AI For Quantum And Quantum For AI: How The AI Boom May Reverberate Across Future Technologies

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Insider Brief

  • Quantonation offered a presentation that covers the venture capital firm’s view on the relationship between quantum and artificial intelligence (AI) technologies at a recent investor day event.
  • AI and quantum technology are symbiotic technologies that can drive each other forward and reshaping the landscape of computational power.
  • Ultimately, the AI boom could create a virtuous side effect to help the growth of quantum AI.

One of the hotly debated buzzwords in the quantum industry is “quantum AI” — using quantum computing to power artificial intelligence (AI). A pioneering quantum technologies venture firm suggests that quantum AI might may be more than just a marketing attempt to hang two hip-sounding technologies together to take advantage of the current AI boom. Quantum for AI — and AI for quantum — may be the key to overcoming critical technological challenges and creating practical solutions to big societal and scientific problems.

In an online white paper based on a recent presentation by Quantonation, the venture capital firm said that there’s a symbiotic relationship between AI and quantum technology and that these fields are driving each other forward and reshaping the landscape of computational power.

The team also suggests that the intersection of AI and quantum may be more than a happy scientific exploration, but an inevitable partnership being shaped by today’s economic forces and tomorrow’s technological barriers.

It’s Crunch Time

The rapid advance of AI has led to an unprecedented demand for computational resources — an AI compute crunch. Quantonation pointed out that the computational requirements for training frontier AI models have skyrocketed, requiring 1000 times more compute power than four years ago. This surge has also led to a 100-fold increase in the cost of training these models. NVIDIA, with its dominant market position, has seen rapid growth, achieving a market cap exceeding $3.1 trillion.

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This rapidly inflating demand for computational power would be enough to present technological and economic incentives to explore alternatives to traditional CPUs and GPUs, Quantonation points out. They identified several promising areas in research and development, including Field Programmable Gate Arrays (FPGAs), Machine Learning Application-Specific Integrated Circuits (ML ASICs), superconducting, thermodynamic, reversible, optical, analog and biological computing.

Among these, quantum computing stands out as one of the most disruptive, offering new algorithms and problem-solving approaches that are unattainable with other hardware.

The team writes: “Critically, quantum computing doesn’t just offer a constant factor speedup – it unlocks entirely new algorithms and approaches to problem-solving. If you are going to unseat NVIDIA and other well funded incumbents, then you need more than just great execution. You need a fundamentally disruptive approach.”

The Virtuous Cycle: AI and Quantum

Quantonation emphasized the mutually beneficial relationship between AI and quantum technologies, highlighting several key areas where they intersect:

Quantum for AI

  1. Faster Training and Inference: Quantum machine learning is an active research area, exploring heuristic approaches on near-term quantum devices and long-term research on complexity speedups for future quantum computers.
  2. Quantum-Inspired Classical Algorithms: Techniques like tensor networks, developed for quantum physics, are being applied to large linear algebra problems on classical computers.
  3. Better Training Data: Quantum computers can provide more accurate simulations of the physical world, while quantum sensors offer better measurements. This improved data can enhance classical machine learning models.
  4. Enhanced Privacy and Security: Quantum computing supports blind quantum computing, ensuring the privacy of data during computations.

AI for Quantum

  1. Quantum Processor Design Optimization: Machine learning can assist in the design and simulation of quantum processors.
  2. Improved Calibration and Control: Better machine learning can enhance the calibration and programming of quantum systems, reducing errors.
  3. Optimization in Variational Quantum Algorithms: Hybrid algorithms, which combine classical and quantum computing, can benefit from improved machine learning techniques.
  4. Automated Translation of Classical Code to Quantum Algorithms: AI systems could facilitate the porting of classical code to quantum algorithms, aiding in the adoption of quantum computing.

Practical Examples

Although quantum AI may sound long-future futuristic, the initial waves of transformation are hitting the shore now. Researchers and entrepreneurs are exploring paths to quantum AI now and building products that could take advantage of merging these two powerful technologies. Quantonation provided a few real-world examples from companies in their portfolio that demonstrate the practical applications of the AI-quantum synergy:

  1. Qubit Pharmaceuticals: Quantum-accelerated simulations could help train better machine learning models for drug discovery, creating a virtuous cycle of improved data and models.
  2. Multiverse Computing: Developed CompactifAI, a quantum-inspired approach that significantly accelerates the training of Large Language Models (LLMs).
  3. Pasqal: Leveraging analog quantum programming with neutral atom computers to implement graph neural networks, addressing problems in logistics, social network analysis, and biological processes.

The State of Quantum Machine Learning

The field of quantum machine learning (QML) is in its early stages but is growing rapidly. In 2023, over 4,000 publications on QML marked a significant increase from a decade earlier, the team writes. Despite this growth, QML still represents a small fraction of all machine learning research, indicating that much work remains.

And there are challenges — a lot of them — that scientists will need to overcome before QML becomes practical, Quantonation notes.

They write that theoretical quantum speedups in machine learning will face the need for significant quantum memory and the difficulty of comparing performance at scale. The team also emphasized the importance of empirical results, which are currently limited by the subscale size of existing quantum computers.

Looking Ahead

Quantonation anticipates that the ongoing AI boom will drive increased interest and investment in quantum technologies. Quantum-inspired classical algorithms are already making an impact, and future generations of quantum computers are expected to bring practical quantum machine learning techniques to fruition.

However, realizing the long-term potential of these technologies will require focused, sustained investment and effort. Quantonation writes that they remain committed to supporting the convergence of AI and quantum tech, backing startups and technologies that are poised to shape the future of computing.

You can read Quantonation’s complete white paper here.

Matt Swayne

With a several-decades long background in journalism and communications, Matt Swayne has worked as a science communicator for an R1 university for more than 12 years, specializing in translating high tech and deep tech for the general audience. He has served as a writer, editor and analyst at The Quantum Insider since its inception. In addition to his service as a science communicator, Matt also develops courses to improve the media and communications skills of scientists and has taught courses. [email protected]

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