IBM has made developments in fusing AI and Quantum Computing. The field of quantum computing is rapidly evolving, with a growing focus on how these powerful machines can integrate with classical computing systems. Quantum computers hold immense potential as accelerators for complex calculations that even the most advanced supercomputers struggle with. Classical computers play a crucial role in pre- and post-processing stages of quantum algorithms, managing errors, refining results, and overseeing the entire processing task. Similar to the growing application of AI in various sectors, it’s natural to explore how AI can enhance quantum computing capabilities. Several companies are actively pursuing this goal.
While often used interchangeably, AI and quantum computing are different technologies. AI involves training and utilizing neural network models on classical computing platforms powered by CPUs, GPUs, and other traditional processing elements. Quantum computers, on the other hand, leverage alternative architectures like superconducting qubits to tackle highly complex problems using the principles of quantum mechanics. Despite their differences in hardware, software, and infrastructure, the integration of these two technologies is gaining traction, particularly to benefit quantum computing advancements.
IBM: A Leader in Quantum and AI
IBM stands out as a leader in the quantum computing race, continuously pushing the boundaries in hardware, software, and system technologies. They have already deployed functional quantum computers worldwide. Additionally, IBM is a powerhouse in the realm of AI with its renowned watsonx platform, known for its accomplishments since its 2011 Jeopardy win. Watsonx has since transformed into a scalable enterprise platform, encompassing AI studio, data management, governance, and assistant solutions. Now, IBM is bringing these two domains together to propel quantum computing and accelerate its widespread adoption.
How IBM is Integrating AI into Quantum Computing
In a recent discussion, IBM explained how they are incorporating AI technology into their Qiskit software to improve the user-friendliness of the SDK tools and OpenQASM3 (a quantum assembly language). They are utilizing their watsonx generative AI platform, powered by the company’s Granite AI model, to create digital assistants that provide developer support and quantum code assistance.
IBM is actively researching and developing new AI models to bolster other critical aspects. This includes circuit optimization, resource management, and improved error suppression, mitigation, and correction. The company also introduced of the Qiskit Code Assistant service with a Visual Studio Extension. They also plan to launch two quantum chatbots. One catering to developers and another for general users of IBM Quantum services.
AI-powered Enhancements
Circuit optimization is one area where AI models can be embedded as plugins within the Qiskit SDK through a transpiler service or combined with heuristic methods. According to IBM, this transpiler service facilitates superior mapping of abstract circuits to quantum ISA circuits, resulting in significant improvements:
- Up to 40% reduction in circuit size
- Enhanced circuit quality
- Processing speed improvements ranging from 2x to 5x
For resource management, IBM is developing AI solutions to:
- More accurately estimate quantum runtime
- Identify workloads with a high probability of failure
- Partition circuits for parallel processing, optimizing the utilization of both classical and quantum resources. This approach involves leveraging AI supercomputers.
The Road Ahead for Quantum Computing
IBM’s ambitious roadmap targets100 million gates by the end of this decade and 1 billion gates by 2033. This progress could speed up the emergence of heterogeneous data centers by the end of the decade. It can seamlessly combine the power of cutting-edge CPUs, AI accelerators, and QPUs (quantum processing units).
