15 hasil untuk "Dylan Herman"
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Artikel Jurnal
Solving linear systems on quantum hardware with hybrid HHL++
Abstract The limited capabilities of current quantum hardware significantly constrain the scale of experimental demonstrations of most quantum algorithmic primitives. This makes it challenging to perform benchmarking of the current hardware using useful quantum algorithms, i.e…
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Artikel Jurnal
Composite adverse outcomes in obstetric studies: a systematic review
Abstract Background Composite outcomes are increasingly being used in obstetric trials. The aim of this systematic review is to critically appraise the use of composite outcomes in obstetric RCTs with an intention of identifying limitations and providing potential solutions for future research…
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Artikel Jurnal
Variation in outcome reporting in studies on the prevention and management of pregnancy-associated venous thromboembolism: a scoping review
Background: Pregnancy-associated venous thromboembolism (pVTE) is a notable cause of maternal morbidity and mortality. Yet, robust comparisons across studies are hampered by inconsistencies in outcome reporting. Objectives: This study determined the state of outcome reporting in studies addressing…
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Artikel Jurnal
Expressivity of Variational Quantum Machine Learning on the Boolean Cube
Categorical data play an important part in machine learning research and appears in a variety of applications. Models that can express large classes of real-valued functions on the Boolean cube are useful for problems involving discrete-valued data types, including those which are not Boolean. To…
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Artikel Jurnal
Constrained quantum optimization for extractive summarization on a trapped-ion quantum computer
Abstract Realizing the potential of near-term quantum computers to solve industry-relevant constrained-optimization problems is a promising path to quantum advantage. In this work, we consider the extractive summarization constrained-optimization problem and demonstrate the largest-to-date…
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Artikel Jurnal
Alignment between initial state and mixer improves QAOA performance for constrained optimization
Abstract Quantum alternating operator ansatz (QAOA) has a strong connection to the adiabatic algorithm, which it can approximate with sufficient depth. However, it is unclear to what extent the lessons from the adiabatic regime apply to QAOA as executed in practice with small to moderate depth. In…
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Artikel Jurnal
Parameter Setting in Quantum Approximate Optimization of Weighted Problems
Quantum Approximate Optimization Algorithm (QAOA) is a leading candidate algorithm for solving combinatorial optimization problems on quantum computers. However, in many cases QAOA requires computationally intensive parameter optimization. The challenge of parameter optimization is particularly…
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Artikel Jurnal
Characterizing barren plateaus in quantum ansätze with the adjoint representation
Abstract Variational quantum algorithms, a popular heuristic for near-term quantum computers, utilize parameterized quantum circuits which naturally express Lie groups. It has been postulated that many properties of variational quantum algorithms can be understood by studying their corresponding…
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Artikel Jurnal
Characterizing privacy in quantum machine learning
Abstract Ensuring data privacy in machine learning models is critical, especially in distributed settings where model gradients are shared among multiple parties for collaborative learning. Motivated by the increasing success of recovering input data from the gradients of classical models, this…
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Artikel Jurnal
Constrained optimization via quantum Zeno dynamics
Abstract Constrained optimization problems are ubiquitous in science and industry. Quantum algorithms have shown promise in solving optimization problems, yet none of the current algorithms can effectively handle arbitrary constraints. We introduce a technique that uses quantum Zeno dynamics to…
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Artikel Jurnal
Synergizing quantum techniques with machine learning for advancing drug discovery challenge
Abstract The Quantum Computing for Drug Discovery Challenge, held at the 42nd International Conference on Computer-Aided Design (ICCAD) in 2023, was a multi-month, research-intensive competition. Over 70 teams from more than 65 organizations from 12 different countries registered, focusing on the…
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Artikel Jurnal
Hardness of the maximum-independent-set problem on unit-disk graphs and prospects for quantum speedups
Rydberg atom arrays are among the leading contenders for the demonstration of quantum speedups. Motivated by recent experiments with up to 289 qubits [Ebadi et al., Science 376, 1209 (2022)0036-807510.1126/science.abo6587], we study the maximum-independent-set problem on unit-disk graphs with a…
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Artikel Jurnal
Decomposition pipeline for large-scale portfolio optimization with applications to near-term quantum computing
Industrially relevant constrained optimization problems, such as portfolio optimization and portfolio rebalancing, are often intractable or difficult to solve exactly. In this work, we propose and benchmark a decomposition pipeline targeting portfolio optimization and rebalancing problems with…
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Artikel Jurnal
Quantum Deep Hedging
Quantum machine learning has the potential for a transformative impact across industry sectors and in particular in finance. In our work we look at the problem of hedging where deep reinforcement learning offers a powerful framework for real markets. We develop quantum reinforcement learning…