Programmable Quantum Annealer with Reconfigurable Qubit Interactions
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Solution Overview
Problem
Current quantum computing devices, including those based on superconducting qubits, are limited by hard-wired constraints that restrict interactions between qubits, making them non-programmable and non-scalable, thus unable to efficiently solve a wide range of computational problems.
Innovation Solution
A quantum system comprising a plurality of qubits is used to encode computational problems into a problem Hamiltonian, which is then evolved through an initial to a final Hamiltonian via quantum annealing, allowing for interactions between qubits to reveal solutions, with a focus on single-body and short-range interactions to enable fully programmable and scalable architectures.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If quantum computing devices use hard-wired constraints to define qubit interactions, then the device structure is simplified and manufacturing is easier, but the device becomes non-programmable and non-scalable, limiting its ability to solve a wide range of computational problems
Solution Approach 1:
The patent implements dynamic reconfigurability by allowing the quantum computing device to change its interaction topology between qubits during operation. The system can dynamically adjust which qubits interact with each other, transforming from a static hard-wired structure to a dynamic reconfigurable architecture. This enables the same physical device to adapt to different computational problems by reconfiguring its connectivity pattern, thereby achieving both ease of manufacture (fixed physical structure) and adaptability (reconfigurable logic).
Solution Approach 2:
The patent changes the interaction parameters between qubits by introducing adjustable coupling mechanisms. Instead of fixed hard-wired connections, the system allows the interaction strength and connectivity between qubits to be modified through control parameters such as coupling coefficients and interaction ranges. This parameter change enables the device to scale and be programmed for different problems while maintaining a manageable physical structure.
2Adaptability or versatility
If quantum computing devices allow arbitrary interactions between all qubits, then full programmability is achieved, but the device complexity increases significantly, making it difficult to scale
Solution Approach 1:
The patent segments the quantum computing device into multiple groups or layers of qubits with different interaction capabilities. Instead of allowing arbitrary interactions between all qubits simultaneously, the system divides qubits into subsets that can interact within their group, while inter-group interactions are controlled through specific mechanisms. This segmentation reduces the overall complexity by breaking down the full N-qubit interaction problem into smaller, more manageable sub-problems, enabling scalability while maintaining programmability.
Solution Approach 2:
The patent implements local quality by assigning different interaction properties to different regions or groups of qubits. Rather than uniform arbitrary interactions across the entire system, each qubit group has specific interaction characteristics optimized for particular computational tasks. This local differentiation reduces global complexity while maintaining the ability to program diverse computational problems by activating appropriate local interaction patterns.
3Productivity
If quantum computing devices are designed for specific computational problems, then they can achieve high performance for those problems, but they cannot efficiently solve a wide range of different computational problems
Solution Approach 1:
The patent designs the quantum computing device with universal functionality by incorporating reconfigurable interaction mechanisms that can be programmed for different computational problems. The system uses a standardized qubit architecture with adjustable coupling parameters, allowing the same physical device to function as a specialized quantum annealer for one problem and be reprogrammed as a different type of quantum computer for another problem. This multi-functionality maintains high computation speed across diverse problem types while eliminating the need for problem-specific hardware design.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This approach allows for faster computation of solutions to complex problems, such as NP-hard problems, by enabling flexible encoding and interaction configurations, overcoming the limitations of existing quantum computing devices.
Implementation Method 1
evolving the quantum system from an initial quantum state towards a ground state of a final Hamiltonian
Data Source
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AI summary
A method of computing a solution to a computational problem using a quantum system comprising a plurality of qubits is provided. The method includes encoding the computational problem into a problem Hamiltonian of the quantum system, wherein the problem Hamiltonian is a single-body Hamiltonian including a plurality of adjustable parameters, and wherein the encoding includes determining, from the computational problem, a problem-encoding configuration for the plurality of adjustable parameters. The method further includes evolving the quantum system from an initial quantum state towards a ground state of a final Hamiltonian of the quantum system, wherein the final Hamiltonian is the sum of the problem Hamiltonian and a short-range Hamiltonian, wherein the plurality of adjustable parameters of the problem Hamiltonian are in the problem-encoding configuration and wherein the short-range Hamiltonian is a d-body Hamiltonian, wherein d is independent of the computational problem. The method further includes measuring at least a portion of the plurality of qubits to obtain a read-out of the quantum system. The method further includes determining a solution to the computational problem from the read-out.