Variational Hamiltonian simulation for translational invariant systems
via classical pre-processing
- URL: http://arxiv.org/abs/2106.03680v5
- Date: Mon, 6 Mar 2023 13:02:39 GMT
- Title: Variational Hamiltonian simulation for translational invariant systems
via classical pre-processing
- Authors: Refik Mansuroglu, Timo Eckstein, Ludwig N\"utzel, Samuel A. Wilkinson,
and Michael J. Hartmann
- Abstract summary: We introduce a variational algorithm which uses solutions of classical optimizations to predict efficient quantum circuits.
Our strategy can improve upon the Trotter- Suzuki accuracy by several orders of magnitude.
We can extrapolate our method to beyond classically simulatable system sizes.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: The simulation of time evolution of large quantum systems is a classically
challenging and in general intractable task, making it a promising application
for quantum computation. A Trotter-Suzuki approximation yields an
implementation thereof, where a higher approximation accuracy can be traded for
an increased gate count. In this work, we introduce a variational algorithm
which uses solutions of classical optimizations to predict efficient quantum
circuits for time evolution of translationally invariant quantum systems. Our
strategy can improve upon the Trotter-Suzuki accuracy by several orders of
magnitude. It translates into a reduction in gate count and hence gain in
overall fidelity at the same algorithmic accuracy. This is important in
NISQ-applications where the fidelity of the output state decays exponentially
with the number of gates. The performance advantage of our classical assisted
strategy can be extended to open boundaries with translational symmetry in the
bulk. We can extrapolate our method to beyond classically simulatable system
sizes, maintaining its total fidelity advantage over a Trotter-Suzuki
approximation making it an interesting candidate for beyond classical time
evolution.
Related papers
- Application of Langevin Dynamics to Advance the Quantum Natural Gradient Optimization Algorithm [47.47843839099175]
A Quantum Natural Gradient (QNG) algorithm for optimization of variational quantum circuits has been proposed recently.
In this study, we employ the Langevin equation with a QNG force to demonstrate that its discrete-time solution gives a generalized form, which we call Momentum-QNG.
arXiv Detail & Related papers (2024-09-03T15:21:16Z) - 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) - Nonadiabatic geometric quantum gates with on-demand trajectories [2.5539863252714636]
We propose a general protocol for constructing geometric quantum gates with on-demand trajectories.
Our scheme adopts reverse engineering of the target Hamiltonian using smooth pulses.
Because a particular geometric gate can be induced by various different trajectories, we can further optimize the gate performance.
arXiv Detail & Related papers (2024-01-20T06:57:36Z) - Challenges of variational quantum optimization with measurement shot noise [0.0]
We study the scaling of the quantum resources to reach a fixed success probability as the problem size increases.
Our results suggest that hybrid quantum-classical algorithms should possibly avoid a brute force classical outer loop.
arXiv Detail & Related papers (2023-07-31T18:01:15Z) - Problem specific classical optimization of Hamiltonian simulation [1.602751335094621]
We present a classical pre-processing routine for variational Hamiltonian simulation.
We show that there always exists potential for optimization with respect to a Trotter sequence of the same order.
We find accuracy improvements of more than three orders of magnitude for our method as compared to Trotter sequences of the same gate number.
arXiv Detail & Related papers (2023-06-12T16:12:08Z) - Making Trotterization adaptive and energy-self-correcting for NISQ
devices and beyond [0.0]
Simulation of continuous time evolution requires time discretization on both classical and quantum computers.
We introduce a quantum algorithm to solve this problem, providing a controlled solution of the quantum many-body dynamics of local observables.
Our algorithm can be potentially useful on a more general level whenever time discretization is involved concerning, for instance, also numerical approaches based on time-evolving block decimation methods.
arXiv Detail & Related papers (2022-09-26T12:54:32Z) - Fundamental limitations on optimization in variational quantum
algorithms [7.165356904023871]
A leading paradigm to establish such near-term quantum applications is variational quantum algorithms (VQAs)
We prove that for a broad class of such random circuits, the variation range of the cost function vanishes exponentially in the number of qubits with a high probability.
This result can unify the restrictions on gradient-based and gradient-free optimizations in a natural manner and reveal extra harsh constraints on the training landscapes of VQAs.
arXiv Detail & Related papers (2022-05-10T17:14:57Z) - Quantum algorithms for grid-based variational time evolution [36.136619420474766]
We propose a variational quantum algorithm for performing quantum dynamics in first quantization.
Our simulations exhibit the previously observed numerical instabilities of variational time propagation approaches.
arXiv Detail & Related papers (2022-03-04T19:00:45Z) - 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) - Accelerating variational quantum algorithms with multiple quantum
processors [78.36566711543476]
Variational quantum algorithms (VQAs) have the potential of utilizing near-term quantum machines to gain certain computational advantages.
Modern VQAs suffer from cumbersome computational overhead, hampered by the tradition of employing a solitary quantum processor to handle large data.
Here we devise an efficient distributed optimization scheme, called QUDIO, to address this issue.
arXiv Detail & Related papers (2021-06-24T08:18:42Z) - Quantum-optimal-control-inspired ansatz for variational quantum
algorithms [105.54048699217668]
A central component of variational quantum algorithms (VQA) is the state-preparation circuit, also known as ansatz or variational form.
Here, we show that this approach is not always advantageous by introducing ans"atze that incorporate symmetry-breaking unitaries.
This work constitutes a first step towards the development of a more general class of symmetry-breaking ans"atze with applications to physics and chemistry problems.
arXiv Detail & Related papers (2020-08-03T18:00:05Z)
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.