On Performance and Limitations of NISQ Hardware for Simulations of Quantum Wave Packet Dynamics
- URL: http://arxiv.org/abs/2605.20078v1
- Date: Tue, 19 May 2026 16:33:26 GMT
- Title: On Performance and Limitations of NISQ Hardware for Simulations of Quantum Wave Packet Dynamics
- Abstract summary: We investigate one-dimensional wave packet dynamics using a grid-based encoding of wave function onto qubit registers.<n>Time evolution is implemented via split-operator approach, with kinetic energy operator applied.<n>We benchmark this approach on classical simulators and quantum hardware.
- Score: 2.1204495827342438
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Digital quantum simulation offers a promising route for studying quantum dynamics, but efficient operator representations and circuit depth remain key challenges for near-term hardware. We investigate one-dimensional wave packet dynamics using a grid-based encoding of the wave function onto qubit registers. Time evolution is implemented via split-operator approach, with kinetic energy operator applied using Quantum Fourier Transform (QFT) with polynomial scaling and potential energy operator expressed through commuting Pauli-Z gates, improving accuracy and enabling incorporation of arbitrary discretized potentials. While the full Pauli decomposition of Hamiltonian scales exponentially as O(4^n ), the present approach reduces the operator scaling to O(2^n) for n qubits. We benchmark this approach on classical simulators and quantum hardware (IBM Quantum and IonQ) for two- to five-qubit implementations. For two- and three-qubit cases, all platforms qualitatively reproduce the benchmarked dynamics; at larger qubit counts, the IBM results deviate more strongly, whereas IonQ remains closer to the benchmark.
Related papers
- Hybrid Quantum State Preparation via Data Compression [0.0]
Quantum state preparation (QSP) for a general $n$qubit state requires $O(2n)$ CNOT and circuit depth.<n>We introduce an ancilla-free hybrid classical-quantum strategy that reduces this cost to $O(poly(n)$ for a broad class of compressible data.
arXiv Detail & Related papers (2025-12-01T15:32:46Z) - Solving quantum-inspired dynamics on quantum and classical annealers [0.0]
We propose a benchmarking suite inspired by physical dynamics to challenge both quantum and classical computers.<n>We convert the real-time propagator of an $n$-qubit, possibly non-Hermitian, Hamiltonian into a quadratic-unconstrained binary optimisation problem.<n>The resulting QUBO instances are executed on D-Wave quantum annealers as well as using two classical solvers, Simulated Annealing and VeloxQ.
arXiv Detail & Related papers (2025-09-04T07:27:11Z) - Vectorized Attention with Learnable Encoding for Quantum Transformer [0.6766416093990318]
We propose the Vectorized Quantum Transformer (VQT), a model that supports ideal masked attention matrix computation.<n>Our noise intermediate-scale quantum friendly VQT approach unlocks a novel architecture for end-to-end machine learning in quantum computing.
arXiv Detail & Related papers (2025-08-25T20:33:14Z) - 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) - Circuit-Efficient Qubit-Excitation-based Variational Quantum Eigensolver [7.865137519552981]
We present a circuit-efficient implementation of two-body Qubit-Excitation-Based (QEB) operator for building shallow-circuit wave function Ansatze.
This work shows great promise for quantum simulations of electronic structures, leading to improved performance on current quantum hardware.
arXiv Detail & Related papers (2024-06-17T16:16:20Z) - A Quantum-Classical Collaborative Training Architecture Based on Quantum
State Fidelity [50.387179833629254]
We introduce a collaborative classical-quantum architecture called co-TenQu.
Co-TenQu enhances a classical deep neural network by up to 41.72% in a fair setting.
It outperforms other quantum-based methods by up to 1.9 times and achieves similar accuracy while utilizing 70.59% fewer qubits.
arXiv Detail & Related papers (2024-02-23T14:09:41Z) - QuantumSEA: In-Time Sparse Exploration for Noise Adaptive Quantum
Circuits [82.50620782471485]
QuantumSEA is an in-time sparse exploration for noise-adaptive quantum circuits.
It aims to achieve two key objectives: (1) implicit circuits capacity during training and (2) noise robustness.
Our method establishes state-of-the-art results with only half the number of quantum gates and 2x time saving of circuit executions.
arXiv Detail & Related papers (2024-01-10T22:33:00Z) - Quantum emulation of the transient dynamics in the multistate
Landau-Zener model [50.591267188664666]
We study the transient dynamics in the multistate Landau-Zener model as a function of the Landau-Zener velocity.
Our experiments pave the way for more complex simulations with qubits coupled to an engineered bosonic mode spectrum.
arXiv Detail & Related papers (2022-11-26T15:04:11Z) - Performance Evaluations of Signed and Unsigned Noisy Approximate Quantum Fourier Arithmetic [3.256297688080386]
We implement QFT-based integer addition and multiplications on quantum computers.<n>These operations are fundamental to various quantum applications.<n>We evaluate these implementations based on IBM's superconducting qubit architecture.
arXiv Detail & Related papers (2021-12-17T06:51:18Z) - Quantum algorithms for quantum dynamics: A performance study on the
spin-boson model [68.8204255655161]
Quantum algorithms for quantum dynamics simulations are traditionally based on implementing a Trotter-approximation of the time-evolution operator.
variational quantum algorithms have become an indispensable alternative, enabling small-scale simulations on present-day hardware.
We show that, despite providing a clear reduction of quantum gate cost, the variational method in its current implementation is unlikely to lead to a quantum advantage.
arXiv Detail & Related papers (2021-08-09T18:00:05Z) - Quantum circuit architecture search for variational quantum algorithms [88.71725630554758]
We propose a resource and runtime efficient scheme termed quantum architecture search (QAS)
QAS automatically seeks a near-optimal ansatz to balance benefits and side-effects brought by adding more noisy quantum gates.
We implement QAS on both the numerical simulator and real quantum hardware, via the IBM cloud, to accomplish data classification and quantum chemistry tasks.
arXiv Detail & Related papers (2020-10-20T12:06:27Z)
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.