Qash-QKDC

Quantum Key-Derivation/Hashing Circuits (Superconductor and Photonic QPUs)

• This jupyter notebook demonstrates a proof of concept for using quantum operations to hash data in a cryptographically secure manner
• There are 2 different circuits demnstrated based on the type of quantum processor (QPU) being used:
  1. superconductor (also compatible with trapped-ion QPUs)
  2. photonic (fock based)
• This notebook uses simulators to demonstrate hashing capabilities, however, these circuits can be ran on physical quantum hardware if adapted correctly
• For proper gaussian-photonic implementation, check out GausQash

Web Demo Now Available!!!

• 
• web demo source code

Updates

• specialized gradient calculation
  • less possibility for hash collisions
  • better key-derivation (more possiblities for key/hash outputs)
• new device compatibility:
  • IonQ & AQT (trapped-ion): compatible with superconductor circuit
  • QuTech/Quantum Inspire
  • IBM/Qiskit
  • Google/Cirq
  • Nvidia/CuQuantum
  • Kokkos

Security Note: these circuits are not battle tested in any capacity and therefore unverified to be cryptographically secure, or programmatically useful in any manner

• If anyone wants to benchmark and/or pentest these circuits feel free to do so
• any feedback related to improving these circuits security and/or usability is highly appreciated

General Notes:

• this notebook was created using python v3.11
• These circuits are not particularly fast in runtime (due to the nature of computations being executed)
• in order to help with the preformance drag and to allow execution on different device types, the JAX python library is used
• at the moment these circuits do not work with complex number operations, they do work with single and double-precision float values

Future Goals:

• create circuits compatible with neutral-atom QPUs
• continue research/development of new and current circuits using physical quantum hardware (whether through the cloud or on-premise access)
• develop web ui for demo usage!

Donations (optional):

• Any donation, no matter how small, is greatly appreciated!!
• click here to donate

Citation (this project):

• please cite this project/repo if using it in research and/or development (USE IN RESEARCH/DEVELOPMENT IS ENCOURAGED)

Credits:

• quantum libraries provided by PennyLane:

  • pennylane research paper:

    Ville Bergholm et al. PennyLane: Automatic differentiation of hybrid quantum-classical computations. 2018. arXiv:1811.04968

• accerlation through JAX library:

  jax2018github, author = {James Bradbury and Roy Frostig and Peter Hawkins and Matthew James Johnson and Chris Leary and Dougal Maclaurin and George Necula and Adam Paszke and Jake Vander{P}las and Skye Wanderman-{M}ilne and Qiao Zhang}, title = {{JAX}: composable transformations of {P}ython+{N}um{P}y programs}, url = {http://github.com/google/jax}, version = {0.3.13}, year = {2018},