36,425 research outputs found
Quantum computers for optimization the performance
Computers decrease human work and concentrate on enhancing the performance to advance the technology. Various methods have been developed to enhance the performance of computers. Performance of computer is based on computer architecture, while computer architecture differs in various devices, such as microcomputers, minicomputers, mainframes, laptops, tablets, and mobile phones. While each device has its own architecture, the majority of these systems are built on Boolean algebra. In this study, a few basic concepts used in quantum computing are discussed. It is known that quantum computers do not possess any transistor and chip while being roughly 100 times faster than a common classic silicon computer. Scientists believe that quantum computers are the next generation of the classic computers
Multistart Methods for Quantum Approximate Optimization
Hybrid quantum-classical algorithms such as the quantum approximate
optimization algorithm (QAOA) are considered one of the most promising
approaches for leveraging near-term quantum computers for practical
applications. Such algorithms are often implemented in a variational form,
combining classical optimization methods with a quantum machine to find
parameters to maximize performance. The quality of the QAOA solution depends
heavily on quality of the parameters produced by the classical optimizer.
Moreover, the presence of multiple local optima in the space of parameters
makes it harder for the classical optimizer. In this paper we study the use of
a multistart optimization approach within a QAOA framework to improve the
performance of quantum machines on important graph clustering problems. We also
demonstrate that reusing the optimal parameters from similar problems can
improve the performance of classical optimization methods, expanding on similar
results for MAXCUT
Integrating quantum and classical computing for multi-energy system optimization using Benders decomposition
During recent years, quantum computers have received increasing attention,
primarily due to their ability to significantly increase computational
performance for specific problems. Computational performance could be improved
for mathematical optimization by quantum annealers. This special type of
quantum computer can solve quadratic unconstrained binary optimization
problems. However, multi-energy systems optimization commonly involves integer
and continuous decision variables. Due to their mixed-integer problem
structure, quantum annealers cannot be directly used for multi-energy system
optimization. To solve multi-energy system optimization problems, we present a
hybrid Benders decomposition approach combining optimization on quantum and
classical computers. In our approach, the quantum computer solves the master
problem, which involves only the integer variables from the original energy
system optimization problem. The subproblem includes the continuous variables
and is solved by a classical computer. For better performance, we apply
improvement techniques to the Benders decomposition. We test the approach on a
case study to design a cost-optimal multi-energy system. While we provide a
proof of concept that our Benders decomposition approach is applicable for the
design of multi-energy systems, the computational time is still higher than for
approaches using classical computers only. We therefore estimate the potential
improvement of our approach to be expected for larger and fault-tolerant
quantum computers
QPLEX: Realizing the Integration of Quantum Computing into Combinatorial Optimization Software
Quantum computing has the potential to surpass the capabilities of current
classical computers when solving complex problems. Combinatorial optimization
has emerged as one of the key target areas for quantum computers as problems
found in this field play a critical role in many different industrial
application sectors (e.g., enhancing manufacturing operations or improving
decision processes). Currently, there are different types of high-performance
optimization software (e.g., ILOG CPLEX and Gurobi) that support engineers and
scientists in solving optimization problems using classical computers. In order
to utilize quantum resources, users require domain-specific knowledge of
quantum algorithms, SDKs and libraries, which can be a limiting factor for any
practitioner who wants to integrate this technology into their workflows. Our
goal is to add software infrastructure to a classical optimization package so
that application developers can interface with quantum platforms readily when
setting up their workflows. This paper presents a tool for the seamless
utilization of quantum resources through a classical interface. Our approach
consists of a Python library extension that provides a backend to facilitate
access to multiple quantum providers. Our pipeline enables optimization
software developers to experiment with quantum resources selectively and assess
performance improvements of hybrid quantum-classical optimization solutions.Comment: Accepted for the IEEE International Conference on Quantum Computing
and Engineering (QCE) 202
The QAOA with Slow Measurements
The Quantum Approximate Optimization Algorithm (QAOA) was originally
developed to solve combinatorial optimization problems, but has become a
standard for assessing the performance of quantum computers. Fully descriptive
benchmarking techniques are often prohibitively expensive for large numbers of
qubits (), so the QAOA often serves in practice as a
computational benchmark. The QAOA involves a classical optimization subroutine
that attempts to find optimal parameters for a quantum subroutine.
Unfortunately, many optimizers used for the QAOA require many shots () per point in parameter space to get a reliable estimate of the energy
being minimized. However, some experimental quantum computing platforms such as
neutral atom quantum computers have slow repetition rates, placing unique
requirements on the classical optimization subroutine used in the QAOA in these
systems. In this paper we investigate the performance of a gradient free
classical optimizer for the QAOA - dual annealing - and demonstrate that
optimization is possible even with and .Comment: Fixing typo in restriction of range of variables being optimized
over, updating arxiv author field to include middle initia
Noisy intermediate-scale quantum computing algorithm for solving an -vertex MaxCut problem with log() qubits
Quantum computers are devices, which allow more efficient solutions of
problems as compared to their classical counterparts. As the timeline to
developing a quantum-error corrected computer is unclear, the quantum computing
community has dedicated much attention to developing algorithms for currently
available noisy intermediate-scale quantum computers (NISQ). Thus far, within
NISQ, optimization problems are one of the most commonly studied and are quite
often tackled with the quantum approximate optimization algorithm (QAOA). This
algorithm is best known for computing graph partitions with a maximal
separation of edges (MaxCut), but can easily calculate other problems related
to graphs. Here, I present a novel quantum optimization algorithm, which uses
exponentially less qubits as compared to the QAOA while requiring a
significantly reduced number of quantum operations to solve the MaxCut problem.
Such an improved performance allowed me to partition graphs with 32 nodes on
publicly available 5 qubit gate-based quantum computers without any
preprocessing such as division of the graph into smaller subgraphs. These
results represent a 40% increase in graph size as compared to state-of-art
experiments on gate-based quantum computers such as Google Sycamore. The
obtained lower bound is 54.9% on the solution for actual hardware benchmarks
and 77.6% on ideal simulators of quantum computers. Furthermore, large-scale
optimization problems represented by graphs of a 128 nodes are tackled with
simulators of quantum computers, again without any predivision into smaller
subproblems and a lower solution bound of 67.9% is achieved. The study
presented here paves way to using powerful genetic optimizer in synergy with
quantum computersComment: 5 pages, 4 figures, 2 tables + Supplementary materia
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