A Field Still Finding Its Footing
Quantum computing doesn't move on the same clock as software. Our research team tracks the hardware and software ecosystem closely, which shapes how — and how early — we invest in it.
Multiple Competing Hardware Approaches
Superconducting qubits, trapped ions, photonics, and neutral atoms are all still viable. We don't take a single-horse view; instead we track error rates, coherence times, and scaling roadmaps across approaches as they publish.
Where the Near-Term Opportunity Lives
- Error correction and control software — much of the near-term value in quantum may accrue to the software and control layers that make noisy hardware usable.
- Specialized tooling for developers building on early quantum hardware.
- Hybrid classical-quantum applications in domains like chemistry and optimization, where even modest quantum advantage could matter.
A Long-Horizon View
We size our quantum investments and our research commitment to match the timeline the field actually requires — not the timeline a pitch deck suggests. That means smaller, earlier positions and a willingness to be wrong about which hardware approach wins.
Why We Stay Close to the Research
Being able to read a new error-correction paper ourselves, rather than relying entirely on a company's own claims, is how we keep our long-horizon bets honest.
Sources
- Google Quantum AI, "Quantum error correction below the surface code threshold," Nature 638, 920–926 (2025). DOI: 10.1038/s41586-024-08449-y — the peer-reviewed demonstration that logical error rates can be suppressed below the surface-code threshold, the benchmark the field has targeted since the 1990s.
- AbuGhanem, "Google Quantum AI's Quest for Error-Corrected Quantum Computers" (arXiv:2410.00917) — an independent review of hardware, software, and error-correction progress, useful as a survey alongside primary results.
Bringing the Strongest Bets to Our Network
When our evaluation gives us genuine conviction on a quantum opportunity, we invest — and we bring it to our network of outside investors, so the deal isn't gated solely on our own capital.
Quant Labs View
What matters technically: whether error rates are actually falling as code distance increases, in line with surface-code theory — that's the difference between a hardware approach that scales and one that's stalled.
What we would test: independent replication of a claimed error-correction milestone against the published methodology, and whether the result holds on a second device rather than a single showcase chip.
What could invalidate the thesis: the gap between "below threshold" demonstrations and a genuinely useful fault-tolerant computation turning out to be far longer than current roadmaps suggest — the field has a history of multi-year slippage on exactly this timeline.
Commercial implication: we size quantum positions for a long horizon and prioritize teams working on error correction and control software, where near-term value is most likely to accrue while hardware is still maturing.