Solving Big Math Problems with Small Quantum Computers
This patent describes how a classical computer can break down large mathematical problems into smaller pieces that even limited quantum computers can solve, then combine the results.
Original patent title: “Machine learning mapping for quantum processing units”
This patent describes how a classical computer can break down large mathematical problems into smaller pieces that even limited quantum computers can solve, then combine the results. Granted to Quantum Computing in 2024 with 26 claims, and it is expected to expire in 2042.
Coverage
What does this patent actually cover?
This patent describes a method for solving complex mathematical problems, called 'objective functions,' that are too large for current quantum computers. First, a classical computer obtains an objective function that has more variables than the quantum computer has 'logical qubits' (ClaimclaimA numbered sentence at the end of a patent that legally defines what the inventor owns. The most important section.Read more → 1). Next, the classical computer uses machine learning to break down this big problem into several smaller 'sub-problems,' each small enough for the quantum computer to handle (AbstractabstractA short summary at the front of the patent describing the invention. Not legally binding.Read more →, Claim 1). The quantum computer then solves each sub-problem by initializing its qubits, applying changes (perturbations), and measuring the results multiple times ('shots') (Claim 1). From these measurements, an 'expectation value' is determined for each sub-problem, leading to its solution (Claim 1). Finally, the classical computer gathers all the sub-problem solutions to find the overall solution to the original big problem and stores it (Claim 1). For example, if you have a complex optimization problem with 100 variables, but your quantum computer only has 10 logical qubits, this method would break the 100-variable problem into several 10-variable sub-problems.
The gap
What does this patent NOT cover?
- Does not cover solving mathematical problems that involve fewer variables than the quantum computer has logical qubits, meaning the problem fits directly on the quantum computer (ClaimclaimA numbered sentence at the end of a patent that legally defines what the inventor owns. The most important section.Read more → 1).
- Does not cover methods that solve the entire objective function directly on a quantum computer without first decomposing it into smaller sub-problems (ClaimclaimA numbered sentence at the end of a patent that legally defines what the inventor owns. The most important section.Read more → 1).
- Does not cover purely classical computer systems solving large problems without involving a quantum computing system for sub-problem solutions (ClaimclaimA numbered sentence at the end of a patent that legally defines what the inventor owns. The most important section.Read more → 1).
- Does not cover determining sub-problem solutions without using an 'expectation value' derived from a set of raw outputs from multiple 'shots' (ClaimclaimA numbered sentence at the end of a patent that legally defines what the inventor owns. The most important section.Read more → 1).
- Does not cover decomposition methods that do not involve machine learning, as described in the abstractabstractA short summary at the front of the patent describing the invention. Not legally binding.Read more →.
These exclusions are unique to PatentBrief — derived from the actual claim language, not patent-office boilerplate.
Key facts
What made this novel
The core innovation is intelligently breaking down a problem too big for a quantum computer into smaller, manageable pieces using machine learning, then reassembling the quantum-derived solutions. This overcomes the major hurdle of limited qubit availability in current quantum hardware.
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
Optimization problems in logistics or finance
Drug discovery simulations
Materials science research
Quantum machine learning applications
Hybrid quantum-classical algorithms
Why it matters
The bigger picture
Quantum computers today have a limited number of stable qubits, which restricts the size and complexity of problems they can solve. This patent addresses a fundamental challenge in quantum computing by providing a way to tackle larger, more commercially relevant problems despite these hardware limitations. It enables a hybrid approach, combining the strengths of classical computers for problem management with quantum computers for specific computational tasks. This approach is crucial for expanding the practical applications of quantum computing in the near term.
Filed
June 30, 2022
Granted
June 11, 2024
Market context
Who's building on this
Companies in this space
Quantum Computing Inc., the assigneeassigneeThe entity that owns the patent — usually the inventor's employer or a company.Read more →, is actively developing solutions in this space. Other companies like IBM, Google, and Amazon Web Services (AWS) are also investing heavily in hybrid quantum-classical computing architectures and algorithms to overcome current qubit limitations and expand the practical applications of quantum technology.
Market impact
This patent addresses a critical bottleneck in the quantum computing market: the limited number of stable qubits. By enabling smaller quantum computers to tackle larger problems, it potentially expands the addressable market for quantum solutions. This could accelerate the development and adoption of quantum computing across various industries, making quantum hardware more useful sooner than if only full-scale quantum computers could solve complex problems.
Claim 1 — Plain English
What this patent covers
This patent describes a method for solving complex mathematical problems, called 'objective functions,' that are too large for current quantum computers. First, a classical computer obtains an objective function that has more variables than the quantum computer has 'logical qubits' (Claim 1). Next, the classical computer uses machine learning to break down this big problem into several smaller 'sub-problems,' each small enough for the quantum computer to handle (Abstract, Claim 1). The quantum computer then solves each sub-problem by initializing its qubits, applying changes (perturbations), and measuring the results multiple times ('shots') (Claim 1). From these measurements, an 'expectation value' is determined for each sub-problem, leading to its solution (Claim 1). Finally, the classical computer gathers all the sub-problem solutions to find the overall solution to the original big problem and stores it (Claim 1). For example, if you have a complex optimization problem with 100 variables, but your quantum computer only has 10 logical qubits, this method would break the 100-variable problem into several 10-variable sub-problems.
The clever bit
The core innovation is intelligently breaking down a problem too big for a quantum computer into smaller, manageable pieces using machine learning, then reassembling the quantum-derived solutions. This overcomes the major hurdle of limited qubit availability in current quantum hardware.
What it does not cover
- Does not cover solving mathematical problems that involve fewer variables than the quantum computer has logical qubits, meaning the problem fits directly on the quantum computer (Claim 1).
- Does not cover methods that solve the entire objective function directly on a quantum computer without first decomposing it into smaller sub-problems (Claim 1).
- Does not cover purely classical computer systems solving large problems without involving a quantum computing system for sub-problem solutions (Claim 1).
- Does not cover determining sub-problem solutions without using an 'expectation value' derived from a set of raw outputs from multiple 'shots' (Claim 1).
- Does not cover decomposition methods that do not involve machine learning, as described in the abstract.
Patent timeline
Application submitted to the patent office
Application published, typically 18 months after filing
Patent officially issued
Patent enters public domain
PatentBrief Score
Impact Score
Early stage
Citation count
0/40
No citations yet
Claim breadth
17/20
Very broad protection
Recency
20/20
Granted within 5 years
Assignee scale
0/20
Independent or smaller assigneeassigneeThe entity that owns the patent — usually the inventor's employer or a company.Read more →
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
$37K – $120K
Midpoint $75K · 15.7 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
26 claims as filed with the patent office.
Concepts involved
Citations
Patent lineage
Cite this patent
Berwald, J., Dridi, R., & Chukwu, U. (2024). Solving Big Math Problems with Small Quantum Computers (U.S. Patent No. 12,008,436). U.S. Patent and Trademark Office. https://patentbrief.org/patent/us/12008436/machine-learning-mapping-for-quantum-processing-units
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 Solving Big Math Problems with Small Quantum Computers cover?
This patent describes how a classical computer can break down large mathematical problems into smaller pieces that even limited quantum computers can solve, then combine the results.
Who owns patent US 12008436?
Quantum Computing owns this patent, granted in 2024.
When does this patent expire?
This patent is expected to expire on June 30, 2042, when the invention enters the public domain.
What problem does this patent solve?
Quantum computers today have a limited number of stable qubits, which restricts the size and complexity of problems they can solve. This patent addresses a fundamental challenge in quantum computing by providing a way to tackle larger, more commercially relevant problems despite these hardware limitations. It enables a hybrid approach, combining the strengths of classical computers for problem management with quantum computers for specific computational tasks. This approach is crucial for expanding the practical applications of quantum computing in the near term.
What does this patent NOT cover?
Does not cover solving mathematical problems that involve fewer variables than the quantum computer has logical qubits, meaning the problem fits directly on the quantum computer (Claim 1).
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