Inference-Based Quantum Sensing

Area of Science:

• Quantum Information Science
• Metrology
• Quantum Sensing

Background:

• Quantum sensing (QS) estimates unknown parameters encoded in quantum states.
• Realistic QS scenarios lack a general closed-form expression for system response R(θ).
• Characterizing R(θ) is crucial for accurate parameter estimation and performance evaluation.

Purpose of the Study:

• To present a novel inference-based quantum sensing scheme.
• To enable full characterization of system response R(θ) for a general class of unitary encodings.
• To provide a framework for inferring unknown parameters and determining sensing sensitivity.

Main Methods:

• Developed an inference-based scheme for quantum sensing.
• Showcased that R(θ) can be characterized using only 2n+1 measurements for unitary families.
• Analyzed inference error scaling with the number of measurements (shots).

Main Results:

• Inference error is bounded with high probability for a number of shots scaling as Ω(log³(n)/δ²).
• The framework is applicable to arbitrary probe states and measurement schemes.
• The method remains valid even in the presence of quantum noise.

Conclusions:

• The proposed inference-based scheme offers a robust and broadly applicable approach to quantum sensing.
• It significantly reduces the measurement resources required for accurate parameter estimation.
• The framework's validity under noise and for arbitrary states highlights its practical potential.