Jennifer Prendki on Quantum Accelerators, The ‘No-Cloning’ Problem, QMLOps, and Post-Quantum Security.
Subscribe: Apple • Spotify • Overcast • Pocket Casts • AntennaPod • Podcast Addict • Amazon • RSS.
Jennifer Prendki explains that while universal quantum computers are a decade away, specialized quantum accelerators are already tackling AI problems in finance and pharma. She argues the biggest hurdle isn’t the hardware but the profound software and infrastructure gap, as fundamental principles like the “no-cloning theorem” break traditional MLOps. The conversation explores immediate use cases, the missing “QMLOps” stack, and how teams can prepare for a new computing paradigm.
Interview highlights – key sections from the video version:
-
-
- Framing the Conversation—AI, Data & Quantum Overview Request
- State of Quantum Computing Today & Jennifer’s Career Path
- Real-World Quantum Use Cases and Startup Landscape
- Big Tech Efforts, Qubits 101 & Quantum Supremacy Milestones
- Practical ML Use Cases: Recommendations, Pharma & High-Speed Inference
- Is Quantum Advantage Worth the Investment? Scaling Qubits & Roadmap
- Building the Quantum Stack: Hardware, Cooling & Missing MLOps Layer
- Topological Computing Approaches and Technology Diversity
- Quantum Data-Ops Hurdles: No-Cloning, Probabilistic Results & Lineage
- Hybrid Architectures & Quantum Embeddings for Data Storage
- Rethinking Modeling with Data Manifolds & Topological Data Analysis
- What CTOs Need to Know: Infrastructure, Talent Gaps & Risks
- Geopolitics, Education Gaps & Paths to a Quantum Breakthrough
-
Related content:
- A video version of this conversation is available on our YouTube channel.
- A DeepMind veteran on the future of AI and quantum
- How Two Sigma & Nubank Rewire Finance with Foundation Models
- Your AI playbook for the rest of 2025
- Manos Koukoumidis: How a Public-Benefit Startup Plans to Make Open Source the Default for Serious AI
- Nestor Maslej: 2025 Artificial Intelligence Index
Support our work by subscribing to our newsletter📩
Transcript
Below is a heavily edited excerpt, in Question & Answer format.
Current State and Timeline
How close are we to useful quantum computing for AI applications?
While universal quantum computers capable of solving any AI problem may still be 10-15 years away, specific quantum applications for machine learning are emerging today. Companies like IonQ, D-Wave, and Rigetti are already working with governmental agencies like NASA on real use cases. Today’s leading machines operate at roughly 100 logical qubits, with startups like PsiQuantum targeting the 1,000-qubit range within a few years.
The key distinction is that quantum computers for specific AI inference tasks—particularly those involving structured data and requiring massive parallel computations—are becoming viable now, not in the distant future. We’re seeing the tip of the iceberg, with the main challenge being the gap between research and production rather than the fundamental technology itself.
What’s the reality behind the quantum advantage?
The quantum advantage comes from a fundamental difference in computation. Classical computers explore paths sequentially, while quantum computers leverage superposition to explore many paths simultaneously. This isn’t just a linear improvement—adding 10x more qubits yields an exponential jump in computational power due to quantum mechanics.
Large financial and pharmaceutical companies are investing in this expensive, early-stage technology because they’re already hitting the limits of classical computing for specific inference tasks. The bottleneck is speed and scale, and quantum offers a fundamentally different approach to overcome these limitations.