Fast optimization of parametrized quantum optical circuits
- URL: http://arxiv.org/abs/2004.11002v5
- Date: Tue, 10 Nov 2020 14:40:01 GMT
- Title: Fast optimization of parametrized quantum optical circuits
- Authors: Filippo M. Miatto and Nicol\'as Quesada
- Abstract summary: Parametrized quantum optical circuits are a class of quantum circuits in which the carriers of quantum information are photons and the gates are optical transformations.
We present an algorithm that is orders of magnitude faster than the current state of the art to compute the exact matrix elements of Gaussian operators and their gradient.
Our results will find applications in quantum optical hardware research, quantum machine learning, optical data processing, device discovery and device design.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Parametrized quantum optical circuits are a class of quantum circuits in
which the carriers of quantum information are photons and the gates are optical
transformations. Classically optimizing these circuits is challenging due to
the infinite dimensionality of the photon number vector space that is
associated to each optical mode. Truncating the space dimension is unavoidable,
and it can lead to incorrect results if the gates populate photon number states
beyond the cutoff. To tackle this issue, we present an algorithm that is orders
of magnitude faster than the current state of the art, to recursively compute
the exact matrix elements of Gaussian operators and their gradient with respect
to a parametrization. These operators, when augmented with a non-Gaussian
transformation such as the Kerr gate, achieve universal quantum computation.
Our approach brings two advantages: first, by computing the matrix elements of
Gaussian operators directly, we don't need to construct them by combining
several other operators; second, we can use any variant of the gradient descent
algorithm by plugging our gradients into an automatic differentiation framework
such as TensorFlow or PyTorch. Our results will find applications in quantum
optical hardware research, quantum machine learning, optical data processing,
device discovery and device design.
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