Optimal depth and a novel approach to variational quantum process tomography

In this work, we present two new methods for Variational Quantum Circuit (VQC) Process Tomography onto n qubits systems: PTVQC and U-VQSVD. Compared to the state of the art, PTVQC halves in each run the required amount of qubits for process tomography and decreases the required state initializations from 4^n to just 2^n, all while ensuring high-fidelity reconstruction of the targeted unitary channel U. It is worth noting that, for a fixed reconstruction accuracy, PTVQC achieves faster convergence per iteration step compared to Quantum Deep Neural Network (QDNN) and tensor network schemes. The novel U-VQSVD algorithm utilizes variational singular value decomposition to extract eigenvectors (up to a global phase) and their associated eigenvalues from an unknown unitary representing a general channel. We assess the performance of U-VQSVD by executing an attack on a non-unitary channel Quantum Physical Unclonable Function (QPUF). U-VQSVD outperforms an uninformed impersonation attack (using randomly generated input states) by a factor of 2 to 5, depending on the qubit dimension. For the two presented methods, we propose a new approach to calculate the complexity of the displayed VQC, based on what we denote as optimal depth.