BEIJING, Dec. 19,
2024 /PRNewswire/ -- WiMi Hologram Cloud Inc.
(NASDAQ: WiMi) ("WiMi" or the "Company"), a leading global Hologram
Augmented Reality ("AR") Technology provider, today announced the
research of a new quantum algorithm—the Holographic Quantum Linear
Solver (HQLS), which aims to provide a more efficient and
resource-efficient quantum algorithm for solving the Quantum Linear
System Problem (QLSP). This algorithm is based on a combination of
Variational Quantum Algorithms (VQA) and the classical shadow
framework, overcoming the hardware resource bottlenecks of
traditional quantum linear solver algorithms.
QLSP refers to the problem of solving linear systems of
equations using quantum computing. Solutions to the QLSP often rely
on the quantumization of classical linear algebra algorithms used
in quantum computing. The most famous quantum linear system solving
algorithm is the Harrow-Hassidim-Lloyd (HHL) algorithm, which
accelerates the solution of linear systems through quantum
superposition and interference. In theory, it can reduce the time
complexity from the classical polynomial level to the logarithmic
level of quantum computing. However, the HHL algorithm requires the
use of large-scale controlled gate operations on quantum hardware,
making it difficult to implement on existing quantum computers.
VQAs are a class of algorithms that combine quantum computing
with classical optimization methods. VQAs solve problems by
implementing parameterized quantum circuits in quantum computing
and optimizing the parameters of the quantum circuit using
classical optimizers. VQAs are widely applied in fields such as
quantum machine learning, quantum chemistry, and quantum linear
equation solving.
The core advantage of VQAs lies in their relatively low resource
requirements. By using the variational method, VQAs avoid the need
to perform complex global operations in quantum circuits, instead
optimizing circuit parameters within local spaces. This reduces the
number of qubits and quantum gates required.
The classical shadow framework is a strategy used for
approximate computations, typically playing a role in scenarios
that combine quantum and classical computing. The shadow method
obtains approximations by simulating certain computational
processes and is widely used in model training in machine learning
and algorithm design in quantum computing.
The advantage of the shadow framework is its ability to make
efficient estimates with a small number of samples, significantly
reducing the need for computational resources. Therefore, combining
the shadow framework with quantum computing holds the potential to
create more efficient quantum algorithms.
WiMi's HQLS combines the ideas of VQAs and the classical shadow
framework. It aims to solve linear systems by optimizing the
parameters of the quantum circuit, while avoiding the need for
large controlled units. The core idea of the algorithm is to
optimize the parameters of the quantum circuit using VQA, and to
approximate the computation results at each iteration by combining
the classical shadow framework, thus effectively reducing the
computational complexity of the algorithm.
The basic process of WiMi's HQLS can be divided into the
following steps:
Initialization: Initialize the quantum system and
preprocess the linear system using classical algorithms to generate
the parameterized quantum circuit.
Parameterized Quantum Circuit: Design the quantum circuit
using VQA and initialize the parameters of the circuit.
Iterative Optimization: Optimize the parameters of the
quantum circuit using a classical optimizer, and after each
optimization, obtain an approximate solution through quantum
computation.
Shadow Framework Approximate Calculation: At each
parameter update, use the classical shadow framework to approximate
the output of the quantum circuit, thereby avoiding high quantum
resource consumption.
Convergence Check: Calculate the error between the
current solution and the true solution to determine whether the
algorithm has converged.
Result Output: Output the
solution vector X of the solved system of linear
equations.
WiMi's HQLS resource optimization mainly focuses on two
aspects:
Quantum Bit Count: Traditional quantum linear system
solving algorithms require a large number of quantum bits to
represent the different dimensions of the problem. With the
introduction of VQAs, HQLS only requires quantum bits that scale
logarithmically with the size of the problem, significantly
reducing the number of quantum bits needed.
Quantum Gate Complexity: The optimization of the quantum
circuit can significantly reduce the number of quantum gates,
thereby lowering the complexity of quantum circuit execution. By
combining with the classical shadow framework, HQLS avoids the need
to perform large-scale controlled operations, making the quantum
circuit more compact and efficient.
As a resource-efficient quantum algorithm, WiMi's HQLS
successfully overcomes the challenges of solving linear systems
under the current limitations of quantum hardware. By combining
VQAs and the classical shadow framework, HQLS not only operates
efficiently with fewer quantum bits and quantum gates, but also
demonstrates significant advantages in experiments involving the
solution of multiple linear systems.
In traditional quantum linear solving algorithms (such as the
HHL algorithm), the resource requirements are often quite high,
particularly in terms of the number of quantum bits and the
complexity of quantum gates, making it difficult to implement them
on current noisy intermediate-scale quantum (NISQ) computers.
However, HQLS significantly reduces the resource requirements for
quantum hardware by innovatively introducing the framework of
Variational Quantum Algorithms (VQA). Additionally, with the
assistance of the classical shadow framework, the computational
complexity is further reduced, enabling efficient solutions on
practical quantum hardware.
We have verified the effectiveness of WiMi's HQLS through
experiments on multiple linear systems (such as solving
high-dimensional matrices and discretized Laplace equation
problems). The experimental results demonstrate that HQLS excels in
both solution accuracy and computational efficiency. In particular,
when compared to other quantum linear system solving methods, it
shows lower quantum resource consumption and faster
convergence.
Currently, the experiments of HQLS still rely on noisy
intermediate-scale quantum computers for validation, and thus face
the challenges of quantum noise and errors. In the future, the
combination of quantum error correction (QEC) techniques and noise
suppression algorithms will improve the stability and robustness of
HQLS on practical quantum hardware. By introducing quantum
fault-tolerant technologies, HQLS will be able to scale to larger
quantum computers and operate stably in high-noise
environments.
In the future, with the continuous optimization of quantum
computing hardware and the increase in the number of quantum bits,
HQLS can undergo larger-scale validation on practical quantum
computers. Particularly with advancements in error correction
techniques and improvements in quantum bit quality, it is expected
that the efficiency and accuracy of HQLS will be further
enhanced.
In various application scenarios of quantum computing, HQLS can
not only be applied independently but also combined with other
quantum algorithms to form more complex hybrid quantum algorithms.
For example, HQLS can be combined with quantum optimization
algorithms, such as the Quantum Approximate Optimization Algorithm
(QAOA), to tackle more complex optimization problems. Furthermore,
HQLS can be integrated with quantum simulation algorithms to solve
large-scale linear systems involved in modeling physical
processes.
HQLS has significant application potential, especially in areas
like large-scale data processing, physical simulations, and
optimization problems. As quantum computing capabilities improve,
HQLS could be used to solve more complex linear system problems in
fields such as climate modeling, quantum chemistry, machine
learning, and financial modeling. For instance, in quantum
chemistry, HQLS could be used to solve the electronic structure of
molecular orbitals, providing more efficient simulation results; in
machine learning, it can accelerate the solution of linear
regression and least-squares problems.
WiMi's HQLS, as an interdisciplinary quantum algorithm, is
expected to integrate more deeply with other fields (such as
quantum information, quantum machine learning, quantum chemistry,
etc.) in the future. In particular, in the application of quantum
machine learning, HQLS could provide a more efficient computational
tool for training large-scale machine learning models. Moreover,
with the exploration of emerging fields like quantum consciousness
research and quantum neural networks, HQLS may also become a
foundational tool for solving complex problems in these areas.
The introduction of WiMi's Holographic Quantum Linear Solver
(HQLS) opens up new avenues for the application of quantum
computing in solving linear system problems. By combining
Variational Quantum Algorithms (VQA) with the classical shadow
framework, HQLS effectively reduces the resource requirements for
quantum hardware, enabling efficient solutions on current quantum
computers. Looking ahead, with continuous advancements in quantum
hardware and further optimization of algorithms, HQLS is expected
to see widespread application in multiple fields, driving the
maturation of quantum computing technology and offering new
solutions for solving complex problems in modern science and
engineering.
About WiMi Hologram Cloud
WiMi Hologram Cloud, Inc. (NASDAQ:WiMi) is a holographic cloud
comprehensive technical solution provider that focuses on
professional areas including holographic AR automotive HUD
software, 3D holographic pulse LiDAR, head-mounted light field
holographic equipment, holographic semiconductor, holographic cloud
software, holographic car navigation and others. Its services and
holographic AR technologies include holographic AR automotive
application, 3D holographic pulse LiDAR technology, holographic
vision semiconductor technology, holographic software development,
holographic AR advertising technology, holographic AR entertainment
technology, holographic ARSDK payment, interactive holographic
communication and other holographic AR technologies.
Safe Harbor Statements
This press release contains "forward-looking statements" within
the Private Securities Litigation Reform Act of 1995. These
forward-looking statements can be identified by terminology such as
"will," "expects," "anticipates," "future," "intends," "plans,"
"believes," "estimates," and similar statements. Statements that
are not historical facts, including statements about the Company's
beliefs and expectations, are forward-looking statements. Among
other things, the business outlook and quotations from management
in this press release and the Company's strategic and operational
plans contain forward−looking statements. The Company may also make
written or oral forward−looking statements in its periodic reports
to the US Securities and Exchange Commission ("SEC") on Forms 20−F
and 6−K, in its annual report to shareholders, in press releases,
and other written materials, and in oral statements made by its
officers, directors or employees to third parties. Forward-looking
statements involve inherent risks and uncertainties. Several
factors could cause actual results to differ materially from those
contained in any forward−looking statement, including but not
limited to the following: the Company's goals and strategies; the
Company's future business development, financial condition, and
results of operations; the expected growth of the AR holographic
industry; and the Company's expectations regarding demand for and
market acceptance of its products and services.
Further information regarding these and other risks is included
in the Company's annual report on Form 20-F and the current report
on Form 6-K and other documents filed with the SEC. All information
provided in this press release is as of the date of this press
release. The Company does not undertake any obligation to update
any forward-looking statement except as required under applicable
laws.
View original
content:https://www.prnewswire.com/news-releases/wimi-researches-quantum-linear-solvers-a-resource-efficient-quantum-algorithm-for-linear-systems-of-equations-302336179.html
SOURCE WiMi Hologram Cloud Inc.
