• Corpus ID: 252567996

Towards a scalable discrete quantum generative adversarial neural network

@inproceedings{Chaudhary2022TowardsAS,
  title={Towards a scalable discrete quantum generative adversarial neural network},
  author={Smit Chaudhary and Patrick Huembeli and Ian MacCormack and Taylor Lee Patti and Jean Kossaifi and Alexey Galda},
  year={2022}
}
We introduce a fully quantum generative adversarial network intended for use with binary data. The architecture incorporates several features found in other classical and quantum machine learning models, which up to this point had not been used in conjunction. In particular, we incorporate noise reuploading in the generator, auxiliary qubits in the discriminator to enhance expressivity, and a direct connection between the generator and discriminator circuits, obviating the need to access the… 

References

SHOWING 1-10 OF 20 REFERENCES

Learning representations by back-propagating errors

Back-propagation repeatedly adjusts the weights of the connections in the network so as to minimize a measure of the difference between the actual output vector of the net and the desired output vector, which helps to represent important features of the task domain.

and a at

The xishacorene natural products are structurally unique apolar diterpenoids that feature a bicyclo[3.3.1] framework. These secondary metabolites likely arise from the well-studied, structurally

I and i

There is, I think, something ethereal about i —the square root of minus one, which seems an odd beast at that time—an intruder hovering on the edge of reality.

FRONTIERS IN PHYSICS

and s

A. and Q

Supervised Learning with Quantum Computers

and P

    npj Quantum Information 5

    • 1
    • 2019