Hunter Muench

Research

Quantum resources designed for real tasks

My work investigates how trainable photonic circuits and optimization methods can produce quantum states tailored to computation, sensing, and sampling.

Quantum neural networks for state preparation

Using differentiable continuous-variable quantum circuits to learn Gaussian resource states for specific applications.

  • Gaussian quantum circuits
  • Covariance-matrix learning
  • Loss and thermal noise
  • Energy-aware training

Measurement-based quantum computing

Optimizing graph states, measurement patterns, and feedforward operations for continuous-variable computation.

  • Cluster and graph states
  • Reinforcement learning
  • Gaussian measurements
  • Circuit compilation

Distributed quantum sensing

Designing entangled sensing networks under heterogeneous loss, local noise, correlated noise, and physical constraints.

  • Quantum Fisher information
  • Estimator optimization
  • Network topology
  • Correlated sensor noise

Gaussian Boson Sampling

Studying the preparation of graph-encoded Gaussian resources for photonic sampling tasks.

  • Gaussian graph states
  • Creation circuits
  • Target covariance matrices
  • Application-specific resources