Efficient simulation system of quantum algorithm gates on classical computer based on fast algorithm

• US 20060224547A1
• Filed: 03/24/2005
• Published: 10/05/2006
• Est. Priority Date: 03/24/2005
• Status: Abandoned Application

• Current Assignee: Yamaha Hatsudoki Kabushiki Kaisha (Yamaha Corporation)
• Original Assignee: Yamaha Hatsudoki Kabushiki Kaisha (Yamaha Corporation)
• Inventors: Panfilov, Sergey A., Ulyanov, Sergey V.
• Application Number:
  US11/089,421
• Publication Number:
  US 20060224547A1
• Time in Patent Office: Days
• US Class Current: 706/62
• CPC Class Codes: B82Y 10/00 Nanotechnology for information processing, storage or transmission, e.g. quantum computing or single electron logic
• CPC Class Codes: G06N 10/00 Quantum computing, i.e. information processing based on quantum-mechanical phenomena
• CPC Class Codes: G06N 10/20 Models of quantum computing, e.g. quantum circuits or universal quantum computers
• CPC Class Codes: G06N 10/60 Quantum algorithms, e.g. based on quantum optimisation, quantum Fourier or Hadamard transforms

Abstract

An efficient simulation system of quantum algorithm gates for classical computers with a Von Neumann architecture is described. In one embodiment, a Quantum Algorithm is solved using an algorithmic-based approach, wherein matrix elements of the quantum gate are calculated on demand. In one embodiment, a problem-oriented approach to implementing Grover'"'"'s algorithm is provided with a termination condition determined by observation of Shannon minimum entropy. In one embodiment, a Quantum Control Algorithm is solved by using a reduced number of quantum operations.

Claims

1. A method for simulating a quantum algorithm on a classical computer, comprising:

applying a unitary matrix quantum gate G to an initial vector to produce a basis vector;

measuring said basis vector, wherein elements of said quantum gate G are computed on an as-needed basis;

repeating said steps of applying and measuring k times, where k is selected to minimize Shannon entropy of said basis vector; and

decoding said basis vectors, said decoding including translating said basis vectors into an output vector.

2. The method of claim 1, wherein said quantum gate G describes an entanglement-free quantum algorithm.

3. The method of claim 1, wherein said elements of said basis vector comprise one of two pre-computed values.

4. An intelligent control system comprising a quantum search algorithm configured to minimize Shannon entropy comprising:

a genetic optimizer configured to construct one or more local solutions using a fitness function configured to minimize a rate of entropy production of a controlled plant; and

a quantum search algorithm configured to search said local solutions to find a global solution using a gate G expressing a fitness function configured to minimize Shannon entropy, said gate G corresponding to an entanglement-free quantum algorithm for efficient simulation, and wherein elements of said gate G are computed on an as-needed basis.

5. The intelligent control system of claim 4, wherein said global solution comprises weights for a fuzzy neural network.

6. The intelligent control system of claim 4, wherein said fuzzy neural network is configured to train a fuzzy controller, said fuzzy controller configured to provide control weights to a proportional-integral-differential controller, said proportional-integral-differential controller configured to control said controlled plant.

7. The intelligent control system of claim 4, wherein said fitness function is step-constrained.

8. The intelligent control system of claim 4, wherein each element of a state vector of said quantum search algorithm comprises one of a finite number of pre-computed values.

9. The intelligent control system of claim 4, wherein said quantum search algorithm operates on pseudo-pure states.

10. A method for global optimization to improve a quality of a sub-optimal solution comprising the steps of:

selecting a first gate G corresponding to a first quantum process, modifying said first gate G into a second gate G corresponding to a second quantum process;

having pseudo-pure states;

applying a first transformation to an initial state to produce a coherent superposition of basis states;

applying a second transformation to said coherent superposition using a reversible transformation according to said second gate G to produce coherent output states;

applying a third transformation to said coherent output states to produce an interference of output states; and

selecting a global solution from said interference of output states.

11. The method of claim 10, wherein said first transformation is a Hadamard rotation.

12. The method of claim 10, wherein each of said basis states is represented using qubits.

13. The method of claim 10, wherein said second transformation is a solution to Shrodinger'"'"'s equation.

14. The method of claim 10, wherein said third transformation is a quantum fast Fourier transform.

15. The method of claim 10, wherein said pseudo-pure states are entanglement-free.

16. The method of claim 10, wherein said superposition of input states comprises a collection of local solutions to a global fitness function.

17. A method for terminating iterations of a quantum algorithm, comprising:

performing an interation of a quantum algorithm to produce a measurement vector;

computing a Shannon entropy of said measurement vector;

selecting a termination condition from at least one of;

a first local Shannon entropy minimum, a lowest Shannon entropy within a predefined number of iterations;

a predefined level of acceptable Shannon entropy; and

repeating said performing and computing until said termination condition is satisfied.

18. The method of claim 17, further comprising measuring a final output result.

19. The method of claim 17, further comprising measuring an output result at each iteration.

20. A method for intelligent control comprising a quantum search algorithm corresponding to a quantum system on entanglement-free states configured to minimize Shannon entropy comprising:

optimizing one or more local solutions using a fitness function configured to minimize a rate of entropy production of a controlled plant; and

searching, using a quantum search algorithm to search said local solutions to find a global solution using a fitness function to minimize Shannon entropy.

21. The method of claim 20, wherein said global solution comprises weights for a fuzzy neural network.

22. The method of claim 21 further comprising:

training a fuzzy controller, providing control weights from said fuzzy controller to a proportional-integral-differential controller, and using said proportional-integral-differential controller to control said controlled plant.

23. The method of claim 20, wherein said quantum search algorithm iterates until a first local Shannon entropy minimum is found.

24. The method of claim 20, wherein said quantum search algorithm iterates until a lowest Shannon entropy is found within a predefined number of iterations.

25. A global optimizer to improve a quality of a sub-optimal solution, said optimizer comprising of a computer software loaded into a memory, said software comprising:

a first module for applying a first transformation to an initial state to produce a coherent superposition of basis states;

a second module for applying a second transformation to said coherent superposition using a reversible transformation to produce one or more entanglement-free output states;

a third module for applying a third transformation to said one or more coherent output states to produce an interference of output states; and

a fourth module for selecting a global solution from said interference of output states.

26. The optimizer of claim 25, wherein said first transformation is a Hadamard rotation.

27. The optimizer of claim 25, wherein each of said basis states is represented using qubits.

28. The optimizer of claim 25, wherein said second transformation is based on a solution to Shrodinger'"'"'s equation.

29. The optimizer of claim 25, wherein said third transformation is a quantum fast Fourier transform.

30. The optimizer of claim 25, wherein said fourth module is configured to find a maximum probability.

31. The optimizer of claim 25, wherein said superposition of input states comprises a collection of local solutions to a global fitness function.

32. The optimizer of claim 25, wherein elements of a quantum gate are computed on an as-needed basis.

33. The optimizer of claim 25, wherein a state vector describing said output states is stored in a compressed format.