Coherent-state propagation enables quasi-polynomial classical simulation of bosonic circuits with logarithmically many Kerr gates at exponentially small trace-distance error, with polynomial runtime in the weak-nonlinearity regime.
hub
Quantum annealing in the transverse Ising model
16 Pith papers cite this work, alongside 2,361 external citations. Polarity classification is still indexing.
16
Pith papers citing it
2,361
external citations · OpenAlex
hub tools
citation-role summary
method 1
citation-polarity summary
roles
method 1polarities
use method 1representative citing papers
Establishes discrete log-concavity of ground states for convex potentials and extends Reichardt's HWS tunneling analysis to quadratic spikes via new spectral gap bounds.
New dual-polarity Ising/QUBO framework encodes SAT so that ground states correspond to short implicants, with parameter regimes for minimality and empirical results leaving about one-third of variables unassigned on random 3-SAT.
A multi-mode quantum annealing approach enables VAEs with Boltzmann priors, showing faster training and better generation than Gaussian-prior VAEs on MNIST, Fashion-MNIST, and CelebA plus improved out-of-distribution detection.
AtomTreeSearch embeds a neutral-atom quantum MWIS subroutine inside Monte Carlo Tree Search and matches or exceeds OR-Tools and simulated annealing on TSP instances up to 100 cities.
Constructs and proves correct a QUBO Hamiltonian H_mod,k whose zero-energy ground states exist exactly when a graph admits a nowhere-zero Z_k-flow, with degeneracy matching the flow polynomial.
The hybrid BOCS-GP method with adaptive LCB selection finds better objective values than random-point addition in QUBO and HUBO by selecting points that promote search progress within Hamming-distance neighborhoods.
A logarithmic HUBO encoding with a lexicographic penalty solves minimum graph coloring and related partition-count problems using exponentially fewer qubits per vertex than one-hot encoding.
PDQUBO is a new performance-driven QUBO method for feature selection in recommender systems that incorporates counterfactual performance impacts of features and pairs, is model-agnostic, and outperforms prior quantum and some classical baselines on CTR tasks.
QADR decomposes n-qubit VQCs into local sub-circuits to reduce memory from O(2^n) to O(n * 2^{2d+1}) and mitigate barren plateaus, scaling to 2000 features on MNIST and wind turbine diagnostics while matching classical models.
Systematic D-Wave experiments across Max-Cut, Number Partitioning, and sparse clustering show reverse annealing yields larger efficiency gains than longer forward anneals, with benefits growing for larger, more complex instances.
Apollo is a room-temperature 10000-node CMOS neuromorphic chip whose p-qubit network emulates transverse-field quantum annealing via Suzuki-Trotter and reportedly achieves lower energies than cryogenic QA on 3D spin-glass benchmarks across 300 realizations.
The authors cast rolling stock planning as maximum-weight independent set on a cycle graph, then apply a divide-and-conquer hybrid that solves subgraphs with QAOA (simulated and on IQM Emerald) and show larger subgraphs yield better solutions than smaller ones or pure classical heuristics.
A quantum-inspired framework using effective Hamiltonians, Metropolis annealing and stochastic tensor-network compression is proposed for adaptive multi-demand routing in large-scale QKD networks.
GPU-based quantum-annealing-inspired algorithms outperform both quantum processors and industry classical solvers in sampling speed and full runtime on MO-MaxCut instances.
Proposes a generative-AI framework integrating smart metering, quantum-inspired optimization for gas distribution, billing, and carbon analytics in energy infrastructure.
citing papers explorer
-
Coherent-State Propagation: A Computational Framework for Simulating Bosonic Quantum Systems
Coherent-state propagation enables quasi-polynomial classical simulation of bosonic circuits with logarithmically many Kerr gates at exponentially small trace-distance error, with polynomial runtime in the weak-nonlinearity regime.
-
Log-concavity and tunneling: adiabatic quantum optimization for convex functions (with a spike)
Establishes discrete log-concavity of ground states for convex potentials and extends Reichardt's HWS tunneling analysis to quadratic spikes via new spectral gap bounds.
-
Computing Short SAT Implicants via Ising/QUBO Encodings
New dual-polarity Ising/QUBO framework encodes SAT so that ground states correspond to short implicants, with parameter regimes for minimality and empirical results leaving about one-third of variables unassigned on random 3-SAT.
-
Multi-Mode Quantum Annealing for Generative Representation Learning with Boltzmann Priors
A multi-mode quantum annealing approach enables VAEs with Boltzmann priors, showing faster training and better generation than Gaussian-prior VAEs on MNIST, Fashion-MNIST, and CelebA plus improved out-of-distribution detection.
-
Quantum-enhanced Monte Carlo Tree Search framework for combinatorial optimization problems
AtomTreeSearch embeds a neutral-atom quantum MWIS subroutine inside Monte Carlo Tree Search and matches or exceeds OR-Tools and simulated annealing on TSP instances up to 100 cities.
-
A QUBO Formulation for Nowhere-Zero $k$-Flows
Constructs and proves correct a QUBO Hamiltonian H_mod,k whose zero-energy ground states exist exactly when a graph admits a nowhere-zero Z_k-flow, with degeneracy matching the flow polynomial.
-
Improving search efficiency via adaptive acquisition function selection in discrete black-box optimization
The hybrid BOCS-GP method with adaptive LCB selection finds better objective values than random-point addition in QUBO and HUBO by selecting points that promote search progress within Hamming-distance neighborhoods.
-
Qubit-efficient and gate-efficient encodings of graph partitioning problems for quantum optimization
A logarithmic HUBO encoding with a lexicographic penalty solves minimum graph coloring and related partition-count problems using exponentially fewer qubits per vertex than one-hot encoding.
-
Performance-Driven QUBO for Recommender Systems on Quantum Annealers
PDQUBO is a new performance-driven QUBO method for feature selection in recommender systems that incorporates counterfactual performance impacts of features and pairs, is model-agnostic, and outperforms prior quantum and some classical baselines on CTR tasks.
-
Quantum Algorithm for Distributed Reduction of Entanglements (QADR): A Trainable and Simulation-Efficient QML Framework
QADR decomposes n-qubit VQCs into local sub-circuits to reduce memory from O(2^n) to O(n * 2^{2d+1}) and mitigate barren plateaus, scaling to 2000 features on MNIST and wind turbine diagnostics while matching classical models.
-
Extending the computational reach of Quantum Annealing using Reverse Annealing
Systematic D-Wave experiments across Max-Cut, Number Partitioning, and sparse clustering show reverse annealing yields larger efficiency gains than longer forward anneals, with benefits growing for larger, more complex instances.
-
Quantum-Driven Neuromorphic Computing for Million-Qubit-Scale Workloads
Apollo is a room-temperature 10000-node CMOS neuromorphic chip whose p-qubit network emulates transverse-field quantum annealing via Suzuki-Trotter and reportedly achieves lower energies than cryogenic QA on 3D spin-glass benchmarks across 300 realizations.
-
Rolling Stock Planning Using the Quantum Approximate Optimization Algorithm
The authors cast rolling stock planning as maximum-weight independent set on a cycle graph, then apply a divide-and-conquer hybrid that solves subgraphs with QAOA (simulated and on IQM Emerald) and show larger subgraphs yield better solutions than smaller ones or pure classical heuristics.
-
Quantum-Inspired Hamiltonian Optimization, Stochastic Tensor Networks and Adaptive Congestion Routing for Large-Scale QKD Networks
A quantum-inspired framework using effective Hamiltonians, Metropolis annealing and stochastic tensor-network compression is proposed for adaptive multi-demand routing in large-scale QKD networks.
-
Multi-Objective Optimization by Quantum-Annealing-Inspired Algorithms
GPU-based quantum-annealing-inspired algorithms outperform both quantum processors and industry classical solvers in sampling speed and full runtime on MO-MaxCut instances.
-
A Unified Generative-AI Framework for Smart Energy Infrastructure: Intelligent Gas Distribution, Utility Billing, Carbon Analytics, and Quantum-Inspired Optimisation
Proposes a generative-AI framework integrating smart metering, quantum-inspired optimization for gas distribution, billing, and carbon analytics in energy infrastructure.