Results 51 to 60 of about 4,386 (222)
Quantum approximate optimization for hard problems in linear algebra
SciPost Physics Core, 2021 The quantum approximate optimization algorithm (QAOA) by Farhi et al. is a quantum computational framework for solving quantum or classical optimization tasks.
Ajinkya Borle, Vincent E. Elfving, Samuel J. Lomonaco
doaj +1 more source
Optimized Compilation of Aggregated Instructions for Realistic Quantum Computers
, 2019 Recent developments in engineering and algorithms have made real-world applications in quantum computing possible in the near future. Existing quantum programming languages and compilers use a quantum assembly language composed of 1- and 2-qubit (quantum
Chong, Fred T. +6 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
Out of the Loop: Structural Approximation of Optimisation Landscapes and non-Iterative Quantum Optimisation [PDF]
QuantumThe Quantum Approximate Optimisation Algorithm (QAOA) is a widely studied quantum-classical iterative heuristic for combinatorial optimisation. While QAOA targets problems in complexity class NP, the classical optimisation procedure required in every ...
Tom Krüger, Wolfgang Mauerer
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Scaling quantum approximate optimization on near-term hardware
Scientific Reports, 2022 The quantum approximate optimization algorithm (QAOA) is an approach for near-term quantum computers to potentially demonstrate computational advantage in solving combinatorial optimization problems.
Phillip C. Lotshaw +7 more
doaj +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
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
From the Quantum Approximate Optimization Algorithm to a Quantum Alternating Operator Ansatz
, 2019 The next few years will be exciting as prototype universal quantum processors emerge, enabling implementation of a wider variety of algorithms. Of particular interest are quantum heuristics, which require experimentation on quantum hardware for their ...
Biswas, Rupak +5 more
core +1 more source
Restricted global optimization for QAOA
APL QuantumThe Quantum Approximate Optimization Algorithm (QAOA) has emerged as a promising variational quantum algorithm for addressing NP-hard combinatorial optimization problems. However, a significant limitation lies in optimizing its classical parameters, which is in itself an NP-hard problem.
Peter Gleißner +2 more
openaire +3 more sources
End‐to‐End Portfolio Optimization with Hybrid Quantum Annealing
Advanced Quantum Technologies, EarlyView.This works presents a hybrid quantum‐classical framework for portfolio optimization that combines quantum assisted asset selection and rebalancing with classical weight allocation. The approach processes real market data, embeds it into Quadratic Unconstrained Binary Optimization formulations, and evaluates performance within a unified workflow ...
Sai Nandan Morapakula +5 more
wiley +1 more source