Equivariant quantum circuits for learning on weighted graphs
Variational quantum algorithms are the leading candidate for advantage on near-term
quantum hardware. When training a parametrized quantum circuit in this setting to solve a …
quantum hardware. When training a parametrized quantum circuit in this setting to solve a …
[HTML][HTML] Short-depth QAOA circuits and quantum annealing on higher-order ising models
We present a direct comparison between QAOA (Quantum Alternating Operator Ansatz), and
QA (Quantum Annealing) on 127 qubit problem instances. QAOA with p= 1, 2 rounds is …
QA (Quantum Annealing) on 127 qubit problem instances. QAOA with p= 1, 2 rounds is …
Quantum optimization using a 127-qubit gate-model IBM quantum computer can outperform quantum annealers for nontrivial binary optimization problems
We introduce a comprehensive quantum solver for binary combinatorial optimization
problems on gate-model quantum computers that outperforms any published alternative and …
problems on gate-model quantum computers that outperforms any published alternative and …
Unconstrained binary models of the travelling salesman problem variants for quantum optimization
Quantum computing is offering a novel perspective for solving combinatorial optimization
problems. To fully explore the possibilities offered by quantum computers, the problems …
problems. To fully explore the possibilities offered by quantum computers, the problems …
Encoding-independent optimization problem formulation for quantum computing
F Dominguez, J Unger, M Traube, B Mant… - Frontiers in Quantum …, 2023 - frontiersin.org
We review encoding and hardware-independent formulations of optimization problems for
quantum computing. Using this generalized approach, an extensive library of optimization …
quantum computing. Using this generalized approach, an extensive library of optimization …
Quantum optimization for the graph coloring problem with space-efficient embedding
Current quantum computing devices have different strengths and weaknesses depending
on their architectures. This means that flexible approaches to circuit design are necessary …
on their architectures. This means that flexible approaches to circuit design are necessary …
Computing high-degree polynomial gradients in memory
Specialized function gradient computing hardware could greatly improve the performance of
state-of-the-art optimization algorithms. Prior work on such hardware, performed in the …
state-of-the-art optimization algorithms. Prior work on such hardware, performed in the …
Error mitigation for variational quantum algorithms through mid-circuit measurements
Noisy intermediate-scale quantum algorithms require novel paradigms of error mitigation. To
obtain noise-robust quantum computers, each logical qubit is equipped with hundreds or …
obtain noise-robust quantum computers, each logical qubit is equipped with hundreds or …
Comparative study of variations in quantum approximate optimization algorithms for the traveling salesman problem
The traveling salesman problem (TSP) is one of the most often-used NP-hard problems in
computer science to study the effectiveness of computing models and hardware platforms. In …
computer science to study the effectiveness of computing models and hardware platforms. In …
The coming decades of quantum simulation
Contemporary quantum technologies face major difficulties in fault tolerant quantum
computing with error correction, and focus instead on various shades of quantum simulation …
computing with error correction, and focus instead on various shades of quantum simulation …