The randomized measurement toolbox
Programmable quantum simulators and quantum computers are opening unprecedented
opportunities for exploring and exploiting the properties of highly entangled complex …
opportunities for exploring and exploiting the properties of highly entangled complex …
Recent advances for quantum classifiers
Abstract Machine learning has achieved dramatic success in a broad spectrum of
applications. Its interplay with quantum physics may lead to unprecedented perspectives for …
applications. Its interplay with quantum physics may lead to unprecedented perspectives for …
[HTML][HTML] Quantum machine learning beyond kernel methods
Abstract Machine learning algorithms based on parametrized quantum circuits are prime
candidates for near-term applications on noisy quantum computers. In this direction, various …
candidates for near-term applications on noisy quantum computers. In this direction, various …
Experimental quantum adversarial learning with programmable superconducting qubits
W Ren, W Li, S Xu, K Wang, W Jiang, F **… - Nature Computational …, 2022 - nature.com
Quantum computing promises to enhance machine learning and artificial intelligence.
However, recent theoretical works show that, similar to traditional classifiers based on deep …
However, recent theoretical works show that, similar to traditional classifiers based on deep …
Nanowire-based integrated photonics for quantum information and quantum sensing
At the core of quantum photonic information processing and sensing, two major building
pillars are single-photon emitters and single-photon detectors. In this review, we …
pillars are single-photon emitters and single-photon detectors. In this review, we …
Quantum machine learning on near-term quantum devices: Current state of supervised and unsupervised techniques for real-world applications
The past decade has witnessed significant advancements in quantum hardware,
encompassing improvements in speed, qubit quantity, and quantum volume—a metric …
encompassing improvements in speed, qubit quantity, and quantum volume—a metric …
Quantum neural network classifiers: A tutorial
Abstract Machine learning has achieved dramatic success over the past decade, with
applications ranging from face recognition to natural language processing. Meanwhile, rapid …
applications ranging from face recognition to natural language processing. Meanwhile, rapid …
Probing many-body localization by excited-state variational quantum eigensolver
Nonequilibrium physics including many-body localization (MBL) has attracted increasing
attentions, but theoretical approaches of reliably studying nonequilibrium properties remain …
attentions, but theoretical approaches of reliably studying nonequilibrium properties remain …
Fock state-enhanced expressivity of quantum machine learning models
The data-embedding process is one of the bottlenecks of quantum machine learning,
potentially negating any quantum speedups. In light of this, more effective data-encoding …
potentially negating any quantum speedups. In light of this, more effective data-encoding …
A preprocessing perspective for quantum machine learning classification advantage in finance using NISQ algorithms
J Mancilla, C Pere - Entropy, 2022 - mdpi.com
Quantum Machine Learning (QML) has not yet demonstrated extensively and clearly its
advantages compared to the classical machine learning approach. So far, there are only …
advantages compared to the classical machine learning approach. So far, there are only …