Variational Quantum Sensing for Structured Linear Function Estimation

• URL: http://arxiv.org/abs/2507.22043v1
• Date: Tue, 29 Jul 2025 17:48:14 GMT
• Title: Variational Quantum Sensing for Structured Linear Function Estimation
• Authors: Priyam Srivastava, Vivek Kumar, Gurudev Dutt, Kaushik P. Seshadreesan,
• Abstract summary: We study the variational optimization of entangled probe states for quantum sensing tasks.<n>Specifically, we consider scenarios where each qubit in a spin-1/2 array accumulates a phase phi_i = alpha_i * theta.<n>We benchmark the optimized circuits for two relevant cases: (i) uniform encoding, where all qubits contribute equally to the phase function, and (ii) a custom encoding where a central qubit dominates the weight vector.
• Score: 0.7783491525078104
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We study the variational optimization of entangled probe states for quantum sensing tasks involving the estimation of a structured linear function of local phase parameters. Specifically, we consider scenarios where each qubit in a spin-1/2 array accumulates a phase phi_i = alpha_i * theta, with a known weight vector alpha, reducing the task to single-parameter estimation of theta. Using parameterized quantum circuits composed of dipolar-interacting gates and global rotations, we optimize probe states with respect to the Classical Fisher Information (CFI) using a gradient-free evolutionary strategy. We benchmark the optimized circuits for two relevant cases: (i) uniform encoding, where all qubits contribute equally to the phase function, and (ii) a custom encoding where a central qubit dominates the weight vector. In both cases, the optimized probe states approach the respective entanglement-enhanced (EE) limits dictated by the encoding structure. Our results demonstrate the power of variational approaches for tailoring metrologically useful entanglement to specific estimation tasks in quantum sensor networks.

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