Phenomenological Theory of Variational Quantum Ground-State Preparation
- URL: http://arxiv.org/abs/2205.06278v4
- Date: Thu, 15 Dec 2022 09:04:58 GMT
- Title: Phenomenological Theory of Variational Quantum Ground-State Preparation
- Authors: Nikita Astrakhantsev, Guglielmo Mazzola, Ivano Tavernelli and Giuseppe
Carleo
- Abstract summary: The variational quantum eigensolver (VQE) algorithm aims to prepare the ground state of a Hamiltonian exploiting parametrized quantum circuits.
We show that the algorithm's success crucially depends on other parameters such as the learning rate.
We propose a symmetry-enhanced simulation protocol that should be used if the gap closes.
- Score: 0.0
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: The variational approach is a cornerstone of computational physics,
considering both conventional and quantum computing computational platforms.
The variational quantum eigensolver (VQE) algorithm aims to prepare the ground
state of a Hamiltonian exploiting parametrized quantum circuits that may offer
an advantage compared to classical trial states used, for instance, in quantum
Monte Carlo or tensor network calculations. While traditionally, the main focus
has been on developing better trial circuits, we show that the algorithm's
success crucially depends on other parameters such as the learning rate, the
number $N_s$ of measurements to estimate the gradient components, and the
Hamiltonian gap $\Delta$. We first observe the existence of a finite $N_s$
value below which the optimization is impossible, and the energy variance
resembles the behavior of the specific heat in second-order phase transitions.
Secondly, when $N_s$ is above such threshold level, and learning is possible,
we develop a phenomenological model that relates the fidelity of the state
preparation with the optimization hyperparameters as well as $\Delta$. More
specifically, we observe that the computational resources scale as
$1/\Delta^2$, and we propose a symmetry-enhanced simulation protocol that
should be used if the gap closes. We test our understanding on several
instances of two-dimensional frustrated quantum magnets, which are believed to
be the most promising candidates for near-term quantum advantage through
variational quantum simulations.
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) - Spin coupling is all you need: Encoding strong electron correlation on quantum computers [0.0]
We show that quantum computers can efficiently simulate strongly correlated molecular systems by directly encoding the dominant entanglement structure in the form of spin-coupled initial states.
Our work paves the way towards scalable quantum simulation of electronic structure for classically challenging systems.
arXiv Detail & Related papers (2024-04-29T17:14:21Z) - Hybrid Quantum Classical Simulations [0.0]
We report on two major hybrid applications of quantum computing, namely, the quantum approximate optimisation algorithm (QAOA) and the variational quantum eigensolver (VQE)
Both are hybrid quantum classical algorithms as they require incremental communication between a classical central processing unit and a quantum processing unit to solve a problem.
arXiv Detail & Related papers (2022-10-06T10:49:15Z) - The Variational Quantum Eigensolver: a review of methods and best
practices [3.628860803653535]
The variational quantum eigensolver (or VQE) uses the variational principle to compute the ground state energy of a Hamiltonian.
This review aims to provide an overview of the progress that has been made on the different parts of the algorithm.
arXiv Detail & Related papers (2021-11-09T14:40:18Z) - Benchmarking variational quantum eigensolvers for the
square-octagon-lattice Kitaev model [3.6810704401578724]
Quantum spin systems may offer the first opportunities for beyond-classical quantum computations of scientific interest.
The variational quantum eigensolver (VQE) is a promising approach to finding energy eigenvalues on noisy quantum computers.
We demonstrate the implementation of HVA circuits on Rigetti's Aspen-9 chip with error mitigation.
arXiv Detail & Related papers (2021-08-30T16:58:43Z) - 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) - Realization of arbitrary doubly-controlled quantum phase gates [62.997667081978825]
We introduce a high-fidelity gate set inspired by a proposal for near-term quantum advantage in optimization problems.
By orchestrating coherent, multi-level control over three transmon qutrits, we synthesize a family of deterministic, continuous-angle quantum phase gates acting in the natural three-qubit computational basis.
arXiv Detail & Related papers (2021-08-03T17:49:09Z) - Benchmarking adaptive variational quantum eigensolvers [63.277656713454284]
We benchmark the accuracy of VQE and ADAPT-VQE to calculate the electronic ground states and potential energy curves.
We find both methods provide good estimates of the energy and ground state.
gradient-based optimization is more economical and delivers superior performance than analogous simulations carried out with gradient-frees.
arXiv Detail & Related papers (2020-11-02T19:52:04Z) - Preparation of excited states for nuclear dynamics on a quantum computer [117.44028458220427]
We study two different methods to prepare excited states on a quantum computer.
We benchmark these techniques on emulated and real quantum devices.
These findings show that quantum techniques designed to achieve good scaling on fault tolerant devices might also provide practical benefits on devices with limited connectivity and gate fidelity.
arXiv Detail & Related papers (2020-09-28T17:21:25Z) - 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) - Resource-Optimized Fermionic Local-Hamiltonian Simulation on Quantum
Computer for Quantum Chemistry [6.361119478712919]
We present a framework that enables bootstrapping the VQE progression towards the convergence of the ground-state energy of the fermionic system.
We show that resource-requirement savings of up to more than $20%$, in small instances, is possible.
arXiv Detail & Related papers (2020-04-08T17:59:13Z)
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.