
  Where things stand: Current machines are in the NISQ (Noisy Intermediate-Scale Quantum) era — tens
   to thousands of noisy qubits without full error correction. Google demonstrated "quantum
  supremacy" in 2019 (a specific sampling task faster than classical), and IBM and others have
  continued scaling qubit counts.

  Key challenges:
  - Scaling to enough high-quality qubits for error correction
  - Reducing gate error rates below fault-tolerance thresholds
  - Developing practical algorithms that provide advantage on near-term hardware
  - Building the classical control and software stack

  What quantum computers won't do: They are not universally faster than classical computers. They
  excel at specific structured problems (simulation, factoring, optimization) but do not speed up
  general-purpose computing.

  Practical implications

  - Cryptography: Post-quantum cryptography standards (lattice-based, hash-based, etc.) are being
  standardized now (NIST finalized initial standards in 2024) to prepare for a future where Shor's
  algorithm threatens current public-key schemes.
  - Chemistry/materials: Simulating molecular behavior is a near-term practical target.
  - Optimization: Logistics, finance, and scheduling problems may benefit, though proven quantum
  advantage for optimization remains an open question.
  - Machine learning: Quantum ML is an active research area but has not yet demonstrated clear
  practical advantage.

✻ Crunched for 37s

❯ Write a very long essay about the history of mathematics from ancient times to modern day     
                                                                                                
  ⎿  Interrupted · What should Claude do instead?

────────────────────────────────────────────────────────────────────────────────────────────────────
❯  
────────────────────────────────────────────────────────────────────────────────────────────────────
  ⏵⏵ bypass permissions on (shift+tab to cycle)



