How Quantum Computers Learn to Sort Data
This patent describes a method for training a data classifier using a quantum computer, where classical AI helps prepare the data and interpret the quantum results to improve the quantum system's ability to sort information.
Original patent title: “Classification using quantum neural networks”
This patent describes a method for training a data classifier using a quantum computer, where classical AI helps prepare the data and interpret the quantum results to improve the quantum system's ability to sort information. Owned by Google with 24 claims, and it is expected to expire in 2044.
Coverage
What does this patent actually cover?
The patent details a method for training a classifier that runs on a quantum computer. First, a classical artificial neural network prepares a set of quantum bits (qubits) in an input state with known categories (ClaimclaimA numbered sentence at the end of a patent that legally defines what the inventor owns. The most important section.Read more → 1). These qubits then go through a series of "parameterized quantum gates" — like adjustable switches — which change their quantum state. Another classical artificial neural network then reads the final state of specific "readout qubits" to guess the category of the initial data. This guess is compared to the known category, and the quantum gates' adjustable settings (parameters) are tweaked to make future guesses more accurate (Claim 1, 5). This training process repeats until the classifier performs well (Claim 2). Once trained, the quantum computer can then classify new, unknown data by applying the learned quantum gates and interpreting the readout with the second classical network (Claim 15). For example, a quantum computer could be trained to distinguish between quantum states representing different types of particles, with classical AI assisting in the setup and interpretation.
The gap
What does this patent NOT cover?
- Training a quantum classifier without using any classical artificial neural networks for preparing the input state or determining the predicted classification (ClaimclaimA numbered sentence at the end of a patent that legally defines what the inventor owns. The most important section.Read more → 1).
- Classification methods that do not involve parameterized quantum gates (ClaimclaimA numbered sentence at the end of a patent that legally defines what the inventor owns. The most important section.Read more → 1).
- Quantum machine learning tasks other than classification, such as regression or generative models.
- Training methods that update parameters of a classical neural network based on quantum computations, rather than updating parameters of quantum gates.
- Classification systems where the input state is prepared purely classically without being encoded into qubits.
These exclusions are unique to PatentBrief — derived from the actual claim language, not patent-office boilerplate.
Key facts
What made this novel
The noveltynoveltyThe requirement that an invention be different from anything publicly known before its priority date.Read more → lies in the hybrid approach, specifically using two distinct classical artificial neural networks to assist the quantum computer during the training process: one for preparing the input quantum state and another for interpreting the quantum readout to determine the predicted classification. This bridges the gap between classical data and quantum computation, making the quantum classifier trainable and usable.
The Patent Drawing
Schematic visualization of the patent's claim structure. Hand-drawn diagrams in progress for each landmark patent.
Where you've seen this
Real-world examples
Google's Sycamore processor running classification algorithms
IBM Quantum Experience exploring hybrid quantum-classical machine learning
Quantum-enhanced image recognition
Quantum algorithms for financial fraud detection
Drug discovery applications for classifying molecular structures
Why it matters
The bigger picture
Quantum computers hold promise for solving problems too complex for traditional computers, including advanced machine learning. This patent addresses a fundamental challenge in quantum machine learning: how to effectively train these new types of classifiers. By combining classical AI with quantum processing, it aims to make quantum computers practical for tasks like pattern recognition and data sorting, potentially speeding up drug discovery, materials science, and financial modeling.
Filed
April 26, 2024
Market context
Who's building on this
Companies in this space
Google LLC, the assigneeassigneeThe entity that owns the patent — usually the inventor's employer or a company.Read more →, is a major player in quantum computing research and development, actively pursuing quantum machine learning. Other companies like IBM, Microsoft, and various startups in the quantum software space are also exploring hybrid quantum-classical algorithms for machine learning. Academic institutions and government labs worldwide are also significant contributors to this field.
Market impact
This patent contributes to the foundational intellectual property for quantum machine learning, an area expected to grow significantly. As quantum hardware matures, methods like this could enable new classes of AI applications, potentially impacting industries from finance to healthcare by allowing for the analysis of datasets currently intractable for classical computers. It could also influence the design of future quantum computing platforms that integrate classical AI components.
Claim 1 — Plain English
What this patent covers
The patent details a method for training a classifier that runs on a quantum computer. First, a classical artificial neural network prepares a set of quantum bits (qubits) in an input state with known categories (Claim 1). These qubits then go through a series of "parameterized quantum gates" — like adjustable switches — which change their quantum state. Another classical artificial neural network then reads the final state of specific "readout qubits" to guess the category of the initial data. This guess is compared to the known category, and the quantum gates' adjustable settings (parameters) are tweaked to make future guesses more accurate (Claim 1, 5). This training process repeats until the classifier performs well (Claim 2). Once trained, the quantum computer can then classify new, unknown data by applying the learned quantum gates and interpreting the readout with the second classical network (Claim 15). For example, a quantum computer could be trained to distinguish between quantum states representing different types of particles, with classical AI assisting in the setup and interpretation.
The clever bit
The novelty lies in the hybrid approach, specifically using two distinct classical artificial neural networks to assist the quantum computer during the training process: one for preparing the input quantum state and another for interpreting the quantum readout to determine the predicted classification. This bridges the gap between classical data and quantum computation, making the quantum classifier trainable and usable.
What it does not cover
- Training a quantum classifier without using any classical artificial neural networks for preparing the input state or determining the predicted classification (Claim 1).
- Classification methods that do not involve parameterized quantum gates (Claim 1).
- Quantum machine learning tasks other than classification, such as regression or generative models.
- Training methods that update parameters of a classical neural network based on quantum computations, rather than updating parameters of quantum gates.
- Classification systems where the input state is prepared purely classically without being encoded into qubits.
Patent timeline
Application submitted to the patent office
Patent enters public domain
PatentBrief Score
Impact Score
Early stage
Citation count
0/40
No citations yet
Claim breadth
16/20
Broad claimsclaimsThe numbered statements at the end of a patent that legally define what the inventor owns.Read more →
Recency
0/20
Older than 20 years
Assignee scale
20/20
Major company or institution
PatentBrief Impact Score — based on citation count, claim breadth, recency, and assignee scale. Not a legal assessment.
Heuristic Value Estimate
What this patent might be worth
$31K – $100K
Midpoint $62K · 17.7 yr remaining · industry ×1.6
Heuristic only — blends forward/backward citation counts, claim scope, time remaining, litigation history, and CPC-derived industry baseline. Real valuations need a professional appraisal.
Claim text not yet imported for this patent
The original legal language
Original claims
24 claims as filed with the patent office.
Concepts involved
Cite this patent
Farhi, E. H., & Neven, H. How Quantum Computers Learn to Sort Data (U.S. Patent No. 20,240,296,359). U.S. Patent and Trademark Office. https://patentbrief.org/patent/us/20240296359/classification-using-quantum-neural-networks
Auto-generated from the patent record. Double-check author order and the issue date against the official USPTO document before submitting.
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Common Questions
Frequently Asked Questions
What does How Quantum Computers Learn to Sort Data cover?
This patent describes a method for training a data classifier using a quantum computer, where classical AI helps prepare the data and interpret the quantum results to improve the quantum system's ability to sort information.
Who owns patent US 20240296359?
This patent is owned by Google.
When does this patent expire?
This patent is expected to expire on April 26, 2044, when the invention enters the public domain.
What problem does this patent solve?
Quantum computers hold promise for solving problems too complex for traditional computers, including advanced machine learning. This patent addresses a fundamental challenge in quantum machine learning: how to effectively train these new types of classifiers. By combining classical AI with quantum processing, it aims to make quantum computers practical for tasks like pattern recognition and data sorting, potentially speeding up drug discovery, materials science, and financial modeling.
What does this patent NOT cover?
Training a quantum classifier without using any classical artificial neural networks for preparing the input state or determining the predicted classification (Claim 1).
Same assignee
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