Differentiable Quantum Circuits and Generative Modeling

We present a fresh approach to quantum machine learning by using quantum circuits as probabilistic generative models. The proposed QCBM overcomes the challenging problem in training implicit density models with discrete outputs in deep learning. The key component of our gradient-based learning algorithm is to measure the gradient of the two sample test loss function on a quantum computer unbiasedly and efficiently. With the inspiration of matrix product state, we are able to train a Born machine to generate intermediate-scale images with number of qubits much less than pixel numbers.
We demostrate the generative power of these learning schemes with our new Julia quantum circuit simulator Yao.jl [1]. With Yao.jl, one can simulate quantum machine learning models, quantum optimization algorithms and quantum chemistry problems efficiently and easily. We combined our framework with state of art machine learning framework like Zygote.jl, aimming for quantum software 2.0: "Automatic Differentiable Quantum Circuits".
[1] https://github.com/QuantumBFS/Yao.jl