Scalable Simulation of Quantum Many-Body Dynamics with Or-Represented Quantum Algebra
- URL: http://arxiv.org/abs/2506.13241v1
- Date: Mon, 16 Jun 2025 08:41:54 GMT
- Title: Scalable Simulation of Quantum Many-Body Dynamics with Or-Represented Quantum Algebra
- Authors: Lukas Broers, Rong-Yang Sun, Seiji Yunoki,
- Abstract summary: We present a scalable and general-purpose parallel algorithm for quantum simulations based on or-represented quantum algebra (ORQA)<n>We simulate the kicked Ising model on a 127-qubit heavy-hexagon lattice, tracking the time evolution of local magnetization using up to one trillion Pauli strings.
- Score: 0.0
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: High-performance numerical methods are essential not only for advancing quantum many-body physics but also for enabling integration with emerging quantum computing platforms. We present a scalable and general-purpose parallel algorithm for quantum simulations based on or-represented quantum algebra (ORQA). This framework applies to arbitrary spin systems and naturally integrates with quantum circuit simulation in the Heisenberg picture, particularly relevant to recent large-scale experiments on superconducting qubit processors [Kim et al., Nature 618, 500 (2023)]. As a benchmark, we simulate the kicked Ising model on a 127-qubit heavy-hexagon lattice, tracking the time evolution of local magnetization using up to one trillion Pauli strings. Executed on the supercomputer Fugaku, our simulations exhibit strong scaling up to $2^{17}$ parallel processes with near-linear communication overhead. These results establish ORQA as a practical and high-performance tool for quantum many-body dynamics, and highlight its potential for integration into hybrid quantum-classical computational frameworks, complementing recent advances in tensor-network and surrogate simulation techniques.
Related papers
- VQC-MLPNet: An Unconventional Hybrid Quantum-Classical Architecture for Scalable and Robust Quantum Machine Learning [60.996803677584424]
Variational Quantum Circuits (VQCs) offer a novel pathway for quantum machine learning.<n>Their practical application is hindered by inherent limitations such as constrained linear expressivity, optimization challenges, and acute sensitivity to quantum hardware noise.<n>This work introduces VQC-MLPNet, a scalable and robust hybrid quantum-classical architecture designed to overcome these obstacles.
arXiv Detail & Related papers (2025-06-12T01:38:15Z) - Efficient Learning for Linear Properties of Bounded-Gate Quantum Circuits [63.733312560668274]
Given a quantum circuit containing d tunable RZ gates and G-d Clifford gates, can a learner perform purely classical inference to efficiently predict its linear properties?
We prove that the sample complexity scaling linearly in d is necessary and sufficient to achieve a small prediction error, while the corresponding computational complexity may scale exponentially in d.
We devise a kernel-based learning model capable of trading off prediction error and computational complexity, transitioning from exponential to scaling in many practical settings.
arXiv Detail & Related papers (2024-08-22T08:21:28Z) - Parallel Quantum Computing Simulations via Quantum Accelerator Platform Virtualization [44.99833362998488]
We present a model for parallelizing simulation of quantum circuit executions.
The model can take advantage of its backend-agnostic features, enabling parallel quantum circuit execution over any target backend.
arXiv Detail & Related papers (2024-06-05T17:16:07Z) - Classical Chaos in Quantum Computers [39.58317527488534]
Current-day quantum processors, comprising 50-100 qubits, operate outside the range of quantum simulation on classical computers.
We demonstrate that the simulation of classical limits can be a potent diagnostic tool potentially mitigating this problem.
We find that classical and quantum simulations lead to similar stability metrics in systems with $mathcalO$ transmons.
arXiv Detail & Related papers (2023-04-27T18:00:04Z) - Differentiable matrix product states for simulating variational quantum
computational chemistry [6.954927515599816]
We propose a parallelizable classical simulator for variational quantum eigensolver(VQE)
Our simulator seamlessly integrates the quantum circuit evolution into the classical auto-differentiation framework.
As applications, we use our simulator to study commonly used small molecules such as HF, LiH and H$$O, as well as larger molecules CO$$, BeH$ and H$_4$ with up to $40$ qubits.
arXiv Detail & Related papers (2022-11-15T08:36:26Z) - QuDiet: A Classical Simulation Platform for Qubit-Qudit Hybrid Quantum
Systems [7.416447177941264]
textbfQuDiet is a python-based higher-dimensional quantum computing simulator.
textbfQuDiet offers multi-valued logic operations by utilizing generalized quantum gates.
textbfQuDiet provides a full qubit-qudit hybrid quantum simulator package.
arXiv Detail & Related papers (2022-11-15T06:07:04Z) - Recompilation-enhanced simulation of electron-phonon dynamics on IBM
Quantum computers [62.997667081978825]
We consider the absolute resource cost for gate-based quantum simulation of small electron-phonon systems.
We perform experiments on IBM quantum hardware for both weak and strong electron-phonon coupling.
Despite significant device noise, through the use of approximate circuit recompilation we obtain electron-phonon dynamics on current quantum computers comparable to exact diagonalisation.
arXiv Detail & Related papers (2022-02-16T19:00:00Z) - Holographic dynamics simulations with a trapped ion quantum computer [0.0]
We demonstrate and benchmark a new scalable quantum simulation paradigm.
Using a Honeywell trapped ion quantum processor, we simulate the non-integrable dynamics of the self-dual kicked Ising model.
Results suggest that quantum tensor network methods, together with state-of-the-art quantum processor capabilities, enable a viable path to practical quantum advantage in the near term.
arXiv Detail & Related papers (2021-05-19T18:00:02Z) - Tensor Network Quantum Virtual Machine for Simulating Quantum Circuits
at Exascale [57.84751206630535]
We present a modernized version of the Quantum Virtual Machine (TNQVM) which serves as a quantum circuit simulation backend in the e-scale ACCelerator (XACC) framework.
The new version is based on the general purpose, scalable network processing library, ExaTN, and provides multiple quantum circuit simulators.
By combining the portable XACC quantum processors and the scalable ExaTN backend we introduce an end-to-end virtual development environment which can scale from laptops to future exascale platforms.
arXiv Detail & Related papers (2021-04-21T13:26:42Z) - Preparing random states and benchmarking with many-body quantum chaos [48.044162981804526]
We show how to predict and experimentally observe the emergence of random state ensembles naturally under time-independent Hamiltonian dynamics.
The observed random ensembles emerge from projective measurements and are intimately linked to universal correlations built up between subsystems of a larger quantum system.
Our work has implications for understanding randomness in quantum dynamics, and enables applications of this concept in a wider context.
arXiv Detail & Related papers (2021-03-05T08:32:43Z) - Bit-Slicing the Hilbert Space: Scaling Up Accurate Quantum Circuit
Simulation to a New Level [10.765480856320018]
We enhance quantum circuit simulation in two dimensions: accuracy and scalability.
Experimental results demonstrate that our method can be superior to the state-of-the-art for various quantum circuits.
arXiv Detail & Related papers (2020-07-18T01:26:40Z)
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.