Quanton representation for emulating quantum-like computation on classical processors

• US 10,452,989 B2
• Filed: 05/05/2016
• Issued: 10/22/2019
• Est. Priority Date: 05/05/2015
• Status: Active Grant

• Current Assignee: QlikTech International AB (Qlik Technologies)
• Original Assignee: Kyndi, Inc
• Inventors: Majumdar, Arun
• Primary Examiner(s): Starks, Wilbert L
• Application Number:
  US15/147,751
• Publication Number:
  US 20160328253A1
• Time in Patent Office: 1,265 Days
• Field of Search: None
• CPC Class Codes: G06F 9/45504 Abstract machines for programme code execution, e.g. Java virtual machine [JVM], interpreters, emulators
• CPC Class Codes: G06N 10/00 Quantum computing, i.e. information processing based on quantum-mechanical phenomena
• CPC Class Codes: G06N 3/126 Evolutionary algorithms, e.g. genetic algorithms or genetic programming
• CPC Class Codes: G06N 7/01 Probabilistic graphical models, e.g. probabilistic networks
• CPC Class Codes: G06N 99/00 Subject matter not provided for in other groups of this subclass

Abstract

The Quanton virtual machine approximates solutions to NP-Hard problems in factorial spaces in polynomial time. The data representation and methods emulate quantum computing on classical hardware but also implement quantum computing if run on quantum hardware. The Quanton uses permutations indexed by Lehmer codes and permutation-operators to represent quantum gates and operations. A generating function embeds the indexes into a geometric object for efficient compressed representation. A nonlinear directional probability distribution is embedded to the manifold and at the tangent space to each index point is also a linear probability distribution. Simple vector operations on the distributions correspond to quantum gate operations. The Quanton provides features of quantum computing: superpositioning, quantization and entanglement surrogates. Populations of Quantons are evolved as local evolving gate operations solving problems or as solution candidates in an Estimation of Distribution algorithm. The Quanton representation and methods are fully parallel on any hardware.

Claims

1. A method of emulating an evolution of a quantum system, the method being performed by circuitry, the method comprising:

storing in memory a Quanton virtual machine data structure (QVM) by configuring in the memory a permutation data structure representing a quantum state, configuring in the memory a second data structure representing an evolution operator of the quantum state as directional displacements on a manifold, and configuring in the memory a relational table relating permutations to lattice points on the manifold;

receiving training data representing a plurality of input quantum states respectively associated with a plurality of output quantum states of the plurality of input quantum states after having evolved according to the quantum system;

training, using the received training data, the QVM by

initializing a distribution of the QVM, and

iteratively updating the distribution of the QVM based on results of a fitness function applied to randomly selected QVMs to generate a trained QVM representing a convergence of the distribution of the QVM, wherein

the results of the fitness function applied to a QVM of the randomly selected QVMs include a distance measure between respective states of the output quantum states and corresponding states of the plurality of input quantum states after having applied the QVM of the randomly selected QVMs; and

emulating the quantum system by applying the trained QVM to a quantum input representing an initial state of the quantum system and thereby generating, in polynomial time, a quantum output representing an initial state of the quantum system and corresponding to the quantum input evolved according to the quantum system, wherein

the permutation data structure is an array representing a permutation matrix, and

each of the randomly selected QVMs are determined using respective random numbers generated by the circuitry according to the distribution of the QVM.

2. The method according to claim 1, further comprising:

defining a mapping of quantum states to permutations, wherein

the configuring the QVM in the memory further includes,

the relational table being configured in the memory to relate the permutations to the lattice points that are a countable number of lattice points on the manifold, the lattice points forming vertices of a Birkhoff polytope,

the second data structure being configured in the memory as first probability density, which is a directional probability density on the manifold, and

a second probability density representing a distribution of quantum states, which is a linear probability density in a tangent space, the tangent space being one of a plurality of tangent spaces tangent to the manifold at the lattice points and forming a tessellation the manifold,

the training the QVM by the iteratively updating of the distribution of the QVM further includes, repeating in an iteratively loop until stopping criteria are reached,

determining, as the randomly selected QVMs, samples of QVMs randomly selected from the distribution of the QVM using the random numbers generated by the circuitry,

obtaining the results of the fitness function applied to the randomly selected QVMs by calculating respective fitness values of the samples of QVMs by applying a fitness function to each respective sample of the samples of QVMs,

selecting a fit subset of the samples of QVMs corresponding to fitness values within a range,

updating the distribution of the QVM according to statistical properties of the fit subset of the samples of the QVMs, wherein

the trained QVM is a QVM of the samples of the QVMs that has a greatest fitness value relative to all other of the samples of the QVMs when the stopping criteria are reached.

3. A non-transitory computer readable storage medium including executable instruction, wherein the instructions, when executed by circuitry, cause the circuitry to perform the method according to claim 2.

4. The method according to claim 2, wherein the configuring of the QVM in the memory further includes

the manifold including that the lattice points are arranged as a lattice forming a polytope, wherein each lattice point corresponds to a respective vertex of the polytope, and the lattice points are indexed by respective numbers of a numerical sequence, the respective numbers being associated with the respective permutations.

5. The method according to claim 4, wherein the configuring of the QVM in the memory according to by the polytope formed by the lattice points being one of a Permutohedron, a sorting orbitope, a Birkhoff polytope, or a combinatorial polytope.

6. The method according to claim 4, wherein the configuring of the QVM in the memory according to

the manifold being one of a hypertorus, a hypersphere, a hypercube, a Calabu-Yau manifold, an n-simplex, a 2-polygon, and a zonotope,

a lattice-type determining an arrangement of the lattice points in the lattice being one of a Bravais lattice, a Fibonacci lattice, a Fermat lattice, a binary lattice, a simplicial polytopic number lattice, and a figurate numbers lattice,

a probability distribution type of the first probability density is one of a non-linear probability density function, a von Mises probability density, a von Mises Fisher probability density, a Watson probability density, a Mallows probability density, a Gaussian probability density, a Kent probability density, a Bivariate von Mises probability density, a uniform probability density, a Veterbi probability density, a Bayes probability density, a Markov-chain probability density, a Markov probability density, a mixture model, and a Dirichlet Process mixture model, and

a probability distribution type of the second probability density is one of a linear probability density function, a Gaussian distribution, a Poisson distribution, a Wigner distribution, a Fisher distribution, a hypergeometric distribution, a mixture model probability density, and any combination thereof.

7. The method according to claim 2, wherein the configuring of the QVM in the memory further includes

the manifold being a finite geometric surface, wherein the manifold represents nonlinear characteristics of the-evolution of the quantum system, and

the tangent spaces being a tessellation of the manifold, wherein the tangent spaces represent a local linear approximation of the manifold.

8. The method according to claim 2, further comprising

defining a transition model, which evolves the second probability density according to transitions represented by the first probability density, wherein the first probability density represents transitions along the manifold among the lattice points, and

mapping the second probability to/from a Euclidean space of the tangent space from/to a space of manifold, using a logarithmic map.

9. The method according to claim 2, wherein the configuring of the QVM in the memory according to

identifying lattice points as respectively corresponding to permutations,

identifying lattices points as respectively corresponding to quantum states, and

identifying transitions among lattices points to correspond with quantum gates.

10. The method according to claim 2, wherein

the receiving of the training data further includes mapping the training data to the manifold and tangent spaces to represent input data and output data as permutations corresponding to the lattice points, and

the updating of the QVM includes that the stopping criteria include a measure of how closely permutation transitions represented by the first probability density approximate translations from the permutations of the input data to the permutations of the output data.

11. The method according to claim 2, further comprising

defining operational semantics, wherein respective states of the quantum system correspond to respective permutations of the lattice points and respective gate operations on the states of the quantum system correspond to transitions among the lattice points and/or permutation operations on the permutations of the lattice points.

12. The method according to claim 11, wherein the defining operational semantics further includes

the permutation operations include at least one of a swap operation, an addition operation, or any combination thereof, and

the gate operation is one of a reversible logic gate, a Toffoli gate, a C-NOT gate, a Peres gate, a Fredkin gate, a swap gate, or any combination thereof.

13. The method according to claim 2, wherein the configuring of the QVM in the memory further includes

the permutations being represented as permutrices, each permutrix including a respective permutant and a respective permuton, wherein the respective permutants represent observable states and the respective permutons represent hidden states or imaginary states.

14. The method according to claim 2, wherein the configuring of the QVM in the memory according to

the manifold being a hypersphere,

the countable number of lattice points being a Landau number greater than a dimension of a state space of the quantum system,

the lattice points being arranged in one of a Fibonacci lattice and a Fermat lattice, and

the lattice points being labeled by the respective permutations, which, when the lattice points are arranged in the Fibonacci lattice, are represented by Lehmer codes determined using a Binet Formula.

15. The method according to claim 2, wherein the configuring of the QVM in the memory according to

labelling the lattice points according to a combinatorial arrangement by

determining the respective permutations at the lattice points according to a integer sequence, which relates the respective permutations to successors and predecessors,

determining a permutation transition operator between permutations according to the successors and the predecessors, and

associating the permutation transition operator to an operational semantics, wherein the operational semantics include at least one of a gate operation, a quantum circuit, a permutation operation, a swap operation, and an operation corresponding to a Hamiltonian path of the permutations.

16. The method according to claim 2, wherein the configuring of the QVM in the memory according to labelling the lattice points by the respective permutations according to a sequence pattern that is one of a general quantum permutation circuit, a blockwise pattern, an alternating pattern, a zigzag pattern, and up/down pattern, a Landau Number pattern, and a Baxter permutation pattern.

17. The method according to claim 2, wherein the defining the mapping of the quantum states to permutations includes

a list-permutation bijection between a list and a permutation, the list-permutation bijection being a reversible mapping representing a one-to-one correspondence between a set of the list, which is a sequence of natural numbers, and a set of the permutation, which is a permutation of natural numbers,

a list-natural number bijection between the list and a natural number,

a list-set bijection between the list and a set, and

a list-multiset bijection between the list and a multiset.

18. The method according to claim 2, wherein the receiving the training data includes

representing training data as training-data probability distributions on the tangent spaces, and the training-data probability distributions are discretized to discrete values corresponding to the respective nearest lattice points, and

using a logarithmic map to map distributions between the manifold and the tangent spaces, wherein the distributions correspond to the second probability density.

19. The method according to claim 2, wherein the updating the QVM is further performed by

the determining of the samples of QVMs from the distribution of the QVM is performed by

the determining of the samples of QVMs such that each sample of QVMs has a unique arrangement of the permutations labeling the respective lattice points,

the calculating of the respective fitness values of the samples of QVMs is performed by

obtaining, based on training data, respective input distributions and output distributions of the permutations, the arrangement of the input distributions and the output distributions among the lattice points depending on the unique arrangement of the permutations labeling the lattice points of the sample,

operating on the input distributions using a transition model relating input lattice points to output lattice points to generate transitioned input distributions, and

applying a fitness function to the transitioned input distributions and the output distributions to generate the respective fitness values of the samples, and

the updating of the distribution of the QVM includes adjusting the distribution of the QVM according to a likelihood that, among the unique arrangements of the permutations labeling of the lattice points within the fit subset, a lattice point of the lattice points is labeled by a permutation of the permutations.

20. The method according to claim 19, wherein the updating the QVM is further performed by the calculating of the respective fitness values using the permutational distance function that is one of Levenstein Edit distance, a Hamming distance, and number of permutation operations separating lattice points, and a Kendall tau distance.

21. The method according to claim 2, wherein the updating the QVM is further performed by

the determining of the samples of QVMs from the distribution of the QVM is performed by

the determining the samples of QVMs such that each sample of QVMs has a unique transition model of directional transitions among the lattice points,

the calculating of the respective fitness values for a sample of the samples of QVMs is performed by

obtaining, based on training data, respective input distributions and output distributions of the lattice points,

operating on the input distributions using the corresponding unique transition model of the sample to generate transitioned input distributions, and

applying a fitness function to the transitioned input distributions and the output distributions to generate the respective fitness values-of the samples, and

the updating of the distribution of the QVM includes adjusting the distribution of the QVM according to a likelihood that, among the unique transition models of fit subset, a lattice point of the input distribution transitions to a lattice point of the output distribution.

22. The method according to claim 21, wherein the wherein the training the QVM includes-using the logarithmic map to map the distributions between the manifold and the tangent spaces, wherein the logarithmic map is one of a Riemannian logarithmic map, a Hilbert logarithmic map, and an inverse function between respective a linear space and a non-linear space.

23. The method according to claim 2, wherein

the emulating of the evolution of the quantum system using the QVM by

receiving an input,

mapping the input to an input Quanton state, and

applying the first probability density of the learned QVM to the input Quanton state to generate an output Quanton state.

24. The method according to claim 23, wherein the emulating of the evolution of the quantum system is further performed by

the applying of the first probability density to the learned QVM further includes that the first probability density is learned by the updating of the QVM.

25. The method according to claim 2, wherein the updating the QVM is further performed using a recursive evolution and estimation method by

each permutation of the permutations comprising a permuton and a permutrix, and

the recursive evolution and estimation method including the steps of

defining an observation model and a transition model, wherein the transition model includes a mixed conditional probability distribution based on the first probability density and expressed using at least one of a mean and a variance of a von Mises-Fisher distribution, a mean and a variance of a Gaussian distribution, a mixture model of probability distributions, or any combination thereof,

generating a posterior to predict an evolution of the second probability density using a Forward algorithm by performing a prediction/roll up step and a conditioning step,

the determining samples of QVMs further include generating observations of the second probability density by projecting the posterior onto the manifold to generate a mean vector, and

the selecting a fit subset of the samples of QVMs and the updating of the distribution of the QVM include generating random draws using the mean vector, and modifying the second probability density from a partial observation based on the random draws.

26. The method according to claim 2, wherein the configuring of the QVM in the memory according to labeling the lattice points by the respective permutations according to a sequence pattern using Landau numbering to design the permutations, wherein an operation between the permutations representing transitions between the lattice points corresponds to at least one of an addition, subtraction, multiplication, and division of a Turing computational model.

27. The method according to claim 2, wherein the wherein the emulating the quantum system includes mapping the quantum input to a second probability density of the trained QVM using one of a Bayesian model, a Kalman model, a model applying time dependent density functionals, a model applying direction dependent density functionals, a quantum model, or any combination thereof.

28. The method according to claim 2, wherein the quantum system is a topological quantum computing system and the evolution of the quantum system is a braiding operation of-topological quantum computing.

29. A non-transitory computer readable storage medium including executable instruction, wherein the instructions, when executed by circuitry, cause the circuitry to perform the method according to claim 1.

30. An apparatus comprising:

a memory storing a Quanton virtual machine data structure (QVM), the QVM including

a permutation data structure representing a quantum state,

a second data structure representing an evolution operator of the quantum state as directional displacements on a manifold, and

a relational table relating the permutation data structure to lattice points embedded in the manifold corresponding to the second data structure; and

circuitry configured to

generate random number according a predefined distribution,

receive training data representing a plurality of input quantum states respectively associated with a plurality of output quantum states of the plurality of input quantum states after having evolved according to the quantum system, and

train, using the received training data, the QVM by

initializing a distribution of the QVM, and

iteratively updating the distribution of the QVM based on results of a fitness function applied to randomly selected QVMs to generate a trained QVM representing a convergence of the distribution of the QVM, wherein

the results of the fitness function applied to a QVM of the randomly selected QVMs include a distance measure between respective states of the output quantum states and corresponding states of the plurality of input quantum states after having applied the QVM of the randomly selected QVMs, and

emulate the quantum system by applying the trained QVM to a quantum input and thereby generating, in polynomial time, a quantum output corresponding to the quantum input evolved according to the quantum system, wherein

the permutation data structure is an array representing a permutation matrix, and

each of the randomly selected QVMs are determined using respective random numbers generated by the circuitry according to the distribution of the QVM.

31. The apparatus according to claim 30, wherein

the memory is further configured according to

the relational table relates the permutations to the lattice points that are a countable number of lattice points on the manifold, the lattice points forming vertices of a polytope,

the second data structure is first probability density, which is a directional probability density on the manifold, and

a second probability density, which represents a distribution of quantum states, is a linear probability density in a tangent space, the tangent space being one of a plurality of tangent spaces tangent to the manifold at the lattice points and forming a tessellation the manifold,

the circuitry is further configured to

define a mapping of quantum states to permutations, and

perform the training the QVM by iteratively updating of the distribution of the QVM further by repeating, in an iteratively loop until stopping criteria are reached, the steps of

determining, as the randomly selected QVMs, samples of QVMs randomly selected from the distribution of the QVM using the random numbers generated by the circuitry,

obtaining the results of the fitness function applied to the randomly selected QVMs by calculating respective fitness values of the samples of QVMs by applying a fitness function to each respective sample of the samples of QVMs,

selecting a fit subset of the samples of QVMs corresponding to fitness values within a range,

updating the distribution of the QVM according to statistical properties of the fit subset of the samples of the QVMs, wherein

the trained QVM is a QVM of the samples of the QVMs that has a greatest fitness value relative to all other of the samples of the QVMs when the stopping criteria are reached.

32. The apparatus according to claim 30, wherein the memory circuitry is further configured according to

the manifold is a finite geometric surface, wherein the manifold represents nonlinear characteristics of the evolution of the quantum system, and

the tangent spaces is a tessellation of the manifold, wherein the tangent spaces represent a local linear approximation of the manifold.

33. The apparatus according to claim 30, wherein the circuitry is further configured to train the QVM by

defining a transition model, which evolves the second probability density according to transitions represented by the first probability density, wherein the first probability density represents transitions along the manifold among the lattice points, and

mapping the second probability to/from a Euclidean space of the tangent space from/to a space of manifold, using a logarithmic map.

34. The apparatus according to claim 30, wherein the memory circuitry is further configured according to

the lattice points respectively corresponding to permutations,

the lattices points as respectively corresponding to quantum states, and

transitions among lattices points corresponding with quantum gates.

35. The apparatus according to claim 30, wherein the circuitry is further configured to train the QVM by

mapping the training data to the manifold and tangent spaces to represent input data and output data as permutations corresponding to the lattice points, and

update the QVM using the stopping criteria that further includes a measure of how closely permutation transitions represented by the first probability density approximate translations from the permutations of the input data to the permutations of the output data.

36. The apparatus according to claim 30, wherein the circuitry is further configured to

define operational semantics, wherein respective states of the quantum system correspond to respective permutations of the lattice points and respective gate operations on the states of the quantum system correspond to transitions among the lattice points and/or permutation operations on the permutations of the lattice points.

37. The apparatus according to claim 36, wherein the circuitry is further configured to defining the operational semantics by

the permutation operations include at least one of a swap operation, an addition operation, or a combination thereof, and

the gate operation is one of a reversible logic gate, a Toffoli gate, a C-NOT gate, a Peres gate, a Fredkin gate, a swap gate, or any combination thereof.

38. The apparatus according to claim 30, wherein the memory circuitry is further configured according to

representing the permutations as respective permutrices, each permutrix including a respective permutant and a respective permuton, wherein the respective permutants represent observable states and the respective permutons represent hidden states or imaginary states.

39. The apparatus according to claim 30, wherein the memory circuitry is further configured according to-the manifold is a hypersphere,

the countable number of lattice points is a Landau number greater than a dimension of a state space of the quantum system,

the lattice points are arranged in one of a Fibonacci lattice and a Fermat lattice, and

the lattice points are labeled by the respective permutations, which, when the lattice points are arranged in the Fibonacci lattice, are represented by Lehmer codes determined using a Binet Formula.

40. The apparatus according to claim 30, wherein the memory circuitry is further configured according to-the manifold including that the lattice points are arranged as a lattice forming a polytope, wherein each lattice point corresponds to a respective vertex of the polytope, and the lattice points are indexed by respective numbers of a numerical sequence, the respective numbers being associated with the respective permutations.

41. The apparatus according to claim 40, wherein the memory circuitry is further configured according to

the polytope formed by the lattice points is one of a Permutohedron, a sorting orbitope, a Birkhoff polytope, or a combinatorial polytope.

42. The apparatus according to claim 40, wherein the memory circuitry is further configured according to

the manifold being one of a hypertorus, a hypersphere, a hypercube, a Calabu-Yau manifold, an n-simplex, a 2-polygon, and a zonotope,

a lattice-type determining an arrangement of the lattice points in the lattice is one of a Bravais lattice, a Fibonacci lattice, a Fermat lattice, a binary lattice, a simplicial polytopic number lattice, and a figurate numbers lattice,

a probability distribution type of the first probability density is one of a non-linear probability density function, a von Mises probability density, a von Mises Fisher probability density, a Watson probability density, a Mallows probability density, a Gaussian probability density, a Kent probability density, a Bivariate von Mises probability density, a uniform probability density, a Veterbi probability density, a Bayes probability density, a Markov-chain probability density, a Markov probability density, a mixture model, and a Dirichlet Process mixture model, and

a probability distribution type of the second probability density is one of a linear probability density function, a Gaussian distribution, a Poisson distribution, a Wigner distribution, a Fisher distribution, a hypergeometric distribution, a mixture model probability density, and any combination thereof.

43. The apparatus according to claim 30, wherein the memory circuitry is further configured according to-the lattice points being labeled according to a combinatorial arrangement by

determining the respective permutations at the lattice points according to a integer sequence, which relates the respective permutations to successors and predecessors,

determining a permutation transition operator between permutations according to the successors and the predecessors, and

associating the permutation transition operator to an operational semantics, wherein the operational semantics include at least one of a gate operation, a quantum circuit, a permutation operation, a swap operation, and an operation corresponding to a Hamiltonian path of the permutations.

44. The apparatus according to claim 30, wherein the memory circuitry is further configured according to

the lattice points being labeled by the respective permutations according to a sequence pattern that is one of a general quantum permutation circuit, a blockwise pattern, an alternating pattern, a zigzag pattern, and up/down pattern, a Landau Number pattern, and a Baxter permutation pattern.

45. The apparatus according to claim 30, wherein the defining the mapping of the quantum states to permutations includes

a list-permutation bijection between a list and a permutation, the list-permutation bijection being a reversible mapping representing a one-to-one correspondence between a set of the list, which is a sequence of natural numbers, and a set of the permutation, which is a permutation of natural numbers,

a list-natural number bijection between the list and a natural number,

a list-set bijection between the list and a set, and

a list-multiset bijection between the list and a multiset.

46. The apparatus according to claim 30, wherein the circuitry is further configured to train the QVM by

representing training data as training-data probability distributions on the tangent spaces, and the training-data probability distributions are discretized to discrete values corresponding to the respective nearest lattice points, and

using a logarithmic map to map distributions between the manifold and the tangent spaces, wherein the distributions correspond to the second probability density.

47. The apparatus according to claim 46, wherein the circuitry is further configured to train the QVM by

using the logarithmic map to map the distributions between the manifold and the tangent spaces, wherein the logarithmic map is one of a Riemannian logarithmic map, a Hilbert logarithmic map, and an inverse function between respective a linear space and a non-linear space.

48. The apparatus according to claim 30, wherein the circuitry is further configured to update the QVM by

the determining of the samples of QVMs from the distribution of the QVM further includes that each sample of the samples of QVMs has a unique arrangement of the permutations labeling the respective lattice points,

the calculating of the respective fitness values-further includes

obtaining, based on training data, respective input distributions and output distributions of the permutations, wherein the arrangement of the input distributions and the output distributions among the lattice points depend on the unique arrangement of the permutations labeling the lattice points of the sample,

operating on the input distributions using a transition model relating input lattice points to output lattice points to generate transitioned input distributions, and

applying a fitness function to the transitioned input distributions and the output distributions to generate the respective fitness values-of the samples, and

the updating of the distribution of the QVM further includes adjusting the distribution of the QVM according to a likelihood that, among the unique arrangements of the permutations labeling of the lattice points within the fit subset, a lattice point of the lattice points is labeled by a permutation of the permutations.

49. The apparatus according to claim 48, wherein the circuitry is further configured to update the QVM by calculating the respective fitness values using a distance function that is one of Levenstein Edit distance, a Hamming distance, and number of permutation operations separating lattice points, and a Kendall tau distance.

50. The apparatus according to claim 30, wherein the circuitry is further configured to update the QVM by

the determining of the samples of QVMs from the distribution of the QVM further includes that each sample of the samples of QVMs has a unique transition model of directional transitions among the lattice points,

the calculating of the respective fitness values-further includes

obtaining, based on training data, respective input distributions and output distributions of the lattice points,

operating on the input distributions using the corresponding unique transition model of the sample to generate transitioned input distributions, and

applying a fitness function to the transitioned input distributions and the output distributions to generate the respective fitness values-of the samples, and

the updating of the distribution of the QVM further includes adjusting the distribution of the QVM according to a likelihood that, among the unique transition models of fit subset, a lattice point of the input distribution transitions to a lattice point of the output distribution.

51. The apparatus according to claim 30, wherein the circuitry is further configured to

emulate the evolution of the quantum system using the QVM by

receiving an input,

mapping the input to an input Quanton state, and

applying the first probability density of the learned QVM to the input Quanton state to generate an output Quanton state.

52. The apparatus according to claim 51, wherein the circuitry is further configured to emulate the evolution of the quantum system using the QVM by

applying the first probability density to the learned QVM, wherein the first probability density is learned by the updating of the QVM.

53. The apparatus according to claim 30, wherein the circuitry is further configured to update the QVM using a recursive evolution and estimation method by

each permutation of the permutations comprising a permuton and a permutrix, and

the recursive evolution and estimation method including the steps of

defining an observation model and a transition model, wherein the transition model includes a mixed conditional probability distribution based on the first probability density and expressed using at least one of a mean and a variance of a von Mises-Fisher distribution, a mean and a variance of a Gaussian distribution, a mixture model of probability distributions, or any combination thereof,

generating a posterior to predict an evolution of the second probability density using a Forward algorithm by performing a prediction/roll up step and a conditioning step,

the determining samples of QVMs further include generating observations of the second probability density by projecting the posterior onto the manifold to generate a mean vector, and

the selecting a fit subset of the samples of QVMs and the updating of the distribution of the QVM include generating random draws using the mean vector, and modifying the second probability density from a partial observation based on the random draws.

54. The apparatus according to claim 30, wherein the memory circuitry is further configured according to

labeling the lattice points by the respective permutations according to a sequence pattern using Landau numbering to design the permutations, wherein an operation between the permutations representing transitions between the lattice points corresponds to at least one of an addition, subtraction, multiplication, and division of a Turing computational model.

55. The apparatus according to claim 30, wherein the circuitry is further configured to emulating the quantum system by

the second probability density is generated from training data using one of a Bayesian model, a Kalman model, a model applying time dependent density functionals, a model applying direction dependent density functionals, a quantum model, or any combination thereof.

56. The apparatus according to claim 30, wherein the quantum system is topological quantum computing system and the evolution of the quantum system is a braiding operation of-topological quantum computing.