binary optimization


– brute_force
– ibm_sw_qaoa
– hfs
– google_sw_qaoa
– dwave-hw
Use “solve_binary” to solve binary optimization problems
  • Execute on multiple backends and simulators
  • Submit unconstrained and constrained problems
  • Use a generic decomposition approach to solve large problems
  • Use anneal offsets to improve the probability of finding a better solution

chemistry simulation


– quasar


– protectq
Use “mc_vqe” to solve for specific photochemistry attributes
  • Control the size of the ring
  • Execute on hosted cloud simulators

machine learning


– ibm_sw
Use “fit_and_predict” to classify new data points
  • Input data sets for supervised learning 
  • Execute on hosted cloud simulators


Q3 2019 - Initial Release


Binary Optimization:

  • solve_binary: brute force, hfs, ibm_sw_qaoa, google_sw_qaoa, dwave_hw
  • Anneal Offset / Decomposition


Chemistry Simulation:

  • mc_vqe: quasar
  • find_ground_energy_state: projectq

Machine Learning:

  • fit_and_predict: ibm_sw

Quantum Simulators:

  • IBM, Google, ProjectQ

Quantum Hardware:

  • D-Wave

Binary Optimization:

  • solve_binary: improvements to QAOA
  • SDP: semi definite programming
  • Expanded decomposition

Chemistry Simulation:

  • mc_vqe: expanded tom more complex hamiltonians
  • find_ground_energy_state: additional backends, smarter ansätze, larger molecules

Machine Learning:

  • q-means: quasar, ibm_sw
  • Monte Carlo: oracle over binary vars

Quantum Simulators:

+ Microsoft, Rigetti

Quantum Hardware:

+ Rigetti, IBM