a16z Podcast: Quantum Computing, Now and Next

Episode Summary

Executive Summary: The episode explores why quantum computing may be the next major computing platform: classical chip scaling is hitting economic and physical limits, while quantum machines could unlock new capabilities in chemistry, optimization, and eventually AI. Chad Rigetti explains the field’s progress, current technical bottlenecks, early hybrid use cases, the global competitive landscape, and why startups can compete despite giants like IBM and Google.

Main Topics: Moore’s Law, Amdahl’s Law, and the limits of classical scaling (Priority: 5/5): The conversation frames modern computing history through transistor scaling, then argues that power density, communication overhead, and rising fab costs are slowing brute-force gains from classical hardware. Quantum computing as a new computing paradigm (Priority: 5/5): Rigetti explains that quantum computers encode information in quantum states rather than binary bits, leveraging continuous variables and exponential state growth to represent and process information differently from classical machines. Near-term applications: chemistry, materials, and optimization (Priority: 5/5): The most credible first uses are simulation of quantum systems like molecules and materials, plus hybrid quantum-classical optimization problems that could benefit machine learning and other complex workloads. Current state of the technology and coherence challenges (Priority: 4/5): The field has moved from proving measurable qubit coherence to improving qubit count, gate quality, and reliability enough to run useful computations, though the hardest problems remain unsolved. Industry landscape and global race (Priority: 4/5): Large efforts at IBM, Google, Microsoft, and universities worldwide coexist with smaller startups; the transcript emphasizes that quantum computing is a global competition with meaningful progress in Europe, Australia, and China. Rigetti’s full-stack startup strategy (Priority: 4/5): Rigetti describes building hardware, control systems, cloud integration, and software under one roof, arguing that focused startups can outperform incumbents because the needed expertise and culture are highly specialized. Cryptography and long-term societal impact (Priority: 3/5): Shor’s algorithm is discussed as the famous security threat, but Rigetti argues the more transformative applications will be in drug discovery, energy, materials, and potentially more powerful AI.

Key Arguments: Classical computing is approaching diminishing returns because transistor miniaturization is constrained by heat, fabrication cost, and communication overhead between many processors. Moore’s Law should be understood partly as an economic phenomenon: when enough capital, talent, and demand align, new hardware capabilities accelerate rapidly. Quantum computing is fundamentally different because a qubit can represent continuous quantum states and each additional qubit doubles the accessible state space. The earliest practical quantum applications will likely be in quantum chemistry and materials science, where the systems being modeled are themselves quantum mechanical. Hybrid quantum-classical systems are the near-term path: the quantum processor should do the parts it is uniquely good at while classical hardware handles the rest. Progress in the field has shifted from proving qubits can exist coherently to improving scale and error rates enough for useful computation. A startup can compete with incumbents because quantum computing requires a tightly integrated, specialized organization, not just massive scale. Quantum computers may eventually break RSA via Shor’s algorithm, but that is less compelling than their positive applications in science and industry.

Data Points: Transistor size: 10–20 nanometers - Described as the approximate size of individual transistors in modern chips. Human hair width: 10–20 microns - Used as a comparison to show how tiny transistors have become relative to human hair. Atom scale comparison: About 100 atoms wide - Approximate scale of a 10-nanometer transistor. Fab cost: Tens of billions of dollars - Estimated cost to build leading-edge semiconductor manufacturing infrastructure. Supercomputer fab cost example: $4 billion fab - Used as a contrast for the manufacturing base behind a supercomputer chip stack. Startup manufacturing investment example: $10 million - Referenced as a much smaller investment that can still produce highly capable specialized chips in the new semiconductor landscape. Quantum era timeline: Over 100 years - Quantum mechanics was developed in the first two decades of the 20th century. PhD start year: 2002 - Rigetti mentions starting his PhD in 2002 when superconducting qubit coherence was extremely rare. Early field size: One or two groups - He says only one or two groups in the world had demonstrated measurable superconducting qubit coherence at that time. Industry research community: Thousands of people - Estimated number of people worldwide identifying as quantum computing researchers. Large-scale quantum threat horizon: 20 to 30 years - Rigetti’s estimate for when a machine could practically run Shor’s algorithm at meaningful scale. Startup software engineer growth: Approximately zero to meaningful number in five years - Prediction for the rise of quantum software and engineering roles.

Pivotal Quotes: "what the first applications will be, especially given what classical computers can't do" — Sonal: Podcast introduction to the episode’s focus on quantum computing use cases "quantum computing is getting pulled into that ecosystem and is beginning to be driven by the same economic forces that have been driving other forms of technology thus far" — Chad Rigetti: Explaining how quantum hardware development now follows broader technology economics "ultimately the universe itself and nature at the lowest level operates on quantum mechanics. And that's kind of the machine language that nature uses" — Chad Rigetti: Core rationale for why quantum computers can model certain problems better than classical systems

Implications: Quantum computing is moving from physics experiment toward platform. Expect early impact in chemistry, materials, and hybrid optimization, while the industry races to solve coherence, error, and software challenges before broad adoption.