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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 …
Unsupervised quantum machine learning for fraud detection
We develop quantum protocols for anomaly detection and apply them to the task of credit
card fraud detection (FD). First, we establish classical benchmarks based on supervised and …
card fraud detection (FD). First, we establish classical benchmarks based on supervised and …
Classically approximating variational quantum machine learning with random fourier features
Many applications of quantum computing in the near term rely on variational quantum
circuits (VQCs). They have been showcased as a promising model for reaching a quantum …
circuits (VQCs). They have been showcased as a promising model for reaching a quantum …
Quantum feature maps for graph machine learning on a neutral atom quantum processor
Using a quantum processor to embed and process classical data enables the generation of
correlations between variables that are inefficient to represent through classical …
correlations between variables that are inefficient to represent through classical …
Equivariant quantum graph circuits
We investigate quantum circuits for graph representation learning, and propose equivariant
quantum graph circuits (EQGCs), as a class of parameterized quantum circuits with strong …
quantum graph circuits (EQGCs), as a class of parameterized quantum circuits with strong …
Quantum phase recognition via quantum kernel methods
The application of quantum computation to accelerate machine learning algorithms is one of
the most promising areas of research in quantum algorithms. In this paper, we explore the …
the most promising areas of research in quantum algorithms. In this paper, we explore the …
Let quantum neural networks choose their own frequencies
Parameterized quantum circuits as machine learning models are typically well described by
their representation as a partial Fourier series of the input features, with frequencies …
their representation as a partial Fourier series of the input features, with frequencies …
[كتاب][B] Quantum machine learning and optimisation in finance: on the road to quantum advantage
A Jacquier, O Kondratyev, A Lipton, ML de Prado - 2022 - books.google.com
Learn the principles of quantum machine learning and how to apply them While focus is on
financial use cases, all the methods and techniques are transferable to other fields Purchase …
financial use cases, all the methods and techniques are transferable to other fields Purchase …
Financial risk management on a neutral atom quantum processor
Machine learning models capable of handling the large data sets collected in the financial
world can often become black boxes expensive to run. The quantum computing paradigm …
world can often become black boxes expensive to run. The quantum computing paradigm …
[HTML][HTML] Benchmarking Big Data Systems: Performance and Decision-Making Implications in Emerging Technologies
Systems for graph processing are a key enabler for insights from large-scale graphs that are
critical to many new advanced technologies such as Artificial Intelligence, Internet of Things …
critical to many new advanced technologies such as Artificial Intelligence, Internet of Things …