QUANTUM COMPUTING SYSTEM AND METHOD

• US 20200134503A1
• Filed: 10/28/2019
• Published: 04/30/2020
• Est. Priority Date: 10/31/2018
• Status: Abandoned Application

• Current Assignee: Black Brane Systems Inc.
• Original Assignee: Black Brane Systems Inc.
• Inventors: Lupton, Colin J.E.
• Application Number:
  US16/665,180
• Publication Number:
  US 20200134503A1
• Time in Patent Office: Days
• CPC Class Codes: G06N 10/00 Quantum computing, i.e. information processing based on quantum-mechanical phenomena
• CPC Class Codes: G06N 20/00 Machine learning
• CPC Class Codes: G06N 20/10 using kernel methods, e.g. support vector machines [SVM]
• CPC Class Codes: G06N 5/01 Dynamic search techniques; Heuristics; Dynamic trees; Branch-and-bound
• CPC Class Codes: G06N 7/01 Probabilistic graphical models, e.g. probabilistic networks
• CPC Class Codes: G06N 7/08 using chaos models or non-linear system models

Abstract

In various embodiments, A quantum computing system comprises at least one classical processor configured by operational instructions stored in a classical memory to perform operations including: generating a qubit encoding from an input, wherein the qubit encoding indicates a sublattice of a projective Riemann-hypersphere that represents a plurality of qubits; and generating a quantum solution based on quantum calculations wherein the quantum calculations include a decomposition of the sublattice performed via a plurality of iterations.

Claims

1. A quantum computing system comprising:

at least one classical processor configured by operational instructions stored in a classical memory to perform operations including;

generate a qubit encoding from an input, wherein the qubit encoding indicates a sublattice of a projective Riemann-hypersphere that represents a plurality of qubits; and

generate a quantum solution based on quantum calculations wherein the quantum calculations include a decomposition of the sublattice performed via a plurality of iterations.

2. The quantum computing system of claim 1, wherein the quantum calculations are further based on quantum chaos generated in accordance with a quantum chaos knowledgebase.

3. The quantum computing system of claim 2, wherein the quantum chaos knowledgebase includes a machine learning engine.

4. The quantum computing system of claim 3, wherein the machine learning engine is trained based on a reflective multilayer bootstrapping.

5. The quantum computing system of claim 4, wherein the reflective multilayer bootstrapping includes at least one first layer of training based on a quantum device emulation performed via a classical computer and at least one second layer of training performed via a quantum computer.

6. The quantum computing system of claim 2, wherein the quantum chaos includes type-III quantum chaos.

7. The quantum computing system of claim 2, wherein the quantum chaos knowledgebase represents the quantum chaos in the projective Riemann-hypersphere that represents the plurality of qubits.

8. The quantum computing system of claim 2, wherein the quantum calculations superimpose the quantum chaos on the sublattice.

9. The quantum computing system of claim 1, wherein the classical memory further stores a search database that includes a plurality of search techniques, wherein the classical processor generates a search selection that indicates one of the plurality of search techniques and wherein the plurality of iterations are performed in accordance with the search selection.

10. The quantum computing system of claim 9, wherein the classical processor evaluates a result of each iteration of the plurality of iterations by:

determining an estimated distance to a universal optimum;

an estimated cost to reach the universal optimum; and

terminating the plurality of iterations when the estimated cost to reach the universal optimum exceeds a cost threshold.

11. The quantum computing system of claim 10, wherein, when the estimated cost to reach the universal optimum exceeds the cost threshold, the classical processor terminates the plurality of iterations by calling a true-random quantum nondeterministic choice operator.

12. The quantum computing system of claim 1, wherein the qubit encoding is generated by projecting a quantum matrix calculation to the sublattice of the projective Riemann-hypersphere.

13. A method comprising:

generating, via a classical processor, a qubit encoding from an input, wherein the qubit encoding indicates a sublattice of a projective Riemann-hypersphere that represents a plurality of qubits; and

generating, via the classical processor, a quantum solution based on quantum calculations wherein the quantum calculations include a decomposition of the sublattice performed via a plurality of iterations.

14. The method of claim 13, wherein the quantum calculations are further based on quantum chaos generated in accordance with a quantum chaos knowledgebase.

15. The method of claim 14, wherein quantum chaos knowledgebase includes a machine learning engine trained via first layer of training based on a quantum device emulation performed via a classical computer and at least one second layer of training performed via a quantum computer.

16. The method of claim 14, wherein the quantum chaos includes type-III quantum chaos.

17. The method of claim 14, wherein the quantum chaos knowledgebase represents the quantum chaos in the projective Riemann-hypersphere that represents the plurality of qubits.

18. The method of claim 14, wherein the quantum calculations superimpose the quantum chaos on the sublattice.

19. The method of claim 13, wherein the plurality of iterations are performed in accordance with a search algorithm.

20. The method of claim 13, wherein the plurality of iterations are performed in accordance with a non-linear prediction model.