Optimal depth and a novel approach to variational quantum process tomography

• URL: http://arxiv.org/abs/2404.16541v1
• Date: Thu, 25 Apr 2024 11:58:06 GMT
• Title: Optimal depth and a novel approach to variational quantum process tomography
• Authors: Vladlen Galetsky, Pol Julià Farré, Soham Ghosh, Christian Deppe, Roberto Ferrara,
• Abstract summary: We present two new methods for Variational Quantum Circuit (VQC) Process Tomography onto $n$ qubits systems: PT_VQC and U-VQSVD. Compared to the state of the art, PT_VQC halves in each run the required amount of qubits for process tomography. U-VQSVD outperforms an uninformed attack (using randomly generated input states) by a factor of 2 to 5, depending on the qubit dimension.
• Score: 11.496254312838659
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In this work, we present two new methods for Variational Quantum Circuit (VQC) Process Tomography onto $n$ qubits systems: PT_VQC and U-VQSVD. Compared to the state of the art, PT_VQC 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, PT_VQC 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.

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