Quantum AI: Harnessing the Power of Quantum Computing for AI
Happy Friday! I’m Ylli Bajraktari, CEO of the Special Competitive Studies Project. In this week’s edition of our newsletter, SCSP’s Olivia Armstrong and the Future Technology Platforms team explore the intersection of quantum computing and artificial intelligence and how these technologies will power each other and the future of compute.
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Quantum AI: Harnessing the Power of Quantum Computing for AI
The race to harness the transformative power of quantum computing has become a critical front in the broader technological competition between the United States and its strategic rivals. As quantum capabilities start to mature, they will fundamentally reshape innovation across national security, the supply chain, defense, and most promisingly, how the scientific community conducts fundamental research. At the heart of this quantum revolution lies the promise of a convergence of two major technologies: Quantum AI.
By integrating the unique capabilities of quantum computing with rapid advancements in artificial intelligence, researchers are uncovering pathways to overcome some of the most vexing challenges facing both technologies. The potential payoffs are immense – from dramatically accelerating drug development to unlocking energy efficiency gains.
The United States and China have emerged as the global leaders in the quantum computing race, each pursuing distinct innovation strategies. The U.S. quantum ecosystem is characterized by a diverse array of public-private centers of excellence, from the national hub Elevate Quantum in Colorado to industry leaders like IBM, PsiQuantum, and Atlantic Quantum. In contrast, China's quantum initiatives are largely concentrated within government-affiliated institutions and national laboratories such as the University of Science and Technology of China (USTC) and the Chinese Academy of Science’s Center for Quantum Information and Quantum Physics. The Hefei National High-tech Industry Development Zone, in particular, serves as a central hub for the country's quantum computing efforts. As the Quantum-AI frontier continues to evolve rapidly, the ability to effectively harness these transformative technologies will be a key determinant of future technological supremacy.
Quantum Computing
At the core of quantum computing - as a subset of the quantum information science sector (QIS) - lies the use of qubits – quantum analogues of the classical bits that underpin conventional computing. Unlike classical bits, which can exist in a state of 0 or 1, qubits can exist in superposition which allows them to represent multiple states simultaneously. This quantum mechanical property unlocks exponential performance gains for certain types of algorithms and simulations.
However, realizing the full potential of quantum computing remains an immense technical challenge. Current quantum systems are highly fragile, requiring precise control and error correction techniques to maintain the quantum states necessary for computation. Achieving a stable quantum computer where errors can be reliably detected and corrected, known as fault-tolerant quantum computing, will require millions of stable, interconnected qubits known as ‘logical qubits.’
How AI Can Accelerate Quantum Computing Development
The integration of artificial intelligence with quantum computing represents a critical force multiplier in addressing quantum's fundamental technical challenges. While today's quantum computers remain in the noisy intermediate-scale quantum (NISQ) era – characterized by systems that lack full error correction and limited logical qubits – machine learning techniques are already demonstrating a significant impact in overcoming these limitations. Google's Quantum AI team recently achieved a breakthrough in quantum error correction, using neural networks to demonstrate a quantum memory operating below the surface code threshold – a crucial step toward fault-tolerant quantum computing.
Beyond error correction, AI is revolutionizing how we manage and optimize quantum systems. Reinforcement learning and deep neural networks are helping researchers maximize qubit fidelity, reduce noise, and discover novel quantum circuits that outperform human-designed alternatives. These AI-powered advances in resource allocation and algorithm discovery are accelerating quantum computing's path toward practical applications, demonstrating how the convergence of these technologies can help overcome the field's most pressing technical barriers.
How Quantum Computing Can Benefit AI
Quantum computing represents a transformative force in artificial intelligence development, offering solutions to several critical challenges facing the industry. The technology's quantum parallelism capabilities – a technique in which multiple computations can be run simultaneously – could theoretically reduce training times for large language models. Many current LLMs require approximately over 1 million GPU hours to train – while quantum neural networks (QNNs) promise more efficient processing of complex, high-dimensional datasets compared to classical neural networks. Beyond speed improvements, quantum computing could revolutionize AI through enhanced optimization algorithms, more sophisticated model simulations, and significantly reduced energy consumption for training AI models.
Optimization: Optimization algorithms are used to solve for the most optimal solution to day-to-day challenges in various sectors such as finance, manufacturing, and defense. Currently, optimization problems are run on classical computers as many of today’s quantum optimization algorithms are still nascent but making progress.
Advanced Simulation and Modeling: Quantum AI systems are uniquely suited to model complex, multi-agent scenarios that classical computers struggle to process efficiently. This allows for vivid, realistic modeling of complex situations. Quantum simulation can impact many different sectors - finance, defense, and biology. In biology, it can model complex biological systems, such as protein folding and simulating chemical reactions. For troops on the battlefield, it could simulate battles in real-time, allowing for more decisive action and battle planning.
Drug Discovery and Materials Science: Most notably, quantum computing and quantum AI have the ability to dramatically accelerate the drug and material discovery pipeline. Unlike classical computing, quantum computing can run parallel calculations, speeding up the time it takes to analyze large data sets. Using quantum machine learning (QML), a process where machine learning techniques are applied to quantum computation, would improve efficiency when discovering optimal chemical compounds, shortening the time for potential drug candidates to come to market. Additionally, quantum computing could also parse through large datasets to conduct pattern matching at speeds surpassing classical computing capabilities. This could help speed up DNA sequencing capabilities, further accelerating drug and materials discovery. Research to jump-start quantum computing, AI, and drug and material discovery is currently underway, with researchers from quantum computing company D-Wave and Japan Tobacco’s pharmaceutical research center looking at ways to leverage this technology.
While many of these applications remain in the early stages, some companies are already implementing quantum-inspired techniques on classical computers to improve AI training efficiency.
The Future of Quantum AI
As of right now, classical computers still outperform quantum systems in most real-world applications. However, the convergence of quantum computing and AI on classical systems presents a transformative opportunity. By leveraging machine learning techniques to enhance quantum error correction, optimize qubit management, and accelerate the discovery of novel quantum algorithms, Quantum AI could unlock new frontiers in fields ranging from drug discovery to defense applications.
While post-quantum cryptography (PQC) dominates headlines and policy discussions around quantum computing's future impact, the transformative potential of Quantum AI extends far beyond cryptographic applications. Though Quantum AI will certainly play a role in developing more resilient encryption methods, its most profound implications lie in revolutionizing scientific discovery and the research and discovery process itself. The convergence of quantum computing's unique computational capabilities with advanced AI systems and machine learning techniques promises to dramatically accelerate breakthrough discoveries – from rapidly identifying promising drug candidates to unraveling the mysteries of complex biological systems. This scientific acceleration could compress research timelines from decades to years, fundamentally changing how we approach everything from pharmaceutical development to materials science.
What Needs to Happen
In order for Quantum AI to reshape how fundamental research and development is conducted, continued development of artificial intelligence will remain pivotal. However, a concentrated effort across multiple domains must be made to ensure quantum computing development keeps pace (see notional timeline above).
Funding & Increased R&D
The current quantum computing funding landscape remains more constricted compared to AI investments. The U.S. public sector is the primary investor in quantum tech, with about $4 billion invested. In 2023, the U.S. private sector invested about $1.7 billion in quantum computing. For comparison, private AI investment in the U.S. in 2023 was $67.2 billion, whereas federal funding for AI R&D was roughly $3 billion.
The primary vehicle in the Federal government for quantum-related funding is the National Quantum Initiative Act, passed in 2018. This landmark legislation has served as the cornerstone of America's quantum technology strategy, supporting real progress in quantum research over the past six years and providing sustained, strategic investment needed to secure quantum advantage. Renewed support by the Federal Government is necessary to ensure the Department of Energy (DOE), National Science Foundation (NSF), National Institute of Standards and Technology (NIST), and NASA continue to develop the next generation of quantum technologies, establish world-class research hubs, and train the future quantum workforce.
One assessment of how federal funding for quantum-related work should grow is the Congressional Budget Office’s assessment of the proposed Quantum Initiative Reauthorization Act.
To keep pace with AI advancement and enable Quantum-AI integration, funding needs to be strategically realigned to prioritize quantum computing advancements. In combination with funding, research and development efforts must be aligned to overcome the pressing technical and integration challenges that currently bog down quantum computing development and commercialization.
Policy
As the global race to harness the power of quantum computing intensifies, the United States has a critical opportunity to cement its leadership in Quantum AI. Targeted action to bolster quantum-related efforts by the National Labs, DOE’s Quantum Information Science Research Centers (QISRCs), and NSF’s Quantum Leap Challenge Institutes (QLCIs) will help to advance quantum research and development. This could include directing increased funding towards these initiatives, ensuring dedicated resources for cutting-edge quantum technologies, workforce development, and industry collaboration through public-private partnerships, thereby accelerating the nation's leadership in this maturing technology. Further, these government players could share best practices voluntarily in partnership with the private sector to collectively elevate the nation’s quantum computing know-how, and vice versa like an epistemic community.
Refocused strategy and targeted investments will be essential as the United States seeks to maintain its lead in the global quantum race. Government investment will help foster maturity in quantum computing, which can then invigorate private sector investment in this critical technology. China, in particular, has been pursuing its own national quantum strategy, concentrating significant resources within government-affiliated institutions and national laboratories. The United States must continue to focus its efforts into developing a robust quantum ecosystem with tangible technological breakthroughs. Realizing this quantum future will require a sustained, whole-of-nation commitment.
The Path Forward
We must work with a renewed sense of urgency and national purpose to secure America's quantum advantage. While the road to achieving true Quantum AI is not without significant hurdles, the potential payoffs are immense. Mastering the intersection of these two revolutionary technologies could redefine the future of innovation, giving rise to breakthroughs that could reshape entire industries and the trajectory of the strategic competition. Through sustained, strategic investments, collaborative research, and a whole-of-nation commitment, the United States can ensure it remains at the vanguard of this transformative technology frontier.