Solving computational problems using trainable quantum feature maps

EP4747813A1Pending Publication Date: 2026-05-27PASQAL SAS
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Patent Information

Authority / Receiving Office
EP · EP
Patent Type
Applications
Current Assignee / Owner
PASQAL SAS
Filing Date
2024-07-19
Publication Date
2026-05-27

AI Technical Summary

Technical Problem

Existing quantum machine learning models face challenges in accurately solving complex computational problems like differential equations due to fixed feature encoding architectures, which limit the representation space and require prior knowledge of optimal encoding configurations.

Method used

The introduction of trainable quantum feature maps, where a set of trainable parameters acts directly on the generator Hamiltonian, allows for the dynamic tuning of feature maps during training, optimizing eigenfrequencies and improving the model's ability to approximate solutions to complex problems.

Benefits of technology

This approach enables quantum models to learn optimal basis functions and spectral properties, leading to more accurate and efficient solutions for computational problems like differential equations, without increasing the depth of quantum circuits.

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Abstract

A method and system for solving a computational problem using a hybrid data processor comprising a classical computer and quantum computer the method comprising receiving or determining information on one or more quantum circuits defining operations of a parameterized quantum model of a target function, the target function representing an approximate solution to the computational problem, preferably one or more differential equations and one or more associated boundary conditions, the one or more quantum circuits comprising one or more quantum feature maps for encoding one or more classical input features associated with the target function into the Hilbert space of the quantum register, wherein the one or more feature maps include one or more unitary operators defining a time evolution over a Hamiltonian, preferably a generator Hamiltonian, applied to quantum elements of the quantum register, the Hamiltonian evolution being parameterized by a first set of variational parameters and the one or more classical input features; and, determining the target function based on the parameterized quantum model and a loss function associated with the computational problem, the determining including varying the first set of variational parameters to determine a set of eigenfrequencies of the Hamiltonian which is optimized for the computational problem, the set of eigenfrequencies defining quantum modes, which the parameterized quantum.
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