quantum-computational-sensing

name

quantum-computational-sensing

description

Quantum computational sensing (QCS) methodology for task-specific information extraction combining quantum sensing with quantum computing. Use for binary classification sensing tasks, quantum-enhanced signal processing, and quantum-classical hybrid sensing systems. Keywords: quantum sensing, quantum computational sensing, QCS, superconducting circuits, parameterized quantum circuits, quantum machine learning, displacement sensing, quantum-enhanced classification.

Quantum Computational Sensing (QCS)

Experimental framework for quantum computational sensing that combines quantum sensing with quantum computing to extract task-relevant information from physical signals.

Core Concepts

Quantum Computational Sensing Paradigm

• Traditional approach: Estimate signal → classical postprocessing → task output
• QCS approach: Quantum processing directly maps signal to task output
• Advantage: Higher accuracy for specific tasks vs raw-signal estimation

Key Components

1. Quantum Sensing: Physical signal encoded in quantum state
2. Parameterized Quantum Circuits: Pre-sensing and post-sensing processing
3. Binary Classification: Direct mapping to qubit ground/excited states
4. Single-Shot Measurement: Direct prediction from qubit measurement

Technical Specifications

Hardware Implementation

• Platform: Superconducting circuit (qubit + oscillator)
• Circuit Depth: Up to 24 entangling gates
• Parameters: 38 free parameters
• Training: In silico (simulation-based)

Performance Metrics

• Accuracy Improvement: 15 percentage points over conventional methods
• Expressivity: Systematically improves with circuit depth
• Robustness: Validated on noisy superconducting hardware

Workflow

Step 1: Signal Encoding

Encode complex-valued displacement in oscillator quantum state

Step 2: Pre-Sensing Quantum Processing

Apply parameterized quantum circuit before sensing

Step 3: Quantum Sensing

Physical sensing operation on oscillator

Step 4: Post-Sensing Quantum Processing

Apply parameterized quantum circuit after sensing

Step 5: Measurement

Single qubit measurement outputs prediction

Implementation

Circuit Structure

Input: Complex displacement α

Pre-sensing circuit U(θ₁):
  - Parameterized rotations
  - Entangling gates

Sensing: Coupling to oscillator

Post-sensing circuit U(θ₂):
  - Parameterized rotations
  - Entangling gates

Output: Qubit measurement → Class label

Training Strategy

1. Define binary classification task
2. Generate training data (displacements with labels)
3. Optimize circuit parameters in simulation
4. Deploy to hardware
5. Validate performance

Applications

Binary Classification Sensing

• Task: Predict class label of displacement
• Approach: Direct quantum mapping
• Advantage: No intermediate estimation needed

Quantum-Enhanced Signal Processing

• Signal property estimation
• Feature extraction
• Quantum machine learning preprocessing

References

• Paper: arXiv:2604.13177 - "Quantum computational displacement sensing"
• Category: Quantum Machine Learning / Quantum Sensing

Related Skills

• quantum-neural-network-designer
• quantum-sensing
• quantum-machine-learning