Quantum optimization: Potential, challenges, and the path forward
Recent advances in quantum computers are demonstrating the ability to solve problems at a
scale beyond brute force classical simulation. As such, a widespread interest in quantum …
scale beyond brute force classical simulation. As such, a widespread interest in quantum …
Efficient discrete feature encoding for variational quantum classifier
Recent days have witnessed significant interests in applying quantum-enhanced techniques
for solving a variety of machine learning tasks. Variational methods that use quantum …
for solving a variety of machine learning tasks. Variational methods that use quantum …
Quantum-relaxation based optimization algorithms: theoretical extensions
K Teramoto, R Raymond, E Wakakuwa… - arxiv preprint arxiv …, 2023 - arxiv.org
Quantum Random Access Optimizer (QRAO) is a quantum-relaxation based optimization
algorithm proposed by Fuller et al. that utilizes Quantum Random Access Code (QRAC) to …
algorithm proposed by Fuller et al. that utilizes Quantum Random Access Code (QRAC) to …
Recursive quantum relaxation for combinatorial optimization problems
Quantum optimization methods use a continuous degree-of-freedom of quantum states to
heuristically solve combinatorial problems, such as the MAX-CUT problem, which can be …
heuristically solve combinatorial problems, such as the MAX-CUT problem, which can be …
The role of entanglement in quantum-relaxation based optimization algorithms
K Teramoto, R Raymond, H Imai - 2023 IEEE International …, 2023 - ieeexplore.ieee.org
Quantum Random Access Optimizer (QRAO) is a quantum-relaxation based optimization
algorithm proposed by Fuller et al. that utilizes Quantum Random Access Code (QRAC) to …
algorithm proposed by Fuller et al. that utilizes Quantum Random Access Code (QRAC) to …
Trainable discrete feature embeddings for quantum machine learning
N Thumwanit, C Lortaraprasert… - 2021 58th ACM/IEEE …, 2021 - ieeexplore.ieee.org
Quantum classifiers provide sophisticated embeddings of input data in Hilbert space
promising quantum advantage. The advantage stems from quantum feature maps encoding …
promising quantum advantage. The advantage stems from quantum feature maps encoding …
Trainable discrete feature embeddings for variational quantum classifier
N Thumwanit, C Lortaraprasert, H Yano… - arxiv preprint arxiv …, 2021 - arxiv.org
Quantum classifiers provide sophisticated embeddings of input data in Hilbert space
promising quantum advantage. The advantage stems from quantum feature maps encoding …
promising quantum advantage. The advantage stems from quantum feature maps encoding …
Noise Robustness of Quantum Relaxation for Combinatorial Optimization
QRAO (Quantum Random Access Optimization) is a relaxation algorithm that reduces the
number of qubits required to solve a problem by encoding multiple variables per qubit using …
number of qubits required to solve a problem by encoding multiple variables per qubit using …
Quantum random access codes for Boolean functions
JF Doriguello, A Montanaro - Quantum, 2021 - quantum-journal.org
Abstract An $ n\overset {p}{\mapsto} m $ random access code (RAC) is an encoding of $ n $
bits into $ m $ bits such that any initial bit can be recovered with probability at least $ p …
bits into $ m $ bits such that any initial bit can be recovered with probability at least $ p …
The geometry of Bloch space in the context of quantum random access codes
L Mančinska, SAL Storgaard - Quantum Information Processing, 2022 - Springer
We study the communication protocol known as a quantum random access code (QRAC)
which encodes n classical bits into m qubits (m< n) with a probability of recovering any of the …
which encodes n classical bits into m qubits (m< n) with a probability of recovering any of the …