no code implementations • 6 Feb 2024 • Chukwudubem Umeano, Vincent E. Elfving, Oleksandr Kyriienko

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.

no code implementations • 18 Jan 2024 • Dominik Seitz, Niklas Heim, João P. Moutinho, Roland Guichard, Vytautas Abramavicius, Aleksander Wennersteen, Gert-Jan Both, Anton Quelle, Caroline de Groot, Gergana V. Velikova, Vincent E. Elfving, Mario Dagrada

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.

no code implementations • 6 Sep 2023 • Ben Jaderberg, Antonio A. Gentile, Youssef Achari Berrada, Elvira Shishenina, Vincent E. Elfving

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.

no code implementations • 31 Aug 2023 • Chukwudubem Umeano, Annie E. Paine, Vincent E. Elfving, Oleksandr Kyriienko

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.

no code implementations • 14 Dec 2022 • Atiyo Ghosh, Antonio A. Gentile, Mario Dagrada, Chul Lee, Seong-hyok Kim, Hyukgeun Cha, Yunjun Choi, Brad Kim, Jeong-il Kye, Vincent E. Elfving

Harmonic functions are abundant in nature, appearing in limiting cases of Maxwell's, Navier-Stokes equations, the heat and the wave equation.

no code implementations • 6 Dec 2022 • Lucas Leclerc, Luis Ortiz-Guitierrez, Sebastian Grijalva, Boris Albrecht, Julia R. K. Cline, Vincent E. Elfving, Adrien Signoles, Loïc Henriet, Gianni Del Bimbo, Usman Ayub Sheikh, Maitree Shah, Luc Andrea, Faysal Ishtiaq, Andoni Duarte, Samuel Mugel, Irene Caceres, Michel Kurek, Roman Orus, Achraf Seddik, Oumaima Hammammi, Hacene Isselnane, Didier M'tamon

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.

no code implementations • 24 Oct 2022 • Sachin Kasture, Oleksandr Kyriienko, Vincent E. Elfving

When applied to industrially relevant distributions this combination of classical training with quantum sampling represents an avenue for reaching advantage in the NISQ era.

no code implementations • 28 Jun 2022 • Niraj Kumar, Evan Philip, Vincent E. Elfving

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.

no code implementations • 5 May 2022 • Savvas Varsamopoulos, Evan Philip, Herman W. T. van Vlijmen, Sairam Menon, Ann Vos, Natalia Dyubankova, Bert Torfs, Anthony Rowe, Vincent E. Elfving

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.

no code implementations • 11 Nov 2021 • Niklas Heim, Atiyo Ghosh, Oleksandr Kyriienko, Vincent E. Elfving

One of the most promising general approaches is based on recent developments in the field of scientific machine learning for solving PDEs.

no code implementations • 6 Aug 2021 • Annie E. Paine, Vincent E. Elfving, Oleksandr Kyriienko

We propose a quantum algorithm for sampling from a solution of stochastic differential equations (SDEs).

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