1,720,972 research outputs found
Quantum-related approaches for solving optimization problems: From applications to backend
Full text linkL'abstract è presente nell'allegato / the abstract is in the attachmen
Multi-VQC: A Novel QML Approach for Enhancing Healthcare Classification
Full text linkAccurate and reliable diagnosis of diseases is crucial in enabling timely medical treatment and enhancing patient survival rates. In recent years, Machine Learning has revolutionized diagnostic practices by creating classification models capable of identifying diseases. However, these classification problems often suffer from significant class imbalances, which can inhibit the effectiveness of traditional models. Therefore, the interest in Quantum models has arisen, driven by the captivating promise of overcoming the limitations of the classical counterpart thanks to their ability to express complex patterns by mapping data in a higher-dimensional computational space. This work proposes a novel approach for enhancing the classification performance of Quantum Neural Networks (QNN) consisting of multiple Variational Quantum Circuits (VQCs) arranged sequentially. This strategy increases the nonlinearity of the model by exploiting the measurement operation and improving its ability to capture complex patterns. In this analysis, the proposed method is compared against classical models while varying its degrees of freedom, specifically the number of involved VQCs, on three well-known healthcare datasets - Prostate Cancer, Heart Failure, and Diabetes. The results prove the potential of the quantum model and demonstrate the validity of the proposed approach, showing that its advantage increases with the complexity of the classification
Quantum Machine Learning in Healthcare: Evaluating QNN and QSVM Models
Full text linkEffective and accurate diagnosis of diseases such as
cancer, diabetes, and heart failure is crucial for timely medical
intervention and improving patient survival rates. Machine
learning has revolutionized diagnostic methods in recent years
by developing classification models that detect diseases based on
selected features. However, these classification tasks are often
highly imbalanced, limiting the performance of classical models.
Quantum models offer a promising alternative, exploiting their
ability to express complex patterns by operating in a higher-
dimensional computational space through superposition and
entanglement. These unique properties make quantum models
potentially more effective in addressing the challenges of imbal-
anced datasets. This work evaluates the potential of quantum
classifiers in healthcare, focusing on Quantum Neural Networks
(QNNs) and Quantum Support Vector Machines (QSVMs), com-
paring them with popular classical models. The study is based on
three well-known healthcare datasets — Prostate Cancer, Heart
Failure, and Diabetes.
The results indicate that QSVMs outperform QNNs across all
datasets due to their susceptibility to overfitting. Furthermore,
quantum models prove the ability to overcome classical models
in scenarios with high dataset imbalance. Although preliminary,
these findings highlight the potential of quantum models in
healthcare classification tasks and lead the way for further
research in this domai
Qoolchain: A QUBO Preprocessing Toolchain for Enhancing Quantum Optimization
Full text linkSolving combinatorial optimization problems is crucial in research and industry but still challenging since these problems are usually NP-hard or NP-complete. Classical solvers struggle with their non-polynomial complexity. Although heuristic algorithms are widely used, they often fall short in execution time and accuracy, increasing the interest in quantum computing alternatives using Quadratic Unconstrained Binary Optimization (QUBO) formulations. However, current Noisy Intermediate-Scale Quantum (NISQ) computers and future early fault-tolerant quantum devices face limitations in qubit availability and circuit depth, necessitating preprocessing to reduce problem complexity. This study introduces Qoolchain, a QUBO preprocessing toolchain designed to reduce problem size and enhance solver performance. Developed in Cython, Qoolchain is compatible with major quantum frameworks and optimized for the Grover Adaptive Search (GAS) algorithm. It includes steps like persistency identification, decomposition, and probing to estimate function bounds, all with polynomial complexity. Qoolchain also proposes using the Grover Search algorithm for problem segments whose optimal value is known a priori from graph theory and Shannon decomposition to reduce QUBO problem complexity further. Evaluated against the D-Wave preprocessing toolchain on various problems, Qoolchain demonstrates higher efficiency and accuracy. It represents a significant advancement in enabling practical quantum solvers, addressing hardware limitations, and solving complex industry-relevant problems. © 2024 The Author(s). Advanced Quantum Technologies published by Wiley-VCH GmbH
Improving the exploitability of Simulated Adiabatic Bifurcation through a flexible and open-source digital architecture
Full text linkCombinatorial Optimization (CO) problems exhibit exponential complexity, constraining classical computers from providing fast and satisfactory outcomes. Quantum Computers (QCs) can effectively find optimal or near-optimal solutions by exploring the solutions space of a problem encoded in a qubits system, exploiting principles of quantum mechanics. However, non-idealities and high costs limit their availability. These can be overcome by emulating QCs on cheaper and more accessible classical computing platforms, like Field-Programmable Gate Arrays (FPGAs). This article presents a digital architecture, implementing the Ising-compatible Simulated Adiabatic Bifurcation algorithm. It mimics the quantum adiabatic evolution of a network of non-linear Kerr oscillators. The architecture, described in VHDL and targeting FPGAs, consists of processing elements for computing the Kerr oscillators’ evolution, a set of units considering their Ising-related interactions and an evolution variables update unit. The proposed approach includes a speedup-targeting approximation of the algorithm, a method for handling single-variable constraints, and a software model that allows architecture customization for specific problems. Tests were conducted using an Altera Cyclone V SoC with FPGA logic and the Nios II processor for interface purposes. The results demonstrate the functionality of the architecture and its scalability with the problem size, making it suitable for real-world applications
Scheduling of Satellite Constellation Operations in EO Missions Using Quantum Optimization
Full text linkAs Earth Observation (EO) missions advance towards Agile Earth Observation Satellites, the complexity of scheduling problems increases, posing challenges for traditional optimization methods. This paper investigates the potential of a quantum algorithm to address the scheduling problem in EO constellations. In particular, a novel formulation of the satellite constellation optimization problem is proposed, translating it into a Quadratic Unconstrained Binary Optimization (QUBO) problem, i.e., compliant with quantum solvers. Penalty functions are incorporated to optimize mission energy consumption. The formulated QUBO problem is then implemented and solved on a real quantum computer (a D-Wave Quantum Annealer). The performance provided by the quantum machine is compared with established classical meta-heuristic solvers like Simulated Annealing and Tabu Search. The results show that the proposed quantum optimization process achieves better results in terms of both solution quality and computational efficiency
Quantum Optimization for Closed-Loop Scheduling of Earth Observation Satellite Formation
Full text linkThe scheduling complexity of Agile Earth Observation Satellites (AEOSs) increases significantly as Earth Observation missions progress. This makes traditional optimization techniques less effective, restricting their application to small-scale and open-loop scheduling problems. In this paper, we investigate the potential of quantum solvers to address the closed-loop scheduling problem for a formation of AEOSs, overcoming the limitations of classical optimization techniques. To this end, we formulate the scheduling problem as a novel Quadratic Unconstrained Binary Optimization (QUBO) problem, i.e., a formulation specifically designed for quantum solvers. Moreover, penalty functions are introduced to minimize mission energy consumption and reduce deviations between the original and rescheduled solutions. The formulated QUBO problem is implemented on a D-Wave quantum annealer for a daily and large-scale scheduling scenario. The obtained results demonstrate significant improvements in computational efficiency and solution quality compared to traditional methods like Simulated Annealing and Tabu Search, highlighting the potential of quantum solvers in optimizing complex scheduling tasks for AEOS formations
Towards an Automatic Framework for Solving Optimization Problems with Quantum Computers
Full text linkOptimizing objective functions stands to benefit significantly from leveraging quantum computers, promising enhanced solution quality across various application domains in the future. However, harnessing the potential of quantum solvers necessitates formulating problems according to the Quadratic Unconstrained Binary Optimization (QUBO) model, demanding significant expertise in quantum computation and QUBO formulations. This expertise barrier limits access to quantum solutions. Fortunately, automating the conversion of conventional optimization problems into QUBO formulations presents a solution for promoting accessibility to quantum solvers. This article addresses the unmet need for a comprehensive automatic framework to assist users in utilizing quantum solvers for optimization tasks while preserving interfaces that closely resemble conventional optimization practices. The framework prompts users to specify variables, optimization criteria, as well as validity constraints and, afterwards, allows them to choose the desired solver. Subsequently, it automatically transforms the problem description into a format compatible with the chosen solver and provides the resulting solution. Additionally, the framework offers instruments for analyzing solution validity and quality. Comparative analysis against existing libraries and tools in the literature highlights the comprehensive nature of the proposed framework. Two use cases (the knapsack problem and linear regression) are considered to show the completeness and efficiency of the framework in real-world applications. Finally, the proposed framework represents a significant advancement towards automating quantum computing solutions and widening access to quantum optimization for a broader range of users. The framework is publicly available on GitHub (https://github.com/cda-tum/mqt-qao) as part of the Munich Quantum Toolkit (MQT)
Graph Neural Network-Based Predictor for Optimal Quantum Hardware Selection
Full text linkThe growing variety of quantum hardware tech-
nologies, each with unique peculiarities such as connectivity
and native gate sets, creates challenges when selecting the best
platform for executing a specific quantum circuit. This selection
process usually involves a brute-force approach: compiling the
circuit on various devices and evaluating performance based on
factors such as circuit depth and gate fidelity. However, this
method is computationally expensive and does not scale well as
the number of available quantum processors increases.
In this work, we propose a Graph Neural Network (GNN)-
based predictor that automates hardware selection by analyzing
the Directed Acyclic Graph (DAG) representation of a quantum
circuit. Our study evaluates 498 quantum circuits (up to 27
qubits) from the MQT Bench dataset, compiled using Qiskit
on four devices: three superconducting quantum processors
(IBM-Kyiv, IBM-Brisbane, IBM-Sherbrooke) and one trapped-
ion processor (IONQ-Forte). Performance is estimated using a
metric that integrates circuit depth and gate fidelity, resulting in a
dataset where 93 circuits are optimally compiled on the trapped-
ion device, while the remaining circuits prefer superconducting
platforms. By exploiting graph-based machine learning, our
approach avoids extracting the circuit features for the model eval-
uation but directly embeds it as a graph, significantly accelerating
the optimal target decision-making process and maintaining all
the information. Experimental results prove 94.4% accuracy and
an 85.5% F1 score for the minority class, effectively predicting
the best compilation target
AMARETTO: Enabling Efficient Quantum Algorithm Emulation on Low-Tier FPGAs
Full text linkResearchers and industries are increasingly drawn to quantum computing for its computational potential. However, validating new quantum algorithms is challenging due to the limitations of current quantum devices. Software simulators are time and memory-consuming, making hardware emulators an attractive alternative. This article introduces AMARETTO (quAntuM ARchitecture EmulaTion TechnOlogy), designed for quantum computing emulation on low-tier Field-Programmable gate arrays (FPGAs), supporting Clifford+T and rotational gate sets. It simplifies and accelerates the verification of quantum algorithms using a Reduced-Instruction-Set-Computer (RISC)-like structure and efficient handling of sparse quantum gates. A dedicated compiler translates OpenQASM 2.0 into RISC-like instructions. AMARETTO is validated against the Qiskit simulators. Our results show successful emulation of sixteen qubits on a AMD Kria KV260 SoM. This approach rivals other works in emulated qubit capacity on a smaller, more affordable FPGA
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