusion experiments. While practical quantum computers are not yet available, this research informs the hardware specifications required for efficient quantum algorithms in these applications in the future.
Google Quantum AI Presents 3 Case Studies to Explore Quantum Computing Applications
Quantum computing has the potential to transform various industries, but its practical applications for finite-sized problems are still being explored. Google Quantum AI is conducting collaborative research to identify problems where quantum computers outperform classical ones and design practical quantum algorithms. Here are some recent endeavors:
1. Studying enzyme chemistry
Google Quantum AI collaborated with Boehringer Ingelheim and Columbia University to explore the application of quantum computing in understanding the complex electronic structure of the enzyme family Cytochrome P450. By comparing classical and quantum methods, they demonstrated that quantum computers offer higher accuracy in resolving intricate chemistry. This indicates the need for several million physical qubits to achieve quantum advantage for this problem.
2. Exploring sustainable alternatives for lithium-ion batteries
Lithium-ion batteries are crucial but often rely on cobalt, which has environmental and ethical concerns. Researchers, in collaboration with BASF, QSimulate, and Macquarie University, developed quantum simulation techniques for lithium nickel oxide (LNO) as a cobalt alternative. Quantum computers showed efficient calculation of LNO’s energies, but improvements are needed in terms of qubit requirements.
3. Modeling materials for inertial confinement fusion experiments
Researchers are using quantum simulations to calculate the stopping power in warm, dense matter for inertial confinement fusion experiments. The quantum algorithm shows promise and outperforms classical alternatives. Although resource requirements are still uncertain, it falls within a reasonable range.
These case studies demonstrate the potential of quantum computing in solving complex problems and simulating physical systems. The research also highlights the need to develop quantum algorithms in preparation for future error-corrected quantum computers.
If you’re interested in exploring AI solutions for your company, consider the practical applications of quantum computing presented by Google Quantum AI. AI can redefine your work processes and provide automation opportunities. Make sure to define measurable KPIs and select AI tools that align with your needs. Implement AI gradually, starting with a pilot and expanding usage judiciously. For AI KPI management advice, you can connect with us at hello@itinai.com. Stay updated on leveraging AI by following our Telegram channel t.me/itinainews or Twitter @itinaicom.
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