Quantum-inspired dynamical models on quantum and classical annealers
arXiv:2509.03952 · doi:10.1103/rrf3-jm5m
Abstract
We propose a practical, physics-inspired benchmarking suite to challenge both quantum and classical computers by mapping real-time quantum dynamics to a common optimization format. Using a parallel-in-time encoding, we convert the real-time propagator of an -qubit, possibly non-Hermitian, Hamiltonian into quadratic unconstrained binary optimization (QUBO) instances that are executable in a solver-agnostic manner on quantum annealers and classical optimizers alike. This enables direct, like-for-like performance comparisons across fundamentally different computational paradigms.To stress-test the framework, we consider eight representative dynamical models spanning single-qubit rotations, multi-qubit entangling gates (Bell, GHZ, cluster), and PT-symmetric and other non-Hermitian generators, and evaluate success probability and time-to-solution as standard benchmarking metrics. Applying this methodology to two generations of D-Wave quantum annealers and to state-of-the-art classical solvers (Simulated Annealing and the GPU-accelerated VeloxQ), we find that Advantage2 consistently outperforms its predecessor, while VeloxQ retains the shortest absolute runtimes, reflecting the maturity of classical heuristics.We further extend the benchmarks to large-scale instances (), establishing a demanding classical baseline for future hardware. Together, these results position the parallel-in-time QUBO framework as a versatile and physically motivated testbed for quantitatively tracking progress toward quantum-competitive simulation of dynamical systems.
12 pages, 7 figures
References in corpus (39)
- Supplementary information for "Quantum supremacy using a programmable superconducting processor"
- A variational eigenvalue solver on a quantum processor
- Quantum Simulation
- Probing many-body dynamics on a 51-atom quantum simulator
- Ising formulations of many NP problems
- QuTiP 2: A Python framework for the dynamics of open quantum systems
- Quantum computational advantage using photons
- QuTiP: An open-source Python framework for the dynamics of open quantum systems
- Simulated Quantum Computation of Molecular Energies
- Strong quantum computational advantage using a superconducting quantum processor
- Universal digital quantum simulation with trapped ions
- Quantum critical dynamics in a 5000-qubit programmable spin glass
- Computational advantage of quantum random sampling
- Beyond-classical computation in quantum simulation
- Benchmarking gate-based quantum computers
- QuTiP-BoFiN: A bosonic and fermionic numerical hierarchical-equations-of-motion library with applications in light-harvesting, quantum control, and single-molecule electronics
- Quantum speedup of branch-and-bound algorithms
- -symmetric slowing-down of decoherence
- Scaling overhead of embedding optimization problems in quantum annealing
- Scaling Advantage in Approximate Optimization with Quantum Annealing
- Comparing Three Generations of D-Wave Quantum Annealers for Minor Embedded Combinatorial Optimization Problems
- Improved bound on entropy production in a quantum annealer
- Many-body localization enables iterative quantum optimization
- Quantum Simulations of Chemistry in First Quantization with any Basis Set
- Efficiency Optimization in Quantum Computing: Balancing Thermodynamics and Computational Performance
- Quantum annealing for hard 2-SAT problems : Distribution and scaling of minimum energy gap and success probability
- Cyclic Quantum Annealing: Searching for Deep Low-Energy States in 5000-Qubit Spin Glass
- Hybrid quantum-classical computation for automatic guided vehicles scheduling
- Unraveling Reverse Annealing: A Study of D-Wave Quantum Annealers
- On the Baltimore Light RailLink into the quantum future
- Performance of quantum annealing for 2-SAT problems with multiple satisfying assignments
- Computational complexity of three-dimensional Ising spin glass: Lessons from D-Wave annealer
- Solving rescheduling problems in heterogeneous urban railway networks using hybrid quantum-classical approach
- Understanding the physics of D-Wave annealers: From Schrödinger to Lindblad to Markovian Dynamics
- Rydberg atom arrays as quantum simulators for molecular dynamics
- Quantum ergodicity and scrambling in quantum annealers
- Simon's Period Finding on a Quantum Annealer
- Learning-Driven Annealing with Adaptive Hamiltonian Modification for Solving Large-Scale Problems on Quantum Devices
- Quantum sequel of neural network training