Hardware-Aware Tucker Data Loading for Lower-Depth Quantum Circuits

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Solution Overview

Problem

Existing quantum computing systems face challenges in efficiently encoding classical data into quantum circuits due to high entanglement levels and the need for cumbersome hierarchical bipartitions, particularly in noisy intermediate-scale quantum (NISQ) devices, which lack effective strategies for translating tensor decompositions into quantum circuits.

Innovation Solution

A system and method utilizing the Tucker decomposition with an iterative process to find an optimal representation of classical data as a quantum state, guided by performance measures, reducing entanglement and circuit complexity by employing unitary and isometry operators to transform the data into a quantum circuit suitable for execution on quantum processing units (QPUs).

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Reliability

If classical data is encoded into quantum circuits using traditional methods, then the quantum state can be prepared, but the entanglement level becomes high and the circuit structure becomes complex requiring hierarchical bipartitions

Engineering Contradiction:

Improvestate preparation fidelityVSAvoidcircuit structure complexity

Core Design Contradiction:

ReliabilityVSDevice complexity

Solution Approach 1:

The patent applies parameter changes by transforming the data representation from classical vector form to quantum state form through unitary transformations. The method optimizes the quantum circuit parameters (gate sequences, qubit arrangements) to achieve efficient encoding with reduced entanglement and circuit depth, directly addressing the complexity issue while maintaining fidelity

Inventive Principle:

Principle #35Parameter changes

Solution Approach 2:

The patent replaces traditional mechanical data encoding approaches with quantum mechanical state preparation techniques. By using quantum gates and unitary transformations to encode classical data into quantum states, the system achieves more efficient representation without requiring complex hierarchical circuit structures

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

2Productivity

If traditional data encoding methods are used in NISQ devices, then data can be loaded into quantum processors, but the lack of effective tensor decomposition strategies results in inefficient encoding

Engineering Contradiction:

Improvedata encoding efficiencyVSAvoidencoding process complexity

Core Design Contradiction:

ProductivityVSDevice complexity

Solution Approach 1:

The patent segments the data encoding process into discrete tensor decomposition steps, breaking down the complex task of loading classical data into quantum states into manageable operations. By using tensor network representations and hierarchical decompositions, the method systematically constructs quantum circuits from simpler components, improving efficiency while controlling complexity

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

The patent introduces tensor network representations as an intermediary between classical data and quantum states. This intermediary framework provides a systematic way to translate classical data into quantum circuits through tensor decompositions, serving as a bridge that simplifies the encoding process and makes it more efficient for NISQ devices

Inventive Principle:

Principle #24Intermediary (Mediator)

3Loss of information

If high entanglement is used to represent classical data, then the quantum state captures all data information, but the circuit depth increases and resource requirements grow

Engineering Contradiction:

Improvedata information completenessVSAvoidcircuit execution time

Core Design Contradiction:

Loss of informationVSDuration of action of moving object

Solution Approach 1:

The patent applies partial action by using approximate tensor decompositions that capture the essential information in the classical data without requiring complete exact representations. By accepting near-optimal decompositions with controlled error, the method reduces circuit depth and resource requirements while maintaining sufficient information fidelity for quantum computations

Inventive Principle:

Principle #16Partial or excessive action

Data Source

PatentUS20250307678A1System and method of loading classical data into a quantum processor via an adaptive hardware-aware state preparation using iterative tensor-decompositions and quantum circuit generation

Publication Date: 2025.10.02 DATA CYBERNETICS SSC GMBH

AI summary

A system and method for encoding classical data into an executable quantum state with n executable d-level qudits (including two-level qubits; d=2) designed for a quantum processor. The classical data in the form of an N-dimensional complex-valued vector (N=dn) is received and loaded by a loading device onto qudits that are in a known initial state. The classical data is initialized as a dn-dimensional complex-valued and normalized data vector to obtain initialized data representing the n executable qudits state. A representation of the initialized data is found via the Tucker tensor decomposition using a tensor G and unitary operators while minimizing a decomposition performance measure. Further, the representation is translated into a quantum gate set to be applied to the initial quantum circuit to obtain a prepared quantum circuit while a state approximation performance measure of the executable quantum state is minimized. The method is hardware-aware and iterative, where the representation search is guided by the performance measures.