610 research outputs found
QuASeR -- Quantum Accelerated De Novo DNA Sequence Reconstruction
In this article, we present QuASeR, a reference-free DNA sequence
reconstruction implementation via de novo assembly on both gate-based and
quantum annealing platforms. Each one of the four steps of the implementation
(TSP, QUBO, Hamiltonians and QAOA) is explained with simple proof-of-concept
examples to target both the genomics research community and quantum application
developers in a self-contained manner. The details of the implementation are
discussed for the various layers of the quantum full-stack accelerator design.
We also highlight the limitations of current classical simulation and available
quantum hardware systems. The implementation is open-source and can be found on
https://github.com/prince-ph0en1x/QuASeR.Comment: 24 page
Multilevel Combinatorial Optimization Across Quantum Architectures
Emerging quantum processors provide an opportunity to explore new approaches for solving traditional problems in the Post Moore\u27s law supercomputing era. However, the limited number of qubits makes it infeasible to tackle massive real-world datasets directly in the near future, leading to new challenges in utilizing these quantum processors for practical purposes. Hybrid quantum-classical algorithms that leverage both quantum and classical types of devices are considered as one of the main strategies to apply quantum computing to large-scale problems. In this paper, we advocate the use of multilevel frameworks for combinatorial optimization as a promising general paradigm for designing hybrid quantum-classical algorithms. In order to demonstrate this approach, we apply this method to two well-known combinatorial optimization problems, namely, the Graph Partitioning Problem, and the Community Detection Problem. We develop hybrid multilevel solvers with quantum local search on D-Wave\u27s quantum annealer and IBM\u27s gate-model based quantum processor. We carry out experiments on graphs that are orders of magnitudes larger than the current quantum hardware size and observe results comparable to state-of-the-art solvers
Multilevel Combinatorial Optimization Across Quantum Architectures
Emerging quantum processors provide an opportunity to explore new approaches
for solving traditional problems in the post Moore's law supercomputing era.
However, the limited number of qubits makes it infeasible to tackle massive
real-world datasets directly in the near future, leading to new challenges in
utilizing these quantum processors for practical purposes. Hybrid
quantum-classical algorithms that leverage both quantum and classical types of
devices are considered as one of the main strategies to apply quantum computing
to large-scale problems. In this paper, we advocate the use of multilevel
frameworks for combinatorial optimization as a promising general paradigm for
designing hybrid quantum-classical algorithms. In order to demonstrate this
approach, we apply this method to two well-known combinatorial optimization
problems, namely, the Graph Partitioning Problem, and the Community Detection
Problem. We develop hybrid multilevel solvers with quantum local search on
D-Wave's quantum annealer and IBM's gate-model based quantum processor. We
carry out experiments on graphs that are orders of magnitudes larger than the
current quantum hardware size, and we observe results comparable to
state-of-the-art solvers in terms of quality of the solution
Analysis of The Vehicle Routing Problem Solved via Hybrid Quantum Algorithms in Presence of Noisy Channels
The vehicle routing problem (VRP) is an NP-hard optimization problem that has
been an interest of research for decades in science and industry. The objective
is to plan routes of vehicles to deliver goods to a fixed number of customers
with optimal efficiency. Classical tools and methods provide good
approximations to reach the optimal global solution. Quantum computing and
quantum machine learning provide a new approach to solving combinatorial
optimization of problems faster due to inherent speedups of quantum effects.
Many solutions of VRP are offered across different quantum computing platforms
using hybrid algorithms such as quantum approximate optimization algorithm and
quadratic unconstrained binary optimization. In this work, we build a basic VRP
solver for 3 and 4 cities using the variational quantum eigensolver on a fixed
ansatz. The work is further extended to evaluate the robustness of the solution
in several examples of noisy quantum channels. We find that the performance of
the quantum algorithm depends heavily on what noise model is used. In general,
noise is detrimental, but not equally so among different noise sources.Comment: 15 Pages, 5 figures, 15 tables. arXiv admin note: substantial text
overlap with arXiv:2112.1540
QED driven QAOA for network-flow optimization
We present a general framework for modifying quantum approximate optimization
algorithms (QAOA) to solve constrained network flow problems. By exploiting an
analogy between flow constraints and Gauss's law for electromagnetism, we
design lattice quantum electrodynamics (QED) inspired mixing Hamiltonians that
preserve flow constraints throughout the QAOA process. This results in an
exponential reduction in the size of the configuration space that needs to be
explored, which we show through numerical simulations, yields higher quality
approximate solutions compared to the original QAOA routine. We outline a
specific implementation for edge-disjoint path (EDP) problems related to
traffic congestion minimization, numerically analyze the effect of initial
state choice, and explore trade-offs between circuit complexity and qubit
resources via a particle-vortex duality mapping. Comparing the effect of
initial states reveals that starting with an ergodic (unbiased) superposition
of solutions yields better performance than beginning with the mixer
ground-state, suggesting a departure from the "short-cut to adiabaticity"
mechanism often used to motivate QAOA.Comment: 14 pages, 10 figure
Multimodal Container Planning: a QUBO Formulation and Implementation on a Quantum Annealer
Quantum computing is developing fast. Real world applications are within
reach in the coming years. One of the most promising areas is combinatorial
optimisation, where the Quadratic Unconstrained Binary Optimisation (QUBO)
problem formulation is used to get good approximate solutions. Both the
universal quantum computer as the quantum annealer can handle this kind of
problems well. In this paper, we present an application on multimodal container
planning. We show how to map this problem to a QUBO problem formulation and how
the practical implementation can be done on the quantum annealer produced by
D-Wave Systems.Comment: 15 page
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