Optimizing quantum sensing networks via genetic algorithms and deep learning
arXiv:2507.17460 · doi:10.1088/2058-9565/ae2d8e
Abstract
We investigate the optimization of graph topologies for quantum sensing networks designed to estimate weak magnetic fields. The sensors are modeled as spin systems governed by a transverse-field Ising Hamiltonian in thermal equilibrium at low temperatures. Using a genetic algorithm (GA), we evolve network topologies to maximize a perturbative spectral sensitivity measure, which serves as the fitness function for the GA. For the best-performing graphs, we compute the corresponding quantum Fisher information (QFI) to assess the ultimate bounds on estimation precision. To enable efficient scaling, we use the GA-generated data to train a deep neural network, allowing extrapolation to larger graph sizes where direct computation becomes prohibitive. Our results show that while both the fitness function and QFI initially increase with system size, the QFI exhibits a clear non-monotonic behavior - saturating and eventually declining beyond a critical graph size. This reflects the loss of superlinear scaling of the QFI, as the narrowing of the energy gap signals a crossover to classical scaling of the QFI with system size. The effect is reminiscent of the microeconomic law of diminishing returns: beyond an optimal graph size, further increases yield reduced sensing performance. This saturation and decline in precision are particularly pronounced under Kac scaling, where both the QFI and spin squeezing plateau or degrade with increasing system size. We also attribute observed even-odd oscillations in the spectral sensitivity measure and QFI to quantum interference effects in spin phase space, as confirmed by our phase-space analysis. These findings highlight the critical role of optimizing interaction topology - rather than simply increasing network size - and demonstrate the potential of hybrid evolutionary and learning-based approaches for designing high-performance quantum sensors.
16 pages, 15 figures
References in corpus (49)
- Quantum sensing
- Advances in Quantum Metrology
- Quantum-enhanced measurements: beating the standard quantum limit
- Quantum metrology
- Quantum metrology with nonclassical states of atomic ensembles
- Statistical mechanics and dynamics of solvable models with long-range interactions
- Programmable Quantum Simulations of Spin Systems with Trapped Ions
- Quantum metrology from a quantum information science perspective
- Quantum noise limited and entanglement-assisted magnetometry
- Sub-femtotesla scalar atomic magnetometer using multipass cells
- Magnetic sensitivity beyond the projection noise limit by spin squeezing
- Multi-parameter estimation in networked quantum sensors
- Scalable Spin Squeezing for Quantum-Enhanced Magnetometry with Bose-Einstein Condensates
- Critical Quantum metrology with a finite-component quantum phase transition
- Quantum metrology with a scanning probe atom interferometer
- Learning Quantum Systems
- Quantum Fisher information for states in exponential form
- Quantum critical metrology
- At the limits of criticality-based quantum metrology: apparent super-Heisenberg scaling revisited
- Ultrasensitive Magnetometer Using a Single Atom
- Smooth optimal quantum control for robust solid state spin magnetometry
- Improved Quantum Magnetometry beyond the Standard Quantum Limit
- Review: Quantum Metrology and Sensing with Many-Body Systems
- The SpinBus Architecture: Scaling Spin Qubits with Electron Shuttling
- Ultimate limits for quantum magnetometry via time-continuous measurements
- Quantum-enhanced magnetometry by phase estimation algorithms with a single artificial atom
- QuanEstimation: An open-source toolkit for quantum parameter estimation
- Bath-Induced Correlations Enhance Thermometry Precision at Low Temperatures
- Shot-noise-limited magnetometer with sub-pT sensitivity at room temperature
- Sequential measurements for quantum-enhanced magnetometry in spin chain probes
- Low-temperature quantum thermometry boosted by coherence generation
- Quantum walk coherences on a dynamical percolation graph
- Lattice quantum magnetometry
- Universal quantum magnetometry with spin states at equilibrium
- Phase-space interference in extensive and non-extensive quantum heat engines
- Collective quantum enhancement in critical quantum sensing
- Stochastic collision model approach to transport phenomena in quantum networks
- Optimal Thermometers with Spin Networks
- Quantum critical phenomena in a spin-1/2 frustrated square lattice with spatial anisotropy
- Current Trends in Global Quantum Metrology
- Quantum magnetometry using discrete-time quantum walk
- Neural-network-based parameter estimation for quantum detection
- Enhancing quantum state tomography via resource-efficient attention-based neural networks
- Geometrical optimization of spin clusters for the preservation of quantum coherence
- Quantum transport efficiency in noisy random-removal and small-world networks
- Identifying network topologies via quantum walk distributions
- Multiparameter estimation of continuous-time Quantum Walk Hamiltonians through Machine Learning
- Role of topology in determining the precision of a finite thermometer
- Enhanced quantum transport in chiral quantum walks