Results 31 to 40 of about 2,386 (159)
Quantum machine learning: a classical perspective [PDF]
, 2018 Recently, increased computational power and data availability, as well as algorithmic advances, have led machine learning techniques to impressive results in regression, classification, data-generation and reinforcement learning tasks.
Ben-David S +15 more
core +2 more sources
Constrained quantum optimization for extractive summarization on a trapped-ion quantum computer
Scientific Reports, 2022 Realizing the potential of near-term quantum computers to solve industry-relevant constrained-optimization problems is a promising path to quantum advantage.
Pradeep Niroula +6 more
doaj +1 more source
Multistart Methods for Quantum Approximate Optimization
, 2019 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.
Larson, Jeffrey +2 more
core +1 more source
Natural evolution strategies and variational Monte Carlo
, 2020 A notion of quantum natural evolution strategies is introduced, which provides a geometric synthesis of a number of known quantum/classical algorithms for performing classical black-box optimization. Recent work of Gomes et al.
Carleo, Giuseppe +3 more
core +1 more source
qTorch: The Quantum Tensor Contraction Handler
, 2018 Classical simulation of quantum computation is necessary for studying the numerical behavior of quantum algorithms, as there does not yet exist a large viable quantum computer on which to perform numerical tests.
Aspuru-Guzik, Alán +5 more
core +2 more sources
Solution of SAT problems with the adaptive-bias quantum approximate optimization algorithm
Physical Review Research, 2023 The quantum approximate optimization algorithm (QAOA) is a promising method for solving certain classical combinatorial optimization problems on near-term quantum devices. When employing the QAOA to 3-SAT and Max-3-SAT problems, the quantum cost exhibits
Yunlong Yu +4 more
doaj +1 more source
Network Community Detection On Small Quantum Computers
, 2019 In recent years a number of quantum computing devices with small numbers of qubits became available. We present a hybrid quantum local search (QLS) approach that combines a classical machine and a small quantum device to solve problems of practical size.
Alexeev, Yuri +4 more
core +1 more source
Advanced Intelligent Discovery, EarlyView.This work investigates the optimal initial data size for surrogate‐based active learning in functional material optimization. Using factorization machine (FM)‐based quadratic unconstrained binary optimization (QUBO) surrogates and averaged piecewise linear regression, we show that adequate initial data accelerates convergence, enhances efficiency, and ...
Seongmin Kim, In‐Saeng Suh
wiley +1 more source
A Resource Efficient Ising Model‐Based Quantum Sudoku Solver
Software: Practice and Experience, Volume 56, Issue 6, Page 643-657, June 2026.ABSTRACT Background Quantum algorithms exploit superposition and parallelism to address complex combinatorial problems, many of which fall into the non‐polynomial (NP) class. Sudoku, a widely known logic‐based puzzle, is proven to be NP‐complete and thus presents a suitable testbed for exploring quantum optimization approaches.
Wen‐Li Wang +5 more
wiley +1 more source
For Fixed Control Parameters the Quantum Approximate Optimization Algorithm's Objective Function Value Concentrates for Typical Instances [PDF]
, 2018 The Quantum Approximate Optimization Algorithm, QAOA, uses a shallow depth quantum circuit to produce a parameter dependent state. For a given combinatorial optimization problem instance, the quantum expectation of the associated cost function is the ...
Brandao, Fernando G. S. L. +4 more
core +1 more source