Digital quantum simulator for the time-dependent Dirac equation using
discrete-time quantum walks
- URL: http://arxiv.org/abs/2305.19568v1
- Date: Wed, 31 May 2023 05:36:57 GMT
- Title: Digital quantum simulator for the time-dependent Dirac equation using
discrete-time quantum walks
- Authors: Shigetora Miyashita, Takahiko Satoh, Michihiko Sugawara, Naphan
Benchasattabuse, Ken M. Nakanishi, Michal Hajdu\v{s}ek, Hyensoo Choi, Rodney
Van Meter
- Abstract summary: We introduce a quantum algorithm for simulating the time-dependent Dirac equation in 3+1 dimensions using discrete-time quantum walks.
Our findings indicate that relativistic dynamics is achievable with quantum computers.
- Score: 0.7036032466145112
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: We introduce a quantum algorithm for simulating the time-dependent Dirac
equation in 3+1 dimensions using discrete-time quantum walks. Thus far,
promising quantum algorithms have been proposed to simulate quantum dynamics in
non-relativistic regimes efficiently. However, only some studies have attempted
to simulate relativistic dynamics due to its theoretical and computational
difficulty. By leveraging the convergence of discrete-time quantum walks to the
Dirac equation, we develop a quantum spectral method that approximates smooth
solutions with exponential convergence. This mitigates errors in implementing
potential functions and reduces the overall gate complexity that depends on
errors. We demonstrate that our approach does not require additional operations
compared to the asymptotic gate complexity of non-relativistic real-space
algorithms. Our findings indicate that simulating relativistic dynamics is
achievable with quantum computers and can provide insights into relativistic
quantum physics and chemistry.
Related papers
- 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) - A quantum computing concept for 1-D elastic wave simulation with exponential speedup [0.0]
We present a quantum computing concept for 1-D elastic wave propagation in heterogeneous media.
The method rests on a finite-difference approximation, followed by a sparsity-preserving transformation of the discrete elastic wave equation to a Schr"odinger equation.
An implementation on an error-free quantum simulator verifies our approach and forms the basis of numerical experiments.
arXiv Detail & Related papers (2023-12-22T14:58:01Z) - Quantum computing of reacting flows via Hamiltonian simulation [13.377719901871027]
We develop the quantum spectral and finite difference methods for simulating reacting flows in periodic and general conditions.
The present quantum computing algorithms offer a one-shot'' solution for a given time without temporal discretization.
arXiv Detail & Related papers (2023-12-13T04:31:49Z) - Dense outputs from quantum simulations [5.295277584890625]
The quantum dense output problem is the process of evaluating time-accumulated observables from time-dependent quantum dynamics.
This problem arises frequently in applications such as quantum control and spectroscopic computation.
We present a range of algorithms designed to operate on both early and fully fault-tolerant quantum platforms.
arXiv Detail & Related papers (2023-07-26T18:16:51Z) - Practical limitations of quantum data propagation on noisy quantum processors [0.9362259192191963]
We show that owing to the noisy nature of current quantum processors, such a quantum algorithm will require single- and two-qubit gates with very low error probability to produce reliable results.
Specifically, we provide the upper bounds on how the relative error in variational parameters' propagation scales with the probability of noise in quantum hardware.
arXiv Detail & Related papers (2023-06-22T17:12:52Z) - Quantum Clustering with k-Means: a Hybrid Approach [117.4705494502186]
We design, implement, and evaluate three hybrid quantum k-Means algorithms.
We exploit quantum phenomena to speed up the computation of distances.
We show that our hybrid quantum k-Means algorithms can be more efficient than the classical version.
arXiv Detail & Related papers (2022-12-13T16:04:16Z) - Time-dependent Hamiltonian Simulation of Highly Oscillatory Dynamics and
Superconvergence for Schr\"odinger Equation [2.973326951020451]
We propose a simple quantum algorithm for simulating highly oscillatory quantum dynamics.
To our knowledge, this is the first quantum algorithm that is both insensitive to the rapid changes of the time-dependent Hamiltonian and exhibits commutator scaling.
For the simulation of the Schr"odinger equation, our method exhibits superconvergence and achieves a surprising second order convergence rate.
arXiv Detail & Related papers (2021-11-04T18:50:36Z) - 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) - Fixed Depth Hamiltonian Simulation via Cartan Decomposition [59.20417091220753]
We present a constructive algorithm for generating quantum circuits with time-independent depth.
We highlight our algorithm for special classes of models, including Anderson localization in one dimensional transverse field XY model.
In addition to providing exact circuits for a broad set of spin and fermionic models, our algorithm provides broad analytic and numerical insight into optimal Hamiltonian simulations.
arXiv Detail & Related papers (2021-04-01T19:06:00Z) - Continuous-time dynamics and error scaling of noisy highly-entangling
quantum circuits [58.720142291102135]
We simulate a noisy quantum Fourier transform processor with up to 21 qubits.
We take into account microscopic dissipative processes rather than relying on digital error models.
We show that depending on the dissipative mechanisms at play, the choice of input state has a strong impact on the performance of the quantum algorithm.
arXiv Detail & Related papers (2021-02-08T14:55:44Z) - Information Scrambling in Computationally Complex Quantum Circuits [56.22772134614514]
We experimentally investigate the dynamics of quantum scrambling on a 53-qubit quantum processor.
We show that while operator spreading is captured by an efficient classical model, operator entanglement requires exponentially scaled computational resources to simulate.
arXiv Detail & Related papers (2021-01-21T22:18:49Z)
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.