Variational quantum process tomography

Abstract

Quantum process tomography is an experimental technique to fully characterize an unknown quantum process. Standard quantum process tomography suffers from exponentially scaling of the number of measurements with the increasing system size. In this work, we put forward a quantum machine learning algorithm which approximately encodes the unknown unitary quantum process into a relatively shallow depth parametric quantum circuit. We demonstrate our method by reconstructing the unitary quantum processes resulting from the quantum Hamiltonian evolution and random quantum circuits up to $8$ qubits. Results show that those quantum processes could be reconstructed with high fidelity, while the number of input states required are at least $2$ orders of magnitude less than required by the standard quantum process tomography.

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Cited by in corpus (13)

• Learning Quantum Systems
• Near-Term Quantum Computing Techniques: Variational Quantum Algorithms, Error Mitigation, Circuit Compilation, Benchmarking and Classical Simulation
• Classical Shadows for Quantum Process Tomography on Near-term Quantum Computers
• Gradient-descent quantum process tomography by learning Kraus operators
• Generalization of Quantum Machine Learning Models Using Quantum Fisher Information Metric
• Machine Learning for Estimation and Control of Quantum Systems
• Experimental quantum natural gradient optimization in photonics
• Optimal depth and a novel approach to variational quantum process tomography
• Quantum state tomography with disentanglement algorithm
• Learning unitaries with quantum statistical queries
• Reinforcement learning to learn quantum states for Heisenberg scaling accuracy
• Lindbladian reverse engineering for general non-equilibrium steady states: A scalable null-space approach
• Quantum Machine Learning for State Tomography Using Classical Data