Variational preparation of entangled states on quantum computers
- URL: http://arxiv.org/abs/2306.17422v1
- Date: Fri, 30 Jun 2023 06:29:24 GMT
- Title: Variational preparation of entangled states on quantum computers
- Authors: Vu Tuan Hai and Nguyen Tan Viet and Le Bin Ho
- Abstract summary: We propose a variational approach for preparing entangled quantum states on quantum computers.
We employ various gradient-based optimization techniques to enhance performance.
We demonstrate the effectiveness of the variational algorithm in maximizing the efficiency of quantum state preparation.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: We propose a variational approach for preparing entangled quantum states on
quantum computers. The methodology involves training a unitary operation to
match with a target unitary using the Fubini-Study distance as a cost function.
We employ various gradient-based optimization techniques to enhance
performance, including Adam and quantum natural gradient. Our investigation
showcases the versatility of different ansatzes featuring a hypergraph
structure, enabling the preparation of diverse entanglement target states such
as GHZ, W, and absolutely maximally entangled states. Remarkably, the circuit
depth scales efficiently with the number of layers and does not depend on the
number of qubits. Moreover, we explore the impacts of barren plateaus, readout
noise, and error mitigation techniques on the proposed approach. Through our
analysis, we demonstrate the effectiveness of the variational algorithm in
maximizing the efficiency of quantum state preparation, leveraging low-depth
quantum circuits.
Related papers
- Compact Multi-Threshold Quantum Information Driven Ansatz For Strongly Interactive Lattice Spin Models [0.0]
We introduce a systematic procedure for ansatz building based on approximate Quantum Mutual Information (QMI)
Our approach generates a layered-structured ansatz, where each layer's qubit pairs are selected based on their QMI values, resulting in more efficient state preparation and optimization routines.
Our results show that the Multi-QIDA method reduces the computational complexity while maintaining high precision, making it a promising tool for quantum simulations in lattice spin models.
arXiv Detail & Related papers (2024-08-05T17:07:08Z) - Unveiling quantum phase transitions from traps in variational quantum algorithms [0.0]
We introduce a hybrid algorithm that combines quantum optimization with classical machine learning.
We use LASSO for identifying conventional phase transitions and the Transformer model for topological transitions.
Our protocol significantly enhances efficiency and precision, opening new avenues in the integration of quantum computing and machine learning.
arXiv Detail & Related papers (2024-05-14T09:01:41Z) - Near-Term Distributed Quantum Computation using Mean-Field Corrections
and Auxiliary Qubits [77.04894470683776]
We propose near-term distributed quantum computing that involve limited information transfer and conservative entanglement production.
We build upon these concepts to produce an approximate circuit-cutting technique for the fragmented pre-training of variational quantum algorithms.
arXiv Detail & Related papers (2023-09-11T18:00:00Z) - Decomposition of Matrix Product States into Shallow Quantum Circuits [62.5210028594015]
tensor network (TN) algorithms can be mapped to parametrized quantum circuits (PQCs)
We propose a new protocol for approximating TN states using realistic quantum circuits.
Our results reveal one particular protocol, involving sequential growth and optimization of the quantum circuit, to outperform all other methods.
arXiv Detail & Related papers (2022-09-01T17:08:41Z) - Universal compilation for quantum state preparation and tomography [0.0]
We propose a universal compilation-based variational algorithm for the preparation and tomography of quantum states in low-depth quantum circuits.
We evaluate the performance of various unitary topologies and the trainability of different unitarys for getting high efficiency.
arXiv Detail & Related papers (2022-04-25T13:10:33Z) - Gradient Ascent Pulse Engineering with Feedback [0.0]
We introduce feedback-GRAPE, which borrows some concepts from model-free reinforcement learning to incorporate the response to strong measurements.
Our method yields interpretable feedback strategies for state preparation and stabilization in the presence of noise.
arXiv Detail & Related papers (2022-03-08T18:46:09Z) - Surviving The Barren Plateau in Variational Quantum Circuits with
Bayesian Learning Initialization [0.0]
Variational quantum-classical hybrid algorithms are seen as a promising strategy for solving practical problems on quantum computers in the near term.
Here, we introduce the fast-and-slow algorithm, which uses gradients to identify a promising region in Bayesian space.
Our results move variational quantum algorithms closer to their envisioned applications in quantum chemistry, optimization, and quantum simulation problems.
arXiv Detail & Related papers (2022-03-04T17:48:57Z) - Circuit Symmetry Verification Mitigates Quantum-Domain Impairments [69.33243249411113]
We propose circuit-oriented symmetry verification that are capable of verifying the commutativity of quantum circuits without the knowledge of the quantum state.
In particular, we propose the Fourier-temporal stabilizer (STS) technique, which generalizes the conventional quantum-domain formalism to circuit-oriented stabilizers.
arXiv Detail & Related papers (2021-12-27T21:15:35Z) - 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) - Variational Quantum Optimization with Multi-Basis Encodings [62.72309460291971]
We introduce a new variational quantum algorithm that benefits from two innovations: multi-basis graph complexity and nonlinear activation functions.
Our results in increased optimization performance, two increase in effective landscapes and a reduction in measurement progress.
arXiv Detail & Related papers (2021-06-24T20:16:02Z) - Adaptive pruning-based optimization of parameterized quantum circuits [62.997667081978825]
Variisy hybrid quantum-classical algorithms are powerful tools to maximize the use of Noisy Intermediate Scale Quantum devices.
We propose a strategy for such ansatze used in variational quantum algorithms, which we call "Efficient Circuit Training" (PECT)
Instead of optimizing all of the ansatz parameters at once, PECT launches a sequence of variational algorithms.
arXiv Detail & Related papers (2020-10-01T18:14:11Z)
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.