The Qiskit Transpiler Service beta release is now available on the IBM Quantum Premium Plan, providing users with AI and IBM cloud resources to speed up transpilation tasks.

We’re thrilled to announce that the beta release of the Qiskit Transpiler Service is now available to all IBM Quantum Premium Plan users. The Qiskit Transpiler Service leverages the resources of the IBM Cloud to provide users with the latest transpilation capabilities from the Qiskit SDK. It offers a Python library that helps users seamlessly integrate the service into their current Qiskit patterns and workflows. The new beta release invites users to experiment with new and improved AI-powered transpiler passes, which may be faster and produce better results than traditional methods. At the IBM Quantum Summit 2023, AI-powered transpiler passes for circuit routing and circuit synthesis were introduced, demonstrating their ability to produce shorter and shallower circuits than standard heuristic methods. These capabilities are now available to Premium Plan users as part of the Qiskit Transpiler Service beta release. Since the announcement, efforts have focused on stabilizing the system, enhancing reliability, expanding features, and boosting performance. The new AI-powered passes are designed to perform circuit routing and synthesis tasks more efficiently, significantly reducing two-qubit gate counts by an average of 42 percent. Reinforcement learning methods enable these AI-powered passes to find optimal solutions by interacting with the environment and maximizing cumulative rewards. To start using the Qiskit Transpiler Service, users need to upgrade to Qiskit SDK version 1.0 or later and install the qiskit-transpiler-service package. By integrating these AI-powered transpiler passes, users can improve their experiments and achieve better results.

Optimize Quantum Circuits with AI-Powered Transpiler Passes

Biologists are exploring the complex process of protein folding to understand how proteins quickly and elegantly fold into shapes that enable them to perform unique tasks, which is crucial for cell functionality and disease prevention.

Biologists are unfolding life’s molecular shapes by exploring the complex process of protein folding, which determines how proteins function and malfunction. This process is essential for every task performed by a cell. Misfolded proteins can lead to dysfunction and diseases, making it crucial to understand the folding process. Biochemist Christian Anfinsen hypothesized in the 1950s that an internal code within amino acid sequences directs protein folding. The quest to solve the protein folding problem has been ongoing for decades, involving experiments with individual proteins, computer programs, and the study of physics and chemistry at the atomic level. Recent breakthroughs with AI tools like Google DeepMind’s AlphaFold and RosettaFold All-Atom have significantly advanced protein structure prediction. However, understanding the dynamic structures of proteins within cells remains a challenge. Misfolded proteins can aggregate and cause neurodegenerative diseases like Alzheimer’s. Advances in technologies like cryo-electron microscopy and solid-state nuclear magnetic resonance are helping researchers study proteins more clearly. Understanding protein folding and misfolding is critical for developing treatments for various diseases and aging-related issues.

Biologists Are Unfolding Life’s Molecular Shapes

This article discusses how quantum technology and quantum computing can help reduce the carbon footprint of AI and other data centers and computers.

AI’s carbon footprint is a growing concern as the technology integrates into everyday applications, consuming significant energy. Generating one image with AI can take as much energy as fully charging a smartphone, according to researchers from Hugging Face and Carnegie Mellon University. The article emphasizes the need for standardized ways of measuring AI emissions and highlights quantum technology's potential to mitigate these impacts. By leveraging quantum computing, we can optimize energy usage in data centers, making AI more sustainable. Researchers like Sasha Luccioni and Jesse Dodge advocate for understanding the carbon footprint to make more informed decisions about AI usage. Additionally, running AI models in regions with cleaner power grids can further reduce their carbon footprint. Understanding and addressing the tech sector’s environmental impact is crucial for sustainable development.

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Optimize Quantum Circuits with AI-Powered Transpiler Passes

Biologists Are Unfolding Life’s Molecular Shapes

AI’s Carbon Footprint is Bigger Than You Think

AIView All Blogs

Optimize Quantum Circuits with AI-Powered Transpiler Passes

Biologists Are Unfolding Life’s Molecular Shapes

AI’s Carbon Footprint is Bigger Than You Think

AI View All Blogs