QGOpt: Riemannian optimization for quantum technologies

Abstract

Many theoretical problems in quantum technology can be formulated and addressed as constrained optimization problems. The most common quantum mechanical constraints such as, e.g., orthogonality of isometric and unitary matrices, CPTP property of quantum channels, and conditions on density matrices, can be seen as quotient or embedded Riemannian manifolds. This allows to use Riemannian optimization techniques for solving quantum-mechanical constrained optimization problems. In the present work, we introduce QGOpt, the library for constrained optimization in quantum technology. QGOpt relies on the underlying Riemannian structure of quantum-mechanical constraints and permits application of standard gradient based optimization methods while preserving quantum mechanical constraints. Moreover, QGOpt is written on top of TensorFlow, which enables automatic differentiation to calculate necessary gradients for optimization. We show two application examples: quantum gate decomposition and quantum tomography.

arXiv comment28 pages, 7 figures, 4 tables, a note on relevant research is added

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Cited by in corpus (20)

• TensorCircuit: a Quantum Software Framework for the NISQ Era
• Riemannian geometry and automatic differentiation for optimization problems of quantum physics and quantum technologies
• Scaling of neural-network quantum states for time evolution
• Tensor networks for quantum computing
• Multipartite correlations in quantum collision models
• Robustly learning the Hamiltonian dynamics of a superconducting quantum processor
• Efficient MPS representations and quantum circuits from the Fourier modes of classical image data
• Efficient Quantum Circuit Compilation for Near-Term Quantum Advantage
• Gradient-descent methods for fast quantum state tomography
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