Results 71 to 80 of about 2,283 (206)

Algorithmic QUBO formulations for k-SAT and hamiltonian cycles

, 2022
Quadratic Unconstrained Binary Optimization (QUBO) can be seen as a generic language for optimization problems. QUBOs attract particular attention since they can be solved with quantum hardware, like quantum annealers or quantum gate computers running ...
Nüßlein, Jonas (author), Linnhoff-Popien, Claudia (author), Feld, S. (author), Gabor, Thomas (author)   +3 more
core   +1 more source

Solving Max-3SAT Using QUBO Approximation [PDF]

As contemporary quantum computers do not possess error correction, any calculation performed by these devices can be considered an involuntary approximation.
Nüßlein, Jonas, Linnhoff-Popien, Claudia, Gabor, Thomas, Kölle, Michael, Feld, Sebastian, Zielinski, Sebastian   +5 more
core   +1 more source

Reinforcement Learning-Based Formulations With Hamiltonian-Inspired Loss Functions for Combinatorial Optimization Over Graphs

IEEE Access
Quadratic Unconstrained Binary Optimization (QUBO) is a versatile approach used to represent a wide range of NP-hard Combinatorial Optimization (CO) problems through binary variables. The transformation of QUBO to an Ising Hamiltonian is recognized as an
Redwan Ahmed Rizvee, Raheeb Hassan, Md. Mosaddek Khan   +2 more
doaj   +1 more source

Sampling electronic structure quadratic unconstrained binary optimization problems (QUBOs) with Ocean and Mukai solvers.

PLoS ONE, 2022
The most advanced D-Wave Advantage quantum annealer has 5000+ qubits, however, every qubit is connected to a small number of neighbors. As such, implementation of a fully-connected graph results in an order of magnitude reduction in qubit count.
Alexander Teplukhin, Brian K Kendrick, Susan M Mniszewski, Sergei Tretiak, Pavel A Dub   +4 more
doaj   +1 more source

Fundamental Challenges, Physical Implementations, and Integration Strategies for Ising Machines in Large‐Scale Optimization Tasks

Advanced Electronic Materials, Volume 12, Issue 17, 7 September 2026.
Ising machines are emerging as specialized hardware solvers for computationally hard optimization problems. This review examines five major platforms—digital CMOS, analog CMOS, emerging devices, coherent optics, and quantum systems—highlighting physics‐rooted advantages and shared bottlenecks in scalability and connectivity.
Hyunjun Lee, Joon Pyo Kim, Sanghyeon Kim
wiley   +1 more source

TSP QUBO Repository.

, 2020
Python code for setting up a TSP QUBO matrix, and submitting the problem to the D-Wave quantum annealer. A brute-force analysis is included that calculates the distance of every possible route.
Wils, Kevin
core   +1 more source

Quantum Annealing for Resource Allocation in Next-Generation Wireless Communication Networks: A QUBO Modeling Guide

IEEE Open Journal of the Communications Society
Resource allocation in next-generation (NG) wireless communication networks, including uncrewed aerial vehicle (UAV)-assisted networks, and space-air-ground integrated networks (SAGINs), often involves discrete-variable combinatorial formulations ...
Nguyen Xuan Tung, Le Tung Giang, Mai Dinh Cong, Pham Dang Anh Duc, Nguyen Hoang Viet, Trinh van Chien, Lajos Hanzo   +6 more
doaj   +1 more source

On good encodings for quantum annealer and digital optimization solvers

Scientific Reports, 2023
Several optimization solvers inspired by quantum annealing have been recently developed, either running on actual quantum hardware or simulating it on traditional digital computers.
Alberto Ceselli, Marco Premoli
doaj   +1 more source

Highly‐Uniform Passive Crossbar Arrays of Resistive Switching Random Access Memory (RRAM) for In‐Memory Computing Applications

Advanced Electronic Materials, Volume 12, Issue 18, 21 September 2026.
Passive resistive memory arrays promise efficient in‐memory computing but suffer from sneak paths and programming variability. Here, highly uniform 32 × 32 passive RRAM crossbars are programmed with multilevel precision below 3% error and 99.5% yield.
S. Ricci, P. Mannocci, M. Porzani, D. Bridarolli, F. Carletti, M. Farronato, D. Ielmini   +6 more
wiley   +1 more source

Drone Delivery Packing Problem on a Neutral‐Atom Quantum Computer

Advanced Quantum Technologies, Volume 9, Issue 9, September 2026.
This research models the Drone Delivery Packing Problem as a battery‐constrained graph partitioning. We use Pasqal's Fresnel QPU to sample time‐feasible single‐drone schedules, with tunable delivery targets via hardware parameters, and enforce battery limits by greedy post‐processing.
Sara Tarquini, Matteo Vandelli, Francesco Ferrari, Daniele Dragoni, Francesco Tudisco   +4 more
wiley   +1 more source