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Geometric quantum machine learning of BQP$^A$ protocols and latent graph classifiers
In this Letter we consider Simon's problem for learning properties of Boolean functions, and show that this can be related to an unsupervised circuit classification problem.
Qadence: a differentiable interface for digital-analog programs
Digital-analog quantum computing (DAQC) is an alternative paradigm for universal quantum computation combining digital single-qubit gates with global analog operations acting on a register of interacting qubits.
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 uniquely determined by the feature map's generator Hamiltonians.
What can we learn from quantum convolutional neural networks?
We can learn from analyzing quantum convolutional neural networks (QCNNs) that: 1) working with quantum data can be perceived as embedding physical system parameters through a hidden feature map; 2) their high performance for quantum phase recognition can be attributed to generation of a very suitable basis set during the ground state embedding, where quantum criticality of spin models leads to basis functions with rapidly changing features; 3) pooling layers of QCNNs are responsible for picking those basis functions that can contribute to forming a high-performing decision boundary, and the learning process corresponds to adapting the measurement such that few-qubit operators are mapped to full-register observables; 4) generalization of QCNN models strongly depends on the embedding type, and that rotation-based feature maps with the Fourier basis require careful feature engineering; 5) accuracy and generalization of QCNNs with readout based on a limited number of shots favor the ground state embeddings and associated physics-informed models.
Harmonic (Quantum) Neural Networks
Harmonic functions are abundant in nature, appearing in limiting cases of Maxwell's, Navier-Stokes equations, the heat and the wave equation.
Financial Risk Management on a Neutral Atom Quantum Processor
In this work we propose a quantum-enhanced machine learning solution for the prediction of credit rating downgrades, also known as fallen-angels forecasting in the financial risk management field.
Protocols for classically training quantum generative models on probability distributions
When applied to industrially relevant distributions this combination of classical training with quantum sampling represents an avenue for reaching advantage in the NISQ era.
Integral Transforms in a Physics-Informed (Quantum) Neural Network setting: Applications & Use-Cases
Recently, the machine learning paradigm of Physics-Informed Neural Networks emerged with increasing popularity as a method to solve differential equations by leveraging automatic differentiation.
Quantum Extremal Learning
The algorithm, called quantum extremal learning (QEL), consists of a parametric quantum circuit that is variationally trained to model data input-output relationships and where a trainable quantum feature map, that encodes the input data, is analytically differentiated in order to find the coordinate that extremizes the model.
Quantum Model-Discovery
One of the most promising general approaches is based on recent developments in the field of scientific machine learning for solving PDEs.
Quantum Quantile Mechanics: Solving Stochastic Differential Equations for Generating Time-Series
We propose a quantum algorithm for sampling from a solution of stochastic differential equations (SDEs).
