Quantum computing without leaving Python behind
summary
Survey finds 95% of practitioners use Python for quantum development while Rust rises to ~13%. A C++11 simulator (open source, OpenCL optional) wrapped with Cython runs a 27‑qubit cryptography circuit in ~17s; naive simulation cost scales as G 2^N. IBM moves a Qiskit transpiler to Rust and reports ~6x speedup (later +20%); a PennyLane qjit JIT delivers ~3x* on a 3‑qubit toy. Tools shown: Qiskit, Amazon Braket DM1, OpenCL, nanobind/JAX, and Python 3.13 GIL experiments.
AI-written summary · about
chapters (12)
- 0:00Python dominance in quantum
- 1:55Motivation for Python focus
- 3:18Quantum computing basics
- 9:02Quantum development workflow
- 14:06Quantum software stack
- 18:45Python's role in quantum stack
- 24:25Need for speed in quantum
- 27:57Cirq quantum circuit simulator
- 29:45IBM's transpiler in Rust
- 34:22Just-in-time compilation engine
- 38:14Bringing speed back to Python
- 41:25Q&A and closing
related talks
01h 27mQuantum Computing with Python
27mQuantum Computing with Python: From Qubits to Circuits
47mQuantum in the Cloud: Running Quantum Workloads on Kubernetes
27mWhat are quantum computers, and how can we train them in Python?
26mQuantum Leap: Revolutionizing Rust Software Development
47mQuantum Computing
50mGod really plays dice - Introduction to Quantum Computing with Q#
45mQiskit: Building a quantum computing community