Willow quantum chip demonstrates verifiable quantum advantage on hardware

Perceived novelty vs. prior “quantum advantage” announcements

• Many commenters feel this sounds like yet another recycled “first quantum advantage” claim; several recall multiple earlier Google announcements, also in top journals.
• Others argue this one is meaningfully different because it’s tied to a concrete physics/chemistry task and a Nature paper that carefully frames it as “a viable path to practical quantum advantage,” not a done deal.

What the experiment actually did (vs RCS)

• Multiple explanations stress this is not random circuit sampling (RCS).
• The “Quantum Echoes” algorithm perturbs one qubit and observes how that disturbance propagates, extracting an observable related to a Hamiltonian.
• It’s presented as a quantum-enhanced analogue of difficult nuclear magnetic resonance (NMR) experiments, with some extra information (e.g., Jacobian/Hessian–like data) that’s hard to get classically.

“Verifiable” and repeatability

• Earlier work produced random bitstrings that couldn’t be deterministically checked.
• Here, the output is a reproducible number (an expectation value) that can in principle be checked by classical simulation or alternative experiments, though for larger instances classical simulation becomes intractable.
• Skeptics note:
  • “Verifiable” here does not mean the strong cryptographic notion of classical verification of a quantum device.
  • The team hasn’t actually rerun it on independent hardware; “any other of the same caliber” is a claim, not yet a demonstration.

Usefulness and real-world applications

• Several see this as closer to what quantum computers should be good at: simulating quantum systems (molecules, materials) rather than artificial sampling problems.
• The suggested applications (drug discovery, materials design) are viewed as plausible but extremely timeline-uncertain; commenters say it could be years or decades.

Comparison with classical computation

• Google cites a ~13,000× speedup over a leading supercomputer, based on tensor-network simulation cost estimates.
• Some doubt whether the classical side is fully optimized, and expect eventual classical counter-papers that may reduce the claimed gap.
• Others emphasize that classical algorithms can also be stochastic; the relevant question is precision and cost for the same observable.

Security, cryptography, and Bitcoin

• Multiple subthreads discuss quantum threats to RSA/ECDSA and cryptocurrencies, especially Bitcoin.
• Consensus in the thread: this work is about quantum simulation, not cryptanalysis, and is not a step toward breaking RSA/Bitcoin.
• There is extensive debate about:
  • How hard it would be to migrate Bitcoin and other systems to post-quantum cryptography.
  • Whether legacy data (captured TLS, old encrypted traffic, lost wallets) is at long‑term risk.
  • Timelines: some warn of a “Q‑Day” in the 2030s; others argue practical factoring‑class devices are still very far away and that PQC deployment is already underway.

Hype, funding, and research culture

• A recurring theme is frustration with overhyped corporate press releases versus more modest claims in the paper itself.
• Some view quantum computing as a “snake oil”‑like funding funnel with no near‑term real‑world payoff; others defend it as legitimate basic physics research analogous to early days of classical computing.
• There is debate over corporate vs. university roles: some lament “mega‑monopoly” research, others point out this work is heavily coauthored with major universities.

Maturity of hardware (quantum volume, error rates)

• A few commenters argue that until systems demonstrate high “quantum volume” (e.g., effectively handling circuits of size ~2¹⁶ with good fidelity), most such advantage claims are more like impressive demos than broadly useful computation.
• Others counter that in a nascent field, incremental, domain‑specific milestones are expected and still scientifically meaningful, even if far from factoring large numbers or running Shor at scale.