CN112819172A - Quantum computation simulation method and system based on table function - Google Patents
Quantum computation simulation method and system based on table function Download PDFInfo
- Publication number
- CN112819172A CN112819172A CN202110157917.2A CN202110157917A CN112819172A CN 112819172 A CN112819172 A CN 112819172A CN 202110157917 A CN202110157917 A CN 202110157917A CN 112819172 A CN112819172 A CN 112819172A
- Authority
- CN
- China
- Prior art keywords
- calculation
- quantum
- computation
- function
- characterization
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 238000000034 method Methods 0.000 title claims abstract description 45
- 238000004088 simulation Methods 0.000 title claims abstract description 37
- 238000004364 calculation method Methods 0.000 claims abstract description 155
- 239000013598 vector Substances 0.000 claims abstract description 61
- 239000002096 quantum dot Substances 0.000 claims abstract description 27
- 230000006870 function Effects 0.000 claims description 72
- 238000012512 characterization method Methods 0.000 claims description 63
- 238000004422 calculation algorithm Methods 0.000 claims description 31
- 238000013507 mapping Methods 0.000 claims description 28
- 238000003860 storage Methods 0.000 claims description 27
- 230000005428 wave function Effects 0.000 claims description 20
- 238000012545 processing Methods 0.000 claims description 18
- 230000001149 cognitive effect Effects 0.000 claims description 13
- 238000013500 data storage Methods 0.000 claims description 11
- 210000004556 brain Anatomy 0.000 claims description 7
- 230000003920 cognitive function Effects 0.000 claims description 6
- 238000010845 search algorithm Methods 0.000 claims description 6
- 238000007906 compression Methods 0.000 claims description 5
- 230000006835 compression Effects 0.000 claims description 5
- 230000003044 adaptive effect Effects 0.000 claims description 3
- 238000005094 computer simulation Methods 0.000 claims 2
- 230000008569 process Effects 0.000 abstract description 9
- 238000004891 communication Methods 0.000 abstract description 3
- 238000010586 diagram Methods 0.000 description 5
- 230000007246 mechanism Effects 0.000 description 5
- 230000003925 brain function Effects 0.000 description 4
- 230000000694 effects Effects 0.000 description 4
- 108010047303 von Willebrand Factor Proteins 0.000 description 4
- 239000000203 mixture Substances 0.000 description 3
- 238000005192 partition Methods 0.000 description 3
- 230000010354 integration Effects 0.000 description 2
- 230000033001 locomotion Effects 0.000 description 2
- 230000005389 magnetism Effects 0.000 description 2
- 239000000463 material Substances 0.000 description 2
- 238000012986 modification Methods 0.000 description 2
- 230000004048 modification Effects 0.000 description 2
- 102100032384 Ecto-ADP-ribosyltransferase 3 Human genes 0.000 description 1
- 101000589618 Homo sapiens Ecto-ADP-ribosyltransferase 3 Proteins 0.000 description 1
- 241000121629 Majorana Species 0.000 description 1
- 238000005481 NMR spectroscopy Methods 0.000 description 1
- 238000006243 chemical reaction Methods 0.000 description 1
- 230000019771 cognition Effects 0.000 description 1
- 239000003086 colorant Substances 0.000 description 1
- 239000002131 composite material Substances 0.000 description 1
- 238000004590 computer program Methods 0.000 description 1
- 238000012937 correction Methods 0.000 description 1
- 238000013144 data compression Methods 0.000 description 1
- 238000009826 distribution Methods 0.000 description 1
- 238000005040 ion trap Methods 0.000 description 1
- 238000004519 manufacturing process Methods 0.000 description 1
- 230000003287 optical effect Effects 0.000 description 1
- 239000002245 particle Substances 0.000 description 1
- 230000005610 quantum mechanics Effects 0.000 description 1
- 238000011160 research Methods 0.000 description 1
- 230000009466 transformation Effects 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N10/00—Quantum computing, i.e. information processing based on quantum-mechanical phenomena
Abstract
The invention relates to a quantum computation simulation method and system based on a table function. Simulating calculation vectors of a plurality of degrees of freedom by a plurality of computers, wherein one computer forms a calculation node, the calculation vector of each computer represents a quantum bit, and the calculation vector calculation of each computer adopts a table function calculation method for calculation; assembling and scheduling a plurality of computing nodes formed by the plurality of computers, and encoding the computing nodes into required computing vectors to simulate the characteristics of the qubits; and based on the calculation vector, realizing quantum parallel calculation according to a quantum informatics calculation theory. The quantum computation simulation method and system based on the table function simulate a plurality of qubits through a plurality of computers, then separate the computation and communication of the plurality of simulated qubits to form an ultra-high-speed quantum computation simulation system, simulate the quantum computation process through combining the quantum informatics process, and finally realize the real quantum computation efficiency.
Description
Technical Field
The invention relates to the field of quantum information calculation, in particular to a quantum calculation simulation method and system based on a table function.
Background
A Quantum computer (english: Quantum computer) is a device that performs general-purpose computation using Quantum logic. Unlike an electronic computer (or conventional computer), quantum computing uses quantum bits as objects for storing data, which use quantum algorithms to perform data manipulation. The majorana fermi anti-particle is a property of itself, and is perhaps a key to the realization of quantum computer manufacturing. Compared with the traditional general computer, the theoretical model of the computer is a general turing machine; the theory model of the general quantum computer is a general turing machine which is re-explained by the quantum mechanics law. From the point of view of computability, quantum computers can only solve the problems that can be solved by traditional computers, but from the point of view of computational efficiency, due to the existence of quantum mechanical superposition, certain known quantum algorithms are faster than the traditional general purpose computers in the problem processing speed.
Quantum computing is a hot spot in academia and industry at present due to its fast parallel computing capability far surpassing other computing modes, and a great amount of manpower and material resources are invested in various countries to research and develop quantum computers. The mainstream quantum computing method mainly adopts the methods of nuclear magnetic resonance, optical quantum, ion trap, superconducting quantum and the like to generate physical quanta, then codes and programs the physical quanta, and then expects to realize rapid computation.
However, these methods all adopt a physical quantum method, and due to the self-uncertainty of the physical quantum and the properties of quantum entanglement, the control and error correction of the qubit become difficult. Therefore, there is a long way to perform calculations with physical qubits and prepare a general purpose quantum computer.
Disclosure of Invention
The technical problem to be solved by the invention is to provide a quantum computation simulation method and system based on a table function, which are used for solving the technical problem that the actual quantum computation is difficult to realize by the existing physical quantum, so that the quantum computation efficiency cannot be achieved.
One technical scheme adopted by the invention for solving the technical problems is to construct a quantum computation simulation method based on a table function, which comprises the following steps:
s1, simulating calculation vectors of multiple degrees of freedom by adopting multiple computers, wherein one computer forms a calculation node, the calculation vector of each computer represents a qubit, and the calculation vector calculation of each computer adopts a table function calculation method to calculate;
s2, assembling and scheduling a plurality of computation nodes formed by a plurality of computers, and coding the computation nodes into required computation vectors to simulate the characteristics of qubits;
and S3, based on the calculation vector, according to the quantum informatics calculation theory, realizing quantum parallel calculation.
Further, in step S1, the computation vector of each computer simulates the quantum simulation state of the qubit, and the different quantum simulation states can be converted into each other.
Further, the table function calculation method includes: cognitive classification characterization, coding of characterization, classification calculation of characterization, storage of characterization, and generation and search matching calculation of an input and output function mapping relation table integrated with storage and calculation.
Further, the cognitive classification and characterization comprises establishing a knowledge characterization system corresponding to the cognitive function category of the human brain; the characterized codes comprise different data structures which are established according to different attribute characteristics of different characterization categories to form different codes of different characterization categories; the classification calculation of the representation comprises the steps of calculating and processing data of different representation types by adopting different calculation algorithms according to different representation types and codes; the storage of the characterization comprises the steps of carrying out compression storage on corresponding data based on a customized data storage model according to the characterization category and the coding and classification processing result, and supporting exponential-level quick access; the generation of the integrated input and output function mapping relation table comprises the steps of generating output information result values corresponding to different input information through off-line calculation according to the characterization category, the codes and the classification calculation algorithm, and forming an input and output function mapping relation table of corresponding results based on a unified table function template; and the searching and matching calculation comprises the steps of adopting a multi-stage mode searching algorithm when a calculation task exists, directly searching in an input space of the input and output function mapping relation table through an input variable value, and outputting a calculation result value according to an output true value corresponding to the variable meeting the matching.
Further, the data storage model comprises a one-dimensional infinite depth potential well model, the multi-stage mode search algorithm comprises an adaptive resonant network 3 multi-stage mode search algorithm, and the matching adopts a mode similarity calculation method.
Further, in the step S2, the plurality of computing nodes are connected for assembly and scheduling through a centerless fully-switched network based on a software-defined network, and the output results of the plurality of computers are composited through a designed composite wave function to form a quantum state superposition output.
Further, the complex wave function comprises the combination of quantum eigen-state wave functions of sine waves and cosine waves.
The invention solves the technical problem by adopting another technical scheme that a quantum computation simulation system based on a table function is constructed, and comprises a plurality of computers which are communicated with each other, wherein each computer forms a computation node, the computation vector of each computer represents a quantum bit, and the computation vector computation of each computer adopts a table function computation method to carry out computation; the computers simulate calculation vectors with multiple degrees of freedom, and a plurality of calculation nodes formed by the computers are assembled and scheduled and encoded into required calculation vectors to simulate the characteristics of qubits; and based on the calculation vector, realizing quantum parallel calculation according to a quantum informatics calculation theory.
Further, each of the computers includes:
and the table function calculation module is used for executing cognitive classification representation, coding of the representation, classification calculation of the representation, storage of the representation, and generation and search matching calculation of the input and output function mapping relation table integrated with the storage and calculation.
Further, the multiple computing nodes are connected through a centerless full-switching network based on a software defined network for assembly and scheduling, and the output results of the multiple computers are compounded through a designed complex wave function to form quantum state superposition output.
The quantum computation simulation method and system based on the table function simulate a plurality of qubits through a plurality of computers, then separate the computation and communication of the plurality of simulated qubits to form an ultra-high-speed quantum computation simulation system, simulate the quantum computation process through combining the quantum informatics process, and finally realize the real quantum computation efficiency.
Drawings
The invention will be further described with reference to the accompanying drawings and examples, in which:
FIG. 1 is a flow chart of a table function based quantum computation simulation method of a preferred embodiment of the present invention;
fig. 2 is a schematic diagram of information characterization based on brain cognition according to an embodiment of the present invention;
FIG. 3 is a schematic block diagram of a quantum computing simulation system based on table functions according to a first preferred embodiment of the present invention;
fig. 4 is a schematic block diagram of a quantum computing simulation system based on a table function according to a second preferred embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is described in further detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
The invention relates to a quantum computation simulation method based on a table function, which comprises the following steps: simulating calculation vectors with multiple degrees of freedom by adopting multiple computers, wherein one computer forms a calculation node, the calculation vector of each computer represents a quantum bit, and the calculation vector calculation of each computer adopts a table function calculation method for calculation; assembling and scheduling a plurality of computing nodes formed by the plurality of computers, and encoding the computing nodes into required computing vectors to simulate the characteristics of the qubits; and based on the calculation vector, realizing quantum parallel calculation according to a quantum informatics calculation theory. The quantum computation simulation method and system based on the table function simulate a plurality of qubits through a plurality of computers, then separate the computation and communication of the plurality of simulated qubits to form an ultra-high-speed quantum computation simulation system, simulate the quantum computation process through combining the quantum informatics process, and finally realize the real quantum computation efficiency.
Fig. 1 is a flowchart of a table function based quantum computation simulation method according to a preferred embodiment of the present invention. As shown in fig. 1, in step S1, a plurality of computers are used to simulate calculation vectors of a plurality of degrees of freedom, wherein one computer constitutes one calculation node, the calculation vector of each computer represents one qubit, and the calculation vector calculation of each computer is calculated by using a table function calculation method.
In the present invention, a plurality of von willebrand computers can be used to simulate a computation vector of multiple degrees of freedom, representing the states of multiple qubits by different dimensions of the computation vector. Any one of the computers is a computational node, which is equivalent to a computational vector of degrees of freedom, and can represent different values of a qubit, representing uncertain information that a qubit can represent.
For example, the calculation vector of any one computer is X ═ X1,x2,x3,…,xn]，n∈N+(ii) a Representing quantum analog states of a single computer representation, whichWherein X has an N-state, and when k is represented, k ∈ N+In this state, the k position of X is 1 and the other positions are 0. For example, when k is 3, the quantum simulation is represented as a 3-state vector, and X is [0,0,1,0, …,0](ii) a When k is 9, the quantum simulation is represented as a 9-state vector, X ═ 0,0,0,0,0,0,1, …,0]. By analogy, the quantum simulation state can represent an n-state vector, so that a plurality of different states of the qubit can be simulated by using a single computer, and the states of different simulated qubits can be converted into each other.
The free vector calculation of each computer can adopt a table function calculation algorithm, the calculation result is obtained by directly looking up the table, the processing and calculation time required in the intermediate calculation process is saved, and the calculation time delay is reduced to the minimum. The calculation speed of each quantum calculation simulation node is rapidly improved. Of course, in other preferred embodiments of the present invention, a digital-analog algorithm such as the Grover quantum search algorithm, the Shor quantum algorithm, etc. may be employed. Namely, the quantum computing algorithm is digitally modified, so that the computation can be carried out on the von Willebrand computing node.
In a preferred embodiment of the present invention, the table function calculation method includes: cognitive classification characterization, coding of characterization, classification calculation of characterization, storage of characterization, and generation and search matching calculation of an input and output function mapping relation table integrated with storage and calculation.
Preferably, the cognitive classification characterization includes establishing a knowledge characterization system corresponding to a human brain cognitive function category; the characterized codes comprise different data structures which are established according to different attribute characteristics of different characterization categories to form different codes of different characterization categories; the classification calculation of the representation comprises the steps of calculating and processing data of different representation types by adopting different calculation algorithms according to different representation types and codes; the storage of the characterization comprises the steps of carrying out compression storage on corresponding data based on a customized data storage model according to the characterization category and the coding and classification processing result, and supporting exponential-level quick access; the generation of the integrated input and output function mapping relation table comprises the steps of generating output information result values corresponding to different input information through off-line calculation according to the characterization category, the codes and the classification calculation algorithm, and forming an input and output function mapping relation table of corresponding results based on a unified table function template; and the searching and matching calculation comprises the steps of adopting a multi-stage mode searching algorithm when a calculation task exists, directly searching in an input space of the input and output function mapping relation table through an input variable value, and outputting a calculation result value according to an output true value corresponding to the variable meeting the matching.
In a preferred embodiment of the present invention, the table function calculation algorithm adopted by the single computer-formed calculation node mainly includes the following steps:
step 1: cognitive classification characterization. Based on the structure of the human brain cognitive function, a formal description method is adopted to classify, characterize and describe the cognitive contents of the physical world and the problem space, and a knowledge characterization system corresponding to the human brain cognitive function is established. The step is mainly based on a discipline classification table, and an information classification basic class is constructed. And mapping according to the division of the basic classes and the different attributes of the corresponding basic classes according to 66 partitions of the human brain functions, inheriting the connection relation between classes of the brain function structure, and forming the attribute classes and the connection relation of the information classification. The basic classes and the attribute feature classes are characterized by a certain formalization method, wherein the basic classes and the attribute feature classes comprise motion, color, space topological structures, time sequences, languages, heat, sound, light, points, magnetism, energy and the like, and the characterization results such as numerical values, symbols, images, voice, videos and the like are formed. Overlay knowledge graph related information, and related knowledge systems.
Step 2: and (4) coding the characterization. And establishing a corresponding data structure according to different attribute characteristics of different characterization categories to form different codes of the classification characterization. The method mainly defines different data structures, such as a data structure of a spatial topological structure, a data structure of a language, a data structure of sound and the like, for different attribute feature classes represented by classification, and forms corresponding attribute class feature data structures of brain function partitions. And coding corresponding to different attribute class characteristic data structures, wherein different codes correspond to different data structures.
And step 3: and (4) performing classification calculation of the characterization. And according to different characterization classes and codes, calculating and processing data by adopting different calculation algorithms and the like for different characterization classes. In the step, aiming at the characteristic category and the attribute feature code, a corresponding processing algorithm is constructed to calculate the data. Such as numerical class processing algorithms, symbolic class processing algorithms, speech class processing algorithms, image class processing algorithms, and the like. Then, different algorithms are called for calculation and processing according to different characterization categories and coding input information.
And 4, step 4: and (4) storing the characterization. And according to the classification representation, coding and classification processing results, based on a customized data storage model, carrying out compression storage on corresponding data, and supporting exponential-grade quick access. The step is mainly based on the integration of storage and calculation and the requirement of quick access, and combines the characteristics of classification representation and attribute characteristic data to construct a corresponding data storage model. As can be seen from fig. 2, the data of the attribute features of different characterization classifications is modeled by using an extensible storage model, such as a one-dimensional infinite-depth potential well model, according to different types of values, symbols, images, voices, videos, and the like, and different types of feature attribute data of motions, colors, spatial topological structures, time sequences, languages, heat, sound, light, points, magnetism, energy, and the like. And then, aiming at the data of different representation types, different data compression modes are adopted to store the representation type data.
And 5: and generating an input and output function mapping relation table of the storage and calculation body. According to the classification representation, coding and classification calculation algorithms, output information result values corresponding to different input information are generated through off-line calculation; and forming an input and output truth value mapping relation table of corresponding results based on the unified table function template. In the step, different types of characterization information sets are mainly used as input sets. And according to the classification characterization, coding and classification calculation algorithm, a gridding method is adopted to perform off-line calculation on each input value of the gridding division to generate a corresponding output information result value. And traversing the grid input values of the whole input set to generate a corresponding result value output set. And then forming an input and output truth value mapping relation table of corresponding results based on the unified table function template. The table function template can be simply realized by a two-dimensional table or a multi-dimensional table, and is specifically designed in actual realization according to the data type and the mapping relation. Finally, a memory and calculation database integrated with storage and calculation is generated, and exponential-level quick access is supported.
Step 6: and (5) searching and matching calculation. When a calculation task exists, adopting an ART3(Adaptive Resonance Theory 3) self-Adaptive resonant network 3 multi-level mode search algorithm, and directly searching in an input space of a truth mapping table through an input variable value; judging the matching degree of the input and the input mode in the mapping table by adopting a mode similarity threshold calculation method and a rule; and the output true value corresponding to the input meeting the matching is a calculation result value and can be directly output.
The mode similarity calculation method mainly comprises a text similarity calculation method, a vector space cosine similarity calculation method and the like. The similarity calculation method of the cosine of the vector space uses the cosine value of the included angle of two vectors in the vector space as the measure of the difference between two individuals. The cosine value is closer to 1, which indicates that the included angle is closer to 0 degree, namely the two vectors are more similar, which is called cosine similarity. The cosine formula of the included angle of the n-dimensional vector is as follows:
in the present invention, a plurality of von willebrand computers can be used to simulate a plurality of degrees of freedom of a calculated vector { X } - }1 T,X2 T,X3 T,…,Xw T}. For example, a 9-bit quantum state analog qubit is defined. Wherein, the 5-state analog qubit, the 3-state analog qubit, the 9-state analog qubit, the 7-state analog qubit and the 4-state analog qubit are respectively expressed as follows:
in step S2, the plurality of compute nodes formed by the plurality of computers are assembled and scheduled to be encoded into the required compute vectors to model the characteristics of the qubits. Preferably, the computing nodes formed by the computers are scheduled and assembled through a centerless full-switched network based on a Software Defined Network (SDN), and are encoded into required computing vectors. Preferably, the fast information exchange and superposition coherence of a plurality of calculation vectors can be realized through a designed complex wave function, namely the characteristics of the qubits can be simulated. The complex wave function can be the composition of various quantum eigen state wave functions such as sine waves, cosine waves and the like, and can be compounded and superposed according to the calculation task requirement to achieve the required calculation effect.
In step S3, quantum parallel computation is realized according to quantum informatics computation theory based on the computation vector. In a preferred embodiment of the invention, based on said constructed calculation vector { X } ═ X1 T,X2 T,X3 T,…,Xw TAnd performing a quantum computing process according to a quantum informatics computing theory method, namely, realizing quantum parallel computing, thereby achieving quantum computing efficiency.
The invention uses the traditional Von Neumann architecture to simulate the quantum bit, constructs a simulation method of quantum computation, breaks the constraint of the architecture and provides a super-high-speed computation mode. Although the invention is not described or limited to the following computational process in terms of quantum informatics. It is to be understood that, because the method of modeling qubits is described in detail, the calculation of quantum rates is within the reach of one skilled in the art in conjunction with the computational theory of quantum informatics.
Fig. 3 is a schematic block diagram of a table function based quantum computing simulation system according to a first preferred embodiment of the present invention. As shown in fig. 3, the quantum computation simulation system based on table function includes a plurality of computers 100 communicating with each other, each computing mechanism forming a computation node, the computation vector of each computer representing a qubit, the computation vector computation of each computer being computed by using a table function computation method; the computers simulate calculation vectors with multiple degrees of freedom, and a plurality of calculation nodes formed by the computers are assembled and scheduled and encoded into required calculation vectors to simulate the characteristics of qubits; and based on the calculation vector, realizing quantum parallel calculation according to a quantum informatics calculation theory. Each of the computers 100 includes a table function calculation module 110 and a complex function module 120. The table function calculation module 110 is configured to perform cognitive classification characterization, coding of the characterization, classification calculation of the characterization, storage of the characterization, and generation of an input/output function mapping relation table and search matching calculation. The complex function module 120 is used to realize the fast information exchange and superposition coherence of multiple computation vectors, i.e. to simulate the characteristics of qubits. The complex wave function can be the composition of various quantum eigen state wave functions such as sine waves, cosine waves and the like, and can be compounded and superposed according to the calculation task requirement to achieve the required calculation effect. The plurality of computing nodes 100 are connected for assembly and scheduling through a software defined network based centerless fully switched network 200, and the output results of the plurality of computers are composited through a designed complex wave function to form a quantum state superposition output.
Those skilled in the art will appreciate that the computer 100 may be any suitable computer, preferably a von willebrand computer, on which is loaded any system, protocol, or module required to implement the present invention. The table function calculation module 110 and the complex function module 120 may be any hardware module, software module, or software and hardware module, which can perform the corresponding steps of the foregoing table function-based quantum computing simulation system in a one-to-one correspondence.
Fig. 4 is a schematic block diagram of a quantum computing simulation system based on a table function according to a second preferred embodiment of the present invention. In the preferred embodiment of the invention, a simulation quantum computing system for simulating a single quantum bit is formed by combining a customized linux operating system, a customized eigenstate wave function, a table function computing algorithm, a one-dimensional infinite deep potential well data storage model and a quantum data (library) well under the existing computer system architecture. n (n is a natural number) analog quantum computing nodes are connected through a non-centralized full-switching network based on an SDN, a full-switching network architecture with separated control and data planes is adopted, network information and control information transmitted among different nodes are separated, high-speed data exchange among the nodes is guaranteed, and a quantum computing simulation system supporting quantum computing efficiency is formed.
The internal structures of the n computing nodes are the same, and each node comprises: the system comprises a quantum bit representation module, a quantum gate control module, a customized eigenstate wave function, a table function calculation algorithm, a one-dimensional infinite depth potential well data storage model, a quantum data (library) well, a customized operating system and an SDN-based centerless full-switched network.
The qubit representation module represents a plurality of qubits based on parallel integration of a plurality of computers, wherein each 1 computer realizes the representation of 1 qubit, each qubit has a plurality of states, and the plurality of computers realize the representation of a plurality of qubits and support the coding and state conversion of the plurality of qubits. The quantum gate control module: the method comprises the steps of defining independent numbers of each computer by an SDN mechanism in a software-defined mode, and controlling the output of each computer by sending a network control protocol to realize information control of a quantum logic gate. The customized eigenstate wave function: through a designed specific complex wave function and based on an SDN mechanism, the output results of a plurality of computers are compounded and associated and superposed to form a quantum state superposition effect. The complex wave function can be the composition of various quantum eigen state wave functions such as sine waves, cosine waves and the like, and can be compounded and superposed according to the calculation task requirement to achieve the required calculation effect. The table function calculation algorithm constructs an input-output mapping table by mapping brain function partitions and knowledge classification representation, converts nonlinear calculation into direct table look-up calculation, and greatly improves the calculation speed. The one-dimensional infinite depth potential well data storage model comprises the following steps: and a one-dimensional infinite-depth potential well model is adopted to store quantum data information and support the access of exponential data. The quantum data (library) wells: and realizing a storage library of the quantum information data, and storing all quantum data information. The customized operating system: the linux operating system is subjected to customized transformation so as to adapt to full exchange connection of a plurality of analog quantum bit computers, parallel task distribution and convergence of table function calculation and the like. The SDN-based decentralized fully switched network comprises: based on an SDN mechanism, a full-switching protocol and a full-switching mechanism are adopted to carry out network interconnection on a plurality of computers, and multi-node full-switching calculation is realized.
In a preferred embodiment of the present invention, the table function calculation method includes: cognitive classification characterization, coding of characterization, classification calculation of characterization, storage of characterization, and generation and search matching calculation of an input and output function mapping relation table integrated with storage and calculation.
Preferably, the cognitive classification characterization includes establishing a knowledge characterization system corresponding to a human brain cognitive function category; the characterized codes comprise different data structures which are established according to different attribute characteristics of different characterization categories to form different codes of different characterization categories; the classification calculation of the representation comprises the steps of calculating and processing data of different representation types by adopting different calculation algorithms according to different representation types and codes; the storage of the characterization comprises the steps of carrying out compression storage on corresponding data based on a customized data storage model according to the characterization category and the coding and classification processing result, and supporting exponential-level quick access; the generation of the integrated input and output function mapping relation table comprises the steps of generating output information result values corresponding to different input information through off-line calculation according to the characterization category, the codes and the classification calculation algorithm, and forming an input and output function mapping relation table of corresponding results based on a unified table function template; and the searching and matching calculation comprises the steps of adopting a multi-stage mode searching algorithm when a calculation task exists, directly searching in an input space of the input and output function mapping relation table through an input variable value, and outputting a calculation result value according to an output true value corresponding to the variable meeting the matching.
Those skilled in the art will appreciate that the present invention may be implemented in hardware, software, or a combination of software and hardware. The present invention can be realized in a centralized fashion in at least one computer system, or in a distributed fashion where different elements are spread across several interconnected computer systems. Any kind of computer system or other apparatus adapted for carrying out the methods of the present invention is suited. A typical combination of hardware and software could be a general purpose computer system with a computer program that, when being loaded and executed, controls the computer system such that it carries out the methods described herein.
While the invention has been described with reference to specific embodiments, it will be understood by those skilled in the art that various changes may be made and equivalents may be substituted without departing from the scope of the invention. In addition, many modifications may be made to adapt a particular situation or material to the teachings of the invention without departing from its scope. Therefore, it is intended that the invention not be limited to the particular embodiment disclosed, but that the invention will include all embodiments falling within the scope of the appended claims.
The above description is only for the purpose of illustrating the preferred embodiments of the present invention and is not to be construed as limiting the invention, and any modifications, equivalents and improvements made within the spirit and principle of the present invention are intended to be included within the scope of the present invention.
Claims (10)
1. A quantum computation simulation method based on table functions is characterized by comprising the following steps:
s1, simulating calculation vectors of multiple degrees of freedom by adopting multiple computers, wherein one computer forms a calculation node, the calculation vector of each computer represents a qubit, and the calculation vector calculation of each computer adopts a table function calculation method to calculate;
s2, assembling and scheduling a plurality of computation nodes formed by a plurality of computers, and coding the computation nodes into required computation vectors to simulate the characteristics of qubits;
and S3, based on the calculation vector, according to the quantum informatics calculation theory, realizing quantum parallel calculation.
2. The method of claim 1, wherein in step S1, the computation vector of each computer simulates the quantum simulation state of the qubit, and different quantum simulation states can be switched to each other.
3. The table-function-based quantum computational simulation method of claim 1, wherein the table-function computation method comprises: cognitive classification characterization, coding of characterization, classification calculation of characterization, storage of characterization, and generation and search matching calculation of an input and output function mapping relation table integrated with storage and calculation.
4. The table function-based quantum computational simulation method of claim 3, wherein the cognitive classification characterization comprises establishing a knowledge characterization system corresponding to a human brain cognitive function class; the characterized codes comprise different data structures which are established according to different attribute characteristics of different characterization categories to form different codes of different characterization categories; the classification calculation of the representation comprises the steps of calculating and processing data of different representation types by adopting different calculation algorithms according to different representation types and codes; the storage of the characterization comprises the steps of carrying out compression storage on corresponding data based on a customized data storage model according to the characterization category and the coding and classification processing result, and supporting exponential-level quick access; the generation of the integrated input and output function mapping relation table comprises the steps of generating output information result values corresponding to different input information through off-line calculation according to the characterization category, the codes and the classification calculation algorithm, and forming an input and output function mapping relation table of corresponding results based on a unified table function template; and the searching and matching calculation comprises the steps of adopting a multi-stage mode searching algorithm when a calculation task exists, directly searching in an input space of the input and output function mapping relation table through an input variable value, and outputting a calculation result value according to an output true value corresponding to the variable meeting the matching.
5. The method of claim 4, wherein the data storage model comprises a one-dimensional infinite depth potential well model, the multi-stage pattern search algorithm comprises an adaptive resonant network 3 multi-stage pattern search algorithm, and the matching is performed by a pattern similarity calculation method.
6. The method for quantum computation simulation based on table function as claimed in any one of claims 1-5, wherein in step S2, the multiple computation nodes are connected for assembly and scheduling through a centerless fully-switched network based on software defined network, and the output results of the multiple computers are composited through designed complex wave function to form quantum state superposition output.
7. The method of claim 6, wherein the complex wave function comprises a complex of quantum eigen-state wave functions of sine and cosine waves.
8. A quantum computation simulation system based on a table function is characterized by comprising a plurality of computers which are communicated with each other, wherein each computer forms a computation node, the computation vector of each computer represents a quantum bit, and the computation vector computation of each computer is computed by adopting a table function computation method; the computers simulate calculation vectors with multiple degrees of freedom, and a plurality of calculation nodes formed by the computers are assembled and scheduled and encoded into required calculation vectors to simulate the characteristics of qubits; and based on the calculation vector, realizing quantum parallel calculation according to a quantum informatics calculation theory.
9. The table function based quantum computing simulation system of claim 8, wherein each of the computers comprises:
and the table function calculation module is used for executing cognitive classification representation, coding of the representation, classification calculation of the representation, storage of the representation, and generation and search matching calculation of the input and output function mapping relation table integrated with the storage and calculation.
10. The system according to claim 9, wherein the plurality of computing nodes are connected for assembly and scheduling through a centerless full-switch network based on a software defined network, and the output results of the plurality of computers are combined through a designed complex wave function to form a quantum state superposition output.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110157917.2A CN112819172A (en) | 2021-02-04 | 2021-02-04 | Quantum computation simulation method and system based on table function |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110157917.2A CN112819172A (en) | 2021-02-04 | 2021-02-04 | Quantum computation simulation method and system based on table function |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN112819172A true CN112819172A (en) | 2021-05-18 |
Family
ID=75861565
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202110157917.2A Pending CN112819172A (en) | 2021-02-04 | 2021-02-04 | Quantum computation simulation method and system based on table function |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN112819172A (en) |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113242267A (en) * | 2021-07-12 | 2021-08-10 | 深圳市永达电子信息股份有限公司 | Situation perception method based on brain-like calculation |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107679157A (en) * | 2017-09-27 | 2018-02-09 | 杨建民 | Automatic coding and system |
| DE102017008487A1 (en) * | 2017-09-09 | 2019-03-14 | Friedrich Fix | Qubit structures in quantum computer systems |
| CN110457285A (en) * | 2019-06-06 | 2019-11-15 | 福建奇点时空数字科技有限公司 | A kind of structured message data mapping method based on direct Mapping standard |
| US20200218787A1 (en) * | 2019-01-03 | 2020-07-09 | International Business Machines Corporation | Avoiding data exchange in gate operation for quantum computing gates on a chip |
| CN112001498A (en) * | 2020-08-14 | 2020-11-27 | 苏州浪潮智能科技有限公司 | Data identification method and device based on quantum computer and readable storage medium |
-
2021
- 2021-02-04 CN CN202110157917.2A patent/CN112819172A/en active Pending
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| DE102017008487A1 (en) * | 2017-09-09 | 2019-03-14 | Friedrich Fix | Qubit structures in quantum computer systems |
| CN107679157A (en) * | 2017-09-27 | 2018-02-09 | 杨建民 | Automatic coding and system |
| US20200218787A1 (en) * | 2019-01-03 | 2020-07-09 | International Business Machines Corporation | Avoiding data exchange in gate operation for quantum computing gates on a chip |
| CN110457285A (en) * | 2019-06-06 | 2019-11-15 | 福建奇点时空数字科技有限公司 | A kind of structured message data mapping method based on direct Mapping standard |
| CN112001498A (en) * | 2020-08-14 | 2020-11-27 | 苏州浪潮智能科技有限公司 | Data identification method and device based on quantum computer and readable storage medium |
Non-Patent Citations (2)
| Title |
|---|
| 宋辉: "量子计算机体系结构及模拟技术的研究与实现", 《信息科技辑》, no. 1, pages 4 - 5 * |
| 彭小萍: "自适应共振理论原理与应用研究", 《中国优秀硕士学位论文全文数据库 信息科技辑》 * |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113242267A (en) * | 2021-07-12 | 2021-08-10 | 深圳市永达电子信息股份有限公司 | Situation perception method based on brain-like calculation |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Ma et al. | A survey of weight vector adjustment methods for decomposition-based multiobjective evolutionary algorithms | |
| Zhou et al. | Stacked extreme learning machines | |
| CA3085897A1 (en) | Evolutionary architectures for evolution of deep neural networks | |
| Qi et al. | Multi-objective immune algorithm with Baldwinian learning | |
| Deshpande et al. | Computational creativity via assisted variational synthesis of mechanisms using deep generative models | |
| CN111460928A (en) | Human body action recognition system and method | |
| Bidgoli et al. | Deepcloud. The application of a data-driven, generative model in design | |
| Jiang et al. | Block-level knowledge transfer for evolutionary multitask optimization | |
| Musikawan et al. | Parallelized metaheuristic-ensemble of heterogeneous feedforward neural networks for regression problems | |
| Kim et al. | Learning robot structure and motion embeddings using graph neural networks | |
| CN112819172A (en) | Quantum computation simulation method and system based on table function | |
| Liu et al. | Understanding the distillation process from deep generative models to tractable probabilistic circuits | |
| Yan et al. | Exkgr: Explainable multi-hop reasoning for evolving knowledge graph | |
| Schrodi et al. | Construction of hierarchical neural architecture search spaces based on context-free grammars | |
| CN116579274A (en) | Automatic design method of tensor operation acceleration chip | |
| Zhou et al. | A single-shot generalized device placement for large dataflow graphs | |
| JP2002042104A (en) | Control system and control method using quantum soft computing | |
| Nagar et al. | Quadratic GCN for graph classification | |
| Liu et al. | A pseudoinversion-free method for weight updating in broad learning system | |
| Shan et al. | Evolutionary extreme learning machine optimized by quantum-behaved particle swarm optimization | |
| Mungai et al. | A study on merging mechanisms of simple hopfield network models for building associative memory | |
| Altares-López et al. | AutoQML: Automatic Generation and Training of Robust Quantum-Inspired Classifiers by Using Genetic Algorithms on Grayscale Images | |
| CN113836174A (en) | Asynchronous SQL connection query optimization method based on reinforcement learning DQN algorithm | |
| Li et al. | Sketching merge trees for scientific visualization | |
| Javaheripi et al. | Swann: Small-world architecture for fast convergence of neural networks |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination |