Efficient sampling of ground and low-energy Ising spin configurations
with a coherent Ising machine
- URL: http://arxiv.org/abs/2103.05629v2
- Date: Fri, 28 Jan 2022 02:02:30 GMT
- Title: Efficient sampling of ground and low-energy Ising spin configurations
with a coherent Ising machine
- Authors: Edwin Ng, Tatsuhiro Onodera, Satoshi Kako, Peter L. McMahon, Hideo
Mabuchi, Yoshihisa Yamamoto
- Abstract summary: We show that the nonlinear dynamics of a measurement-feedback-based coherent Ising machine can be exploited to sample ground and low-energy spin configurations.
We formulate a general discrete-time Gaussian-state model of the MFB-CIM which faithfully captures the nonlinear dynamics present at and above system threshold.
- Score: 1.4221284461126298
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: We show that the nonlinear stochastic dynamics of a
measurement-feedback-based coherent Ising machine (MFB-CIM) in the presence of
quantum noise can be exploited to sample degenerate ground and low-energy spin
configurations of the Ising model. We formulate a general discrete-time
Gaussian-state model of the MFB-CIM which faithfully captures the nonlinear
dynamics present at and above system threshold. This model overcomes the
limitations of both mean-field models, which neglect quantum noise, and
continuous-time models, which assume long photon lifetimes. Numerical
simulations of our model show that when the MFB-CIM is operated in a
quantum-noise-dominated regime with short photon lifetimes (i.e., low cavity
finesse), homodyne monitoring of the system can efficiently produce samples of
low-energy Ising spin configurations, requiring many fewer roundtrips to sample
than suggested by established high-finesse, continuous-time models. We find
that sampling performance is robust to, or even improved by, turning off or
altogether reversing the sign of the parametric drive, but performance is
critically reduced in the absence of optical nonlinearity. For the class of
MAX-CUT problems with binary-signed edge weights, the number of roundtrips
sufficient to fully sample all spin configurations up to the first-excited
Ising energy, including all degeneracies, scales as $1.08^N$. At a problem size
of $N = 100$ with a few dozen (median of 20) such desired configurations per
instance, we have found median sufficient sampling times of $6\times10^6$
roundtrips; in an experimental implementation of an MFB-CIM with a 10 GHz
repetition rate, this corresponds to a wall-clock sampling time of 60 ms.
Related papers
- Partition Function Estimation Using Analog Quantum Processors [4.239775815863115]
We evaluate using programmable superconducting flux qubit D-Wave quantum annealers to approximate the partition function of Ising models.<n>We find that fast quench-like anneals can quickly generate ensemble distributions that are very good estimates of the true partition function of the classical Ising model.
arXiv Detail & Related papers (2025-12-22T18:58:46Z) - Equilibrium Matching: Generative Modeling with Implicit Energy-Based Models [52.74448905289362]
EqM is a generative modeling framework built from an equilibrium dynamics perspective.<n>By replacing time-conditional velocities with a unified equilibrium landscape, EqM offers a tighter bridge between flow and energy-based models.
arXiv Detail & Related papers (2025-10-02T17:59:06Z) - Resolving Turbulent Magnetohydrodynamics: A Hybrid Operator-Diffusion Framework [0.2999888908665658]
Hybrid machine learning framework is trained on a comprehensive ensemble of high-fidelity simulations with $mathrmRe in 100, 250, 500, 750, 1000, 3000, 10000$.<n>At extreme turbulence levels, it remains the first surrogate capable of recovering the high-wavenumber evolution of the magnetic field.
arXiv Detail & Related papers (2025-07-02T19:33:57Z) - Learning to Dissipate Energy in Oscillatory State-Space Models [51.98491034847041]
State-space models (SSMs) are a class of networks for sequence learning.<n>We show that D-LinOSS consistently outperforms previous LinOSS methods on long-range learning tasks.
arXiv Detail & Related papers (2025-05-17T23:15:17Z) - Predicting sampling advantage of stochastic Ising Machines for Quantum Simulations [0.0]
We investigate the computational advantage of sIM for simulations of quantum magnets with neural-network quantum states (NQS)
We study the sampling performance of sIM for NQS by comparing sampling on a software-emulated sIM with standard Metropolis-Hastings sampling for NQS.
For the quantum Heisenberg models studied and experimental results on the runtime of sIMs, we project a possible speed-up of 100 to 10000.
arXiv Detail & Related papers (2025-04-25T14:01:00Z) - Rapid quantum ground state preparation via dissipative dynamics [3.3187923242469246]
dissipation has become a promising approach for preparing low-energy states of quantum systems.
However, the potential of dissipative protocols remains unclear beyond certain commuting Hamiltonians.
This work provides significant analytical and numerical insights into the power of dissipation for preparing the ground state of non-commuting Hamiltonians.
arXiv Detail & Related papers (2025-03-20T03:27:52Z) - Turning qubit noise into a blessing: Automatic state preparation and long-time dynamics for impurity models on quantum computers [0.0]
We show that in the dynamical mean field theory approach to strongly-correlated systems, noise can be harnessed to our advantage.
We propose a circuit that harvests amplitude damping to reproduce the dynamics of this model with a blend of noisy and noiseless qubits.
arXiv Detail & Related papers (2024-12-18T10:52:33Z) - Emergent Equilibrium in All-Optical Single Quantum-Trajectory Ising Machines [0.0]
We investigate the dynamics of multi-mode optical systems driven by two-photon processes and subject to non-local losses, incorporating quantum noise at the Gaussian level.
Our findings show that the statistics retrieved from a single Gaussian quantum trajectory exhibits emergent thermal equilibrium governed by an Ising Hamiltonian, encoded in the dissipative coupling between modes.
arXiv Detail & Related papers (2024-12-17T10:31:55Z) - Model Order Reduction for Open Quantum Systems Based on Measurement-adapted Time-coarse Graining [9.507267560064669]
We present a model order reduction technique to reduce the time complexity of open quantum systems.
The method organizes corrections to the lowest-order model which aligns with the RWA Hamiltonian in certain limits.
We derive the fourth-order EQME for a challenging problem related to the dynamics of a superconducting qubit.
arXiv Detail & Related papers (2024-10-30T15:26:42Z) - Simulating Schwinger model dynamics with quasi-one-dimensional qubit arrays [0.0]
We develop a strategy to run Schwinger model dynamics on synthetic quantum spin lattices.
We show that global magnetic field patterns can drive coherent quantum dynamics of the interface equivalent to the lattice Schwinger Hamiltonian.
This work opens up a path for near-term quantum simulators to address questions of immediate relevance to particle physics.
arXiv Detail & Related papers (2024-09-22T17:58:25Z) - Fourier Neural Operators for Learning Dynamics in Quantum Spin Systems [77.88054335119074]
We use FNOs to model the evolution of random quantum spin systems.
We apply FNOs to a compact set of Hamiltonian observables instead of the entire $2n$ quantum wavefunction.
arXiv Detail & Related papers (2024-09-05T07:18:09Z) - Iterated Denoising Energy Matching for Sampling from Boltzmann Densities [109.23137009609519]
Iterated Denoising Energy Matching (iDEM)
iDEM alternates between (I) sampling regions of high model density from a diffusion-based sampler and (II) using these samples in our matching objective.
We show that the proposed approach achieves state-of-the-art performance on all metrics and trains $2-5times$ faster.
arXiv Detail & Related papers (2024-02-09T01:11:23Z) - Capturing dynamical correlations using implicit neural representations [85.66456606776552]
We develop an artificial intelligence framework which combines a neural network trained to mimic simulated data from a model Hamiltonian with automatic differentiation to recover unknown parameters from experimental data.
In doing so, we illustrate the ability to build and train a differentiable model only once, which then can be applied in real-time to multi-dimensional scattering data.
arXiv Detail & Related papers (2023-04-08T07:55:36Z) - Modeling the space-time correlation of pulsed twin beams [68.8204255655161]
Entangled twin-beams generated by parametric down-conversion are among the favorite sources for imaging-oriented applications.
We propose a semi-analytic model which aims to bridge the gap between time-consuming numerical simulations and the unrealistic plane-wave pump theory.
arXiv Detail & Related papers (2023-01-18T11:29:49Z) - Algorithmic Shadow Spectroscopy [0.0]
We present a simulator-agnostic quantum algorithm for estimating energy gaps using very few circuit repetitions (shots) and no extra resources (ancilla qubits)
We demonstrate that our method is intuitively easy to use in practice, robust against gate noise, to a new type of algorithmic error mitigation technique, and uses orders of magnitude fewer number of shots than typical near-term quantum algorithms -- as low as 10 shots per timestep is sufficient.
arXiv Detail & Related papers (2022-12-21T14:23:48Z) - Diffusion Probabilistic Model Made Slim [128.2227518929644]
We introduce a customized design for slim diffusion probabilistic models (DPM) for light-weight image synthesis.
We achieve 8-18x computational complexity reduction as compared to the latent diffusion models on a series of conditional and unconditional image generation tasks.
arXiv Detail & Related papers (2022-11-27T16:27:28Z) - Faster spectral density calculation using energy moments [77.34726150561087]
We reformulate the recently proposed Gaussian Integral Transform technique in terms of Fourier moments of the system Hamiltonian.
One of the main advantages of this framework is that it allows for an important reduction of the computational cost.
arXiv Detail & Related papers (2022-11-01T23:57:58Z) - Efficient simulation of ultrafast quantum nonlinear optics with matrix
product states [0.0]
We develop an algorithm to unravel the MPS quantum state into constituent temporal supermodes.
We observe the development of non-classical Wigner-function negativity in the solitonic mode and quantum corrections to the semiclassical dynamics of the pulse.
arXiv Detail & Related papers (2021-02-11T09:15:24Z) - Fast and differentiable simulation of driven quantum systems [58.720142291102135]
We introduce a semi-analytic method based on the Dyson expansion that allows us to time-evolve driven quantum systems much faster than standard numerical methods.
We show results of the optimization of a two-qubit gate using transmon qubits in the circuit QED architecture.
arXiv Detail & Related papers (2020-12-16T21:43:38Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.