Tensor-Network Noise Mitigation for Near-Term Quantum Circuits

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Solution Overview

Problem

Existing noise mitigation strategies for quantum computing, such as probabilistic error cancellation (PEC) and approximate noise inversion, face challenges in measurement overhead and computational complexity, particularly in near-term quantum computing, where noise mitigation is often implemented in hardware, limiting scalability and efficiency.

Innovation Solution

A scalable tensor network method for noise mitigation that relocates error correction to the classical postprocessing stage, utilizing informationally complete measurements to construct a noise mitigation map, allowing for efficient estimation of observables with reduced measurement overhead and computational complexity.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Reliability

If hardware-based error mitigation methods are used, then noise mitigation can be achieved, but measurement overhead increases significantly

Engineering Contradiction:

Improvenoise mitigationVSAvoidmeasurement overhead

Core Design Contradiction:

ReliabilityVSQuantity of substance

Solution Approach 1:

The patent introduces a tensor network as an intermediary computational structure that mediates between the noisy quantum circuit outputs and the final observable estimates. The tensor network represents the noise mitigation map, allowing classical postprocessing to correct noise effects without requiring additional quantum measurements, thus reducing measurement overhead while maintaining noise mitigation capability

Inventive Principle:

Principle #24Intermediary (Mediator)

Solution Approach 2:

The patent replaces hardware-based error mitigation mechanisms with a classical computational approach using tensor networks. Instead of implementing noise mitigation through additional quantum hardware operations and measurements, the solution substitutes a classical postprocessing algorithm that uses tensor network contractions to mitigate noise effects, thereby reducing the measurement overhead

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

2Measurement precision

If probabilistic error cancellation is used, then noise-free circuit estimation is achieved, but measurement overhead increases

Engineering Contradiction:

Improveobservable estimationVSAvoidmeasurement outcomes needed

Core Design Contradiction:

Measurement precisionVSQuantity of substance

Solution Approach 1:

The patent performs preliminary construction of the tensor network representation of the noise mitigation map before actual measurement and estimation. By pre-computing the tensor network structure and contraction rules, the method prepares the computational framework in advance, allowing efficient processing of measurement outcomes without requiring excessive measurements during the actual estimation phase

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The patent changes the computational parameters and representation of the noise mitigation problem from direct quasi-probability sampling to tensor network contractions. This parameter change transforms the approach from one requiring many measurement outcomes to one that achieves the same precision with fewer measurements by utilizing the structured computational power of tensor networks

Inventive Principle:

Principle #35Parameter changes

3Reliability

If noise mitigation is implemented in hardware, then error correction is achieved, but device complexity and scalability are limited

Engineering Contradiction:

Improveerror correctionVSAvoidhardware implementation

Core Design Contradiction:

ReliabilityVSDevice complexity

Solution Approach 1:

The patent substitutes hardware-based error correction mechanisms with a classical computational system using tensor networks. Instead of implementing complex quantum hardware operations for noise mitigation, the solution uses classical computers to perform tensor network contractions on measurement data, significantly reducing device complexity and improving scalability

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Solution Approach 2:

The patent creates a classical computational copy of the noise mitigation process through tensor networks. Rather than physically implementing noise correction in quantum hardware, the method creates a mathematical representation (tensor network) of the noise mitigation map that can be efficiently computed classically, thereby reducing hardware complexity while maintaining error correction functionality

Inventive Principle:

Principle #26Copying

Data Source

PatentUS20260010815A1Scalable tensor-network-based noise mitigation for near-term quantum computing

Publication Date: 2026.01.08 ALGORITHMIQ OY

AI summary

In one aspect, there is provided a noise mitigation method for an execution of a quantum circuit by a quantum processor. In another aspect, there is provided a computing system comprising a quantum processor and a classical computer, the computing system being configured to carry out the method. In another aspect, there is a computer program product including instructions which, when the program is carried out by a computer system comprising a classical computer and a quantum processor, cause the computer system to carry out the method. In another aspect, there is provided a computer program product including instructions which, when the program is carried out by a classical computer, cause the classical computer to carry out the tensor network contractions according to the method.