Quantum computing has been “five years away” for two decades. But something shifted in 2025: multiple hardware platforms demonstrated error correction at scale, and the first quantum-specific algorithms started outperforming classical alternatives on real problems.

We’re now in the commercialization window.

The Hardware Landscape

Three hardware approaches have emerged as serious contenders:

Superconducting qubits (IBM, Google) lead in qubit count but face scaling challenges. The recent push to “utility-scale” systems around 1,000 qubits is promising, but error rates still limit practical applications.

Trapped ions (IonQ, Quantinuum) offer better qubit quality with slower gate speeds. The 2024 breakthroughs in shuttling architectures may solve the scaling problem.

Neutral atoms (QuEra, Pasqal) are the dark horse—rapid qubit scaling with native multi-qubit gates could enable new algorithm designs.

For investors, the key question isn’t “which hardware wins?” but “how long until useful quantum advantage?”

Where Quantum Advantage Arrives First

Forget breaking encryption. The near-term applications are more mundane but commercially significant:

Molecular Simulation

Pharma companies have spent billions on classical molecular dynamics. Quantum systems can simulate molecular behavior directly—no approximations. Early applications in catalyst design and drug binding affinity are already in pilot programs.

Optimization

Supply chain, logistics, and portfolio optimization problems map naturally to quantum architectures. The value prop isn’t “solving impossible problems” but “solving known problems faster and cheaper.”

Machine Learning

Quantum-enhanced ML is overhyped in some areas but genuine in others. Quantum kernels for specific data types (molecular structures, graph data) show real promise. The key is identifying problems where quantum provides structural advantages, not just speedups.

The Software Gap

Here’s the opportunity most investors miss: quantum software is years behind quantum hardware.

Classical computing had decades to develop compilers, debuggers, and software engineering practices. Quantum is trying to compress that timeline dramatically. We see investment opportunity in:

  • Development tools: IDEs, simulators, and testing frameworks for quantum code
  • Error mitigation: Software techniques to extract useful results from noisy hardware
  • Algorithm libraries: Pre-built quantum routines for common problems
  • Hybrid orchestration: Systems that intelligently split workloads between quantum and classical

Our Investment Thesis

We’re not betting on a single hardware winner. Instead, we’re focused on the software and middleware layer that sits above hardware—tools and platforms that help enterprises capture value regardless of which qubits power the computation.

The companies that win in quantum won’t necessarily be the ones building quantum computers. They’ll be the ones making quantum computers useful.