Teaching game system and teaching interactive control method based on quantum computing principle

Abstract

A teaching game system comprises: a maze generation module configured to generate a maze matrix comprising walls and resource items, a character state management module configured to control dynamic switching of a character between a normal state and an overlay state, an active circuit construction module configured to generate a to-be-supplemented quantum circuit with a part of quantum gates missing in response to a first trigger event of the character moving to a position where a target quantum state is located, and receive user input to construct a first target quantum circuit, a passive circuit construction module configured to lock movement of the character and generate a blank quantum circuit in response to a second trigger event of a number of movements of the character being exhausted, and receive user input to construct a second target quantum circuit, and a circuit evaluation and execution module configured to perform effectiveness evaluation on the first target quantum circuit or the second target quantum circuit, and perform an adjustment operation according to an evaluation result. In this way, the concept of quantum computing is converted into game content, avoiding entry barriers caused by insufficient prior knowledge.

Description

Technical Field

[0001] This application relates to the field of quantum computing technology, and in particular to a teaching game system based on quantum computing principles, a teaching interactive control method based on quantum computing principles, an electronic device, and a computer-readable storage medium. Background Technology

[0002] In the application scenarios of introductory quantum computing education, related technologies often rely entirely on theoretical explanations or static deductions. Quantum computing concepts, limited by their mathematical properties, require a high level of prior knowledge from learners and are susceptible to learning gaps due to differences in cognitive foundations, limited learning methods, and delayed feedback mechanisms. If learners lack the necessary linear algebra knowledge, traditional introductory quantum computing education will struggle to progress smoothly, leading to difficulties in getting started, low participation, and a narrow overall reach in popular science education. Summary of the Invention

[0003] This application provides a teaching game system based on quantum computing principles, a teaching interactive control method based on quantum computing principles, an electronic device, and a computer-readable storage medium.

[0004] This application provides an educational game system based on quantum computing principles, comprising: A maze generation module is configured to generate a maze matrix including walls and resource items, wherein the resource items include target quantum state and superposition state energy; The character state management module is configured to control the dynamic switching of the character between a normal state and a superimposed state. When the character is in the normal state, it is restricted by the entity collision rules of the wall. When it is in the superimposed state, it can pass through the wall and continuously consume the superimposed state energy. An active circuit construction module is configured to generate a quantum circuit to be supplemented with some missing quantum gates in response to a first trigger event in which the character moves to the location of the target quantum state, and to receive user input to construct the first target quantum circuit. The passive circuit construction module is configured to, in response to a second trigger event that the character's movement count has been exhausted, lock the character's movement and generate a blank quantum circuit, and receive user input to construct a second target quantum circuit; The circuit evaluation and execution module is configured to evaluate the effectiveness of the first target quantum circuit or the second target quantum circuit, and perform game resource adjustment operations corresponding to the resource item based on the evaluation results.

[0005] Thus, in this embodiment, the concepts related to quantum computing can be transformed into operable game content through an interactive format combining maze exploration and route construction. This avoids entry barriers caused by insufficient prior knowledge and broadens the reach of quantum computing popular science education. The two route construction modes correspond to different learning stages, adaptable to learners with varying knowledge levels, and provide tiered, progressive learning guidance. The route construction results are directly linked to game resources, providing learners with immediate feedback, reinforcing learning effectiveness, and enhancing engagement and sustained motivation throughout the learning process.

[0006] In some implementations, the maze and role state management module is further configured to: set an observer role in the maze matrix; Set the observer role in the maze matrix; The system also includes an observer interference module, which is configured as follows: Under a preset difficulty level, and when the character in the superposition state and the observer character meet preset spatial interference conditions, the character is controlled to exit the superposition state and a preset value of superposition state energy is deducted.

[0007] Thus, in this embodiment, the observer effect in quantum mechanics can be transformed into an operable game experience through the interaction mechanism between the observer role and the superposition state. This helps learners intuitively understand the principle that observation behavior triggers changes in the superposition state, supplementing the teaching dimension of the original system regarding quantum measurement concepts. The observer mechanism is directly linked to the difficulty level, enriching the game content at different difficulty levels, enhancing the strategy and challenge of advanced stages, guiding learners to rationally plan the timing of using the superposition state, and strengthening resource management awareness. This mechanism does not require modification of the original maze exploration and route construction process and can be directly extended and implemented on the basis of the original system, possessing good functional compatibility and iteration space.

[0008] In some implementations, the maze generation module is further configured to: Multiple sets of quantum entangled portal pairs are set in the maze matrix, and each set of quantum entangled portal pairs includes two portals with the same color markings; The role status management module is also configured as follows: In response to the event that the character enters the first portal and triggers a secondary confirmation command, the character's position is updated to a second portal with the same color as the first portal. The first portal is one of the portals in any of the quantum entangled portal pairs that the character enters, and the second portal is another portal in the quantum entangled portal pair to which the first portal belongs.

[0009] Thus, in this embodiment, the non-local correlation characteristics of quantum entanglement can be transformed into an interactive game through a paired, interconnected portal mechanism. This helps learners intuitively understand the spatial correspondence between two particles in an entangled system, supplementing the original system's quantum concept teaching dimension. The secondary confirmation mechanism effectively reduces accidental portal activation, ensuring a smooth exploration process and improving the user experience. The multiple portals enrich the maze's path selection space, allowing learners to plan the optimal path using route-building resources, enhancing the game's strategic depth and exploration enjoyment. This mechanism requires no modification to the original maze exploration and route-building processes and can be directly extended from the existing system, possessing good functional compatibility and iteration potential.

[0010] In some implementations, the active circuit construction module is further configured to: Set the initial number of qubits and the initial line depth of the quantum circuit to be supplemented; For each target quantum state collected, the number of qubits and the depth of the next generated quantum circuit to be supplemented are incremented by a preset unit, and the circuit construction parameters are updated.

[0011] Thus, in this embodiment, the binding mechanism between the target quantum state collection progress and circuit parameters enables a gradual increase in circuit construction difficulty. This ensures that the complexity of circuit practice matches the learner's real-time progress, avoiding frustration caused by excessive difficulty in the introductory stage while guaranteeing sufficient challenge in the advanced stage. The automatic parameter update mechanism eliminates the need for learners to manually adjust the difficulty, allowing the practice intensity to naturally increase along with the exploration process. This aligns with the step-by-step learning and cognitive patterns, helping learners gradually build their quantum circuit design capabilities. This mechanism, deeply integrated with the active circuit construction mode, optimizes the adaptability of practice without altering the original exploration process, exhibiting good functional compatibility and pedagogical practicality.

[0012] In some implementations, the line evaluation and execution module is further configured as follows: Calculate the first degree of overlap between the final quantum state of the first target quantum circuit and the preset target quantum state, or calculate the second degree of overlap between the final quantum state of the second target quantum circuit and the preset target quantum state; Compare the first overlap or the second overlap with a preset threshold; If the first overlap or the second overlap is greater than or equal to the preset threshold, the validity assessment is determined to be passed, and at least one reward operation is performed, such as increasing the number of moves, increasing the superposition energy, or marking the target quantum state as one of the collected ones. If either the first overlap or the second overlap is less than the preset threshold, the validity assessment is determined to be unsuccessful, and at least one penalty operation is performed, either by deducting the superposition state energy or resetting the target quantum state collection state.

[0013] Thus, in this embodiment, the overlap degree can be used to quantitatively evaluate the route construction results, allowing learners to intuitively understand the degree of matching between their designed routes and the target results, facilitating the identification of deviations and gradual optimization of route design capabilities. A differentiated reward and punishment mechanism based on threshold judgment can provide corresponding feedback to different levels of completion, forming a tiered learning guidance and strengthening learning effectiveness. The evaluation results are deeply integrated with game resources, allowing learners to instantly perceive the impact of their actions and improving the effectiveness of learning feedback. This mechanism is fully compatible with the original active and passive route construction modes, improving the precision of the evaluation system without changing the basic interaction process, and possesses good functional adaptability and pedagogical value.

[0014] In some embodiments, the system further includes a measurement challenge triggering module, which is configured to: When the character is in the superposition state and performs a wall-penetrating operation, a measurement challenge event is triggered according to a preset probability; In response to the aforementioned measurement challenge event, a quantum measurement question is output for the user to answer; If the correct answer is received, the character is allowed to complete the wall-penetrating operation; If an incorrect solution is received, the character is controlled to exit the superposition state and the superposition state energy of a preset value is deducted.

[0015] Thus, in this embodiment, the principle of state collapse caused by quantum measurement can be transformed into a perceptible game interaction through random measurement challenges during the wall-passing process. This helps learners intuitively understand the impact of measurement behavior on superposition states and establish a correspondence between quantum physics concepts and game operations. The probabilistic triggering design can simulate the random characteristics of quantum measurement, increase the strategic nature of using superposition states, guide learners to rationally plan the timing of wall-passing, and enhance the strategic depth of the game. Interspersed knowledge questions can strengthen the memory of quantum measurement-related knowledge points during exploration, enrich the teaching dimensions, and achieve a deep integration of exploration and knowledge practice. This mechanism does not require changes to the original maze exploration and route construction process and can be directly extended and implemented on the basis of the original system, possessing good functional compatibility and educational extension value.

[0016] In some embodiments, the system further includes a dynamic difficulty adjustment module, which is configured as follows: Obtain the learner's current cumulative score and route construction success rate; If the current cumulative score and the success rate meet the preset upgrade conditions, the game difficulty level will be increased; Based on the increased difficulty level, the wall distribution and portal network of the maze matrix are adaptively reconstructed, and at least one obstacle element from the categories of observer role, quantum trap, or rotating wall is added to the maze matrix.

[0017] Thus, in this embodiment, the difficulty level can be automatically triggered based on the learner's real-time performance, achieving a dynamic match between the game's challenge intensity and the learner's skill development. This avoids the mismatch caused by a fixed difficulty level being too high or too low, ensuring a smooth and comfortable learning process. Each upgrade simultaneously reconstructs the maze layout, preventing learners from memorizing fixed paths, maintaining the freshness of the exploration process, and enhancing the game's continued appeal. The multi-dimensional difficulty upgrade design simultaneously covers multiple aspects such as maze structure, question difficulty, and obstacle elements, forming a three-dimensional challenge gradient that matches the learner's multi-dimensional skill development pace. This mechanism eliminates the need for learners to manually adjust the difficulty; it automatically adapts to the game's progress, resulting in a smoother user experience and improving teaching adaptability and learning efficiency.

[0018] This application provides a teaching interaction control method based on quantum computing principles for the aforementioned teaching game system, comprising: Generate a maze matrix including walls and resource items, wherein the resource items include the target quantum state and superposition state energy; The character is controlled to dynamically switch between a normal state and a superposition state. When the character is in the normal state, it is restricted by the entity collision rules of the wall. When the character is in the superposition state, it can pass through the wall and continuously consume the superposition state energy. In response to a first triggering event where the character moves to the location of the target quantum state, a quantum circuit with missing quantum gates is generated to be supplemented, and user input is received to construct the first target quantum circuit; In response to a second trigger event that the character's move count has been exhausted, the character's movement is locked and a blank quantum circuit is generated, and user input is received to construct a second target quantum circuit; The effectiveness of the first target quantum circuit or the second target quantum circuit is evaluated, and the game resource adjustment operation corresponding to the resource item is executed according to the evaluation result.

[0019] Thus, in this embodiment, the concepts related to quantum computing can be transformed into operable game content through an interactive format combining maze exploration and route construction. This avoids entry barriers caused by insufficient prior knowledge and broadens the reach of quantum computing popular science education. The two route construction modes correspond to different learning stages, adaptable to learners with varying knowledge levels, and provide tiered, progressive learning guidance. The route construction results are directly linked to game resources, providing learners with immediate feedback, reinforcing learning effectiveness, and enhancing engagement and sustained motivation throughout the learning process.

[0020] In some implementations, the step of evaluating the effectiveness of the first target quantum circuit or the second target quantum circuit, and performing game resource adjustment operations corresponding to the resource item based on the evaluation results, includes: Calculate the first degree of overlap between the final quantum state of the first target quantum circuit and the preset target quantum state, or calculate the second degree of overlap between the final quantum state of the second target quantum circuit and the preset target quantum state; Compare the first overlap or the second overlap with a preset threshold; If the first overlap or the second overlap is greater than or equal to the preset threshold, the validity assessment is determined to be passed, and at least one reward operation is performed, such as increasing the number of moves, increasing the superposition energy, or marking the target quantum state as one of the collected ones. If either the first overlap or the second overlap is less than the preset threshold, the validity assessment is determined to be unsuccessful, and at least one penalty operation is performed, either by deducting the superposition state energy or resetting the target quantum state collection state.

[0021] Thus, in this embodiment, the overlap degree can be used to quantitatively evaluate the route construction results, allowing learners to intuitively understand the degree of matching between their designed routes and the target results, facilitating the identification of deviations and gradual optimization of route design capabilities. A differentiated reward and punishment mechanism based on threshold judgment can provide corresponding feedback to different levels of completion, forming a tiered learning guidance and strengthening learning effectiveness. The evaluation results are deeply integrated with game resources, allowing learners to instantly perceive the impact of their actions and improving the effectiveness of learning feedback. This mechanism is fully compatible with the original active and passive route construction modes, improving the precision of the evaluation system without changing the basic interaction process, and possesses good functional adaptability and pedagogical value.

[0022] This application provides an electronic device, including a memory and a processor. The memory stores a computer program, which, when executed by the processor, implements the method described above.

[0023] This application provides a computer-readable storage medium storing a computer program that, when executed by one or more processors, implements the above-described method.

[0024] Additional aspects and advantages of embodiments of this application will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of embodiments of this application. Attached Figure Description

[0025] The above and / or additional aspects and advantages of this application will become apparent and readily understood from the description of the embodiments taken in conjunction with the following drawings, wherein: Figure 1 This is a schematic diagram of the architecture of a quantum computing-based educational game system 1000 according to certain embodiments of this application; Figure 2 This is a schematic diagram of a teaching interaction control method based on quantum computing principles according to certain embodiments of this application; Figure 3 This is one of the schematic diagrams of an interactive educational game scenario in certain embodiments of this application; Figure 4 This is the second illustration of an interactive educational game scenario in some embodiments of this application. Detailed Implementation

[0026] The embodiments of this application are described in detail below. Examples of the embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain the embodiments of this application, and should not be construed as limiting the embodiments of this application.

[0027] In the practical application of quantum computing popularization and education, interactive introductory tools are important vehicles for lowering the entry barrier to the subject and expanding the reach of popular science. Most quantum teaching programs still follow the traditional theoretical explanation or card-based deduction model, where learners understand basic concepts such as quantum states and quantum circuits through books or static props, and rely on logical deduction to complete the understanding of principles. This teaching model can play a corresponding role in knowledge transfer when facing learners with a certain mathematical and physical background. However, as the demand for quantum computing popularization extends to younger, zero-based groups, the various drawbacks of the traditional teaching model are gradually becoming apparent, continuously affecting the effectiveness of quantum enlightenment education.

[0028] In various quantum teaching scenarios, learners often need to master linear algebra knowledge such as the Hilbert space tensor product to understand the underlying logic of quantum computing. This high level of prior knowledge raises the entry barrier to the subject and is difficult to adapt to the needs of science education in primary and secondary schools. Consequently, when learners with no prior knowledge encounter quantum computing, the lack of concrete examples makes comprehension difficult, leading to a rapid decline in interest and an inability to keep up with the learning progress.

[0029] Beyond the learning challenges posed by cognitive barriers, the singular nature of traditional teaching interaction methods makes it difficult to maintain learners' long-term attention. Specifically, existing teaching processes typically focus on one-way knowledge output, with learners passively receiving theoretical content. The lack of hands-on practical activities prevents learners from transforming learned concepts into tangible operational experiences, resulting in low knowledge retention and failing to achieve the expected learning outcomes.

[0030] In addition, the difficulty levels of the teaching content in related technologies are relatively fixed, and the challenge intensity cannot be dynamically adjusted according to the learner's real-time performance. This makes it difficult to provide sufficient guidance and assistance to beginners, and also makes it difficult to provide suitable advanced practice space for learners with a foundation.

[0031] Furthermore, the feedback mechanism of existing teaching tools is relatively lagging. After completing the exercises, learners usually have to wait for manual grading or the results to be published at fixed intervals. They cannot get immediate feedback after completing the operation, which can easily disrupt the learning rhythm and is not conducive to timely correction of cognitive biases.

[0032] To address the aforementioned potential problems, this application provides a teaching game system 1000 based on quantum computing principles. The teaching game system 1000 includes a maze generation module 100, a character state management module 200, an active route construction module 300, a passive route construction module 400, and a route evaluation and execution module 500.

[0033] The maze generation module 100 is configured to generate a maze matrix that includes walls and resource items, where the resource items include the target quantum state and superposition state energy. The character state management module 200 is configured to control the dynamic switching of the character between the normal state and the superposition state. When the character is in the normal state, it is restricted by the entity collision rules of the wall. When it is in the superposition state, it can pass through the wall and continuously consume superposition state energy. The active circuit construction module 300 is configured to generate a quantum circuit to be supplemented with some missing quantum gates in response to a first trigger event when the character moves to the target quantum state position, and to receive user input to construct the first target quantum circuit; The passive circuit construction module 400 is configured to lock the character's movement and generate a blank quantum circuit in response to a second trigger event that the character's movement count has been exhausted, and to receive user input to construct a second target quantum circuit. The circuit evaluation and execution module 500 is configured to evaluate the effectiveness of the first target quantum circuit or the second target quantum circuit, and perform game resource adjustment operations corresponding to the resource items based on the evaluation results.

[0034] Specifically, in the application scenarios of introductory quantum computing education, the implementation of related teaching programs relies entirely on theoretical explanations or static deductions. Quantum computing concepts, limited by their mathematical properties, require a high level of prior knowledge from learners and are easily affected by differences in cognitive foundations, monotonous learning methods, and lagging feedback mechanisms, leading to learning gaps. When learners lack the corresponding linear algebra knowledge, introductory quantum computing education based on traditional methods cannot proceed smoothly, resulting in difficulties for learners, low participation, and a narrow overall reach of popular science education. Furthermore, in related technologies, the difficulty levels of teaching content are often implemented using fixed presets. The adaptability of this approach depends on pre-defined learning stages, and in the context of dynamically changing learner levels, it may fail to match real-time learning progress. Learners cannot obtain corresponding practice content based on their own mastery, resulting in low learning efficiency.

[0035] Based on this, this application provides a teaching game system 1000 based on quantum computing principles. It constructs an interactive teaching mechanism with maze exploration as the main focus and route construction as a supplement, in order to lower the entry threshold, achieve tiered learning adaptation, and provide real-time learning feedback. Through the coordinated cooperation of the maze generation module 100, the character state management module 200, the active route construction module 200, the passive route construction module 300, and the route evaluation and execution module 400, a two-way mapping logic between quantum behavior and game operation is developed. Quantum route practice is integrated into the process of learners exploring the maze, and the route construction process of corresponding difficulty is initiated under different trigger conditions, thereby completing the gradual transfer of quantum computing knowledge.

[0036] In some implementations, the maze generation module 100 can be understood as a functional unit responsible for generating game maps and setting up scene elements, undertaking basic scene construction logic such as maze layout generation and wall and resource item placement.

[0037] In some implementations, the role status management module 200 can be understood as a functional unit responsible for controlling the role's operational status, undertaking operational logic such as movement rule determination, status switching control, location update, and energy value adjustment.

[0038] In some implementations, a maze matrix can be understood as a gridded game map carrier, with internal wall structures and various resource items, serving as the spatial basis for character movement and interaction. In some examples, the maze matrix uses a 10... The maze features a grid structure of 10, with resources including different types such as target quantum states and energy spheres. Different resource items correspond to different interaction logics. Eight numbered target quantum states are deployed within the maze. Collectible quantum gate props cover various types, including H, X, Y, Z, S, and CNOT. The number of collected props is positively correlated with the duration of the superposition state. Collected quantum gate props can be simultaneously incorporated into the set of available gates for subsequent circuit construction. The energy sphere is a concrete interactive form of the superposition state energy.

[0039] In some implementations, the normal state can be understood as the basic operating state of the character. When the character is in the normal state, it is restricted by the entity collision rules of the wall and can only move along the barrier-free grid. During the movement, a preset number of moves are consumed.

[0040] In some implementations, the entity collision rule is a passage determination rule based on grid coordinates. When the character is in the normal state, if the character's coordinate position overlaps with the coordinate position of the grid where the wall is located, it is determined to be a collision. The character cannot enter the corresponding grid and can only move along the grids without walls within the maze matrix.

[0041] In some implementations, the superposition state can be understood as a special operating state of a character. After activating the superposition state, the character can pass through walls. During the duration of the state, the superposition state energy will be consumed at a fixed rate. After the energy is exhausted, the character will automatically exit the superposition state.

[0042] In some implementations, superposition energy can be understood as the resource that sustains the operation of the superposition state. It can be obtained by collecting resource items, completing route construction, etc., and is used to support wall-clipping operations and state persistence. Energy is consumed at a fixed rate during the superposition state, and an additional amount of energy is deducted when performing a wall-clipping operation. In some examples, the character initially has 50 movement opportunities and a corresponding amount of superposition energy. The learner can switch the character's state by specifying operation commands.

[0043] In some implementations, the active route construction module 300 can be understood as a functional unit responsible for triggering and carrying out active route practice, which is activated when the role comes into contact with a specified resource item and provides the learner with a semi-complete route framework.

[0044] In some implementations, the first triggering event can be understood as an interactive event triggered when the character moves to the grid position where the target quantum state is located, which is used to initiate an active circuit construction process.

[0045] In some implementations, the quantum circuit to be supplemented can be understood as a pre-built circuit framework with some quantum gates, where one or more empty positions await the learner to fill in the corresponding quantum gates. The quantum circuit can be understood as an operational structure composed of quantum gates in a fixed order, used to realize the evolution of a quantum state from an initial state to a target state. Quantum gates include various basic logic gate types such as H, X, Y, Z, S, and CNOT, and are the smallest unit for constructing a quantum circuit. In some examples, the initial number of qubits and the initial circuit depth of the quantum circuit to be supplemented are set to 2. Each time the learner collects a target quantum state, the number of qubits and the circuit depth of the subsequently generated quantum circuit to be supplemented increase by 1, gradually increasing the difficulty of circuit construction.

[0046] In some implementations, the passive route construction module 400 can be understood as a functional unit responsible for triggering and carrying out passive route practice, which is activated when the character's movement resources are exhausted, forcing the learner to complete the route design in order to restore movement ability.

[0047] In some implementations, the second triggering event can be understood as an interactive event triggered when the character's remaining moves reach zero, used to initiate a passive route construction process.

[0048] In some implementations, a blank quantum circuit can be understood as a circuit framework without pre-set quantum gates, requiring learners to select quantum gates from scratch to build a complete circuit structure.

[0049] In some implementations, the circuit evaluation and execution module 500 can be understood as a functional unit responsible for circuit result verification and resource feedback, undertaking functions such as quantum state evolution calculation, result comparison, and resource parameter adjustment.

[0050] In some implementations, effectiveness assessment can be understood as a process of verifying the results of the quantum circuit constructed by the learner. By calculating the degree of matching between the quantum state output by the circuit and the preset target quantum state, it is determined whether the circuit construction meets the requirements.

[0051] In some implementations, game resource adjustment operations can be understood as adding or subtracting resource parameters in the game based on the evaluation results, in order to provide learners with immediate feedback, and the adjustment objects correspond one-to-one with the types of resource items.

[0052] In some examples, the evaluation process quantifies the degree of matching between the line output state and the target state by measuring overlap; the overlap calculation formula is as follows: The line evaluation and execution module 500 compares the calculated overlap with a preset threshold. If the overlap is greater than or equal to the preset threshold, the evaluation is deemed successful; if the overlap is less than the preset threshold, the evaluation is deemed unsuccessful.

[0053] To more clearly illustrate the operation of the educational game system provided in this application, please refer to the following exemplary description: The maze generation module 100 first completes the game initialization, generating a maze matrix of a specified size, and randomly placing elements such as walls, target quantum states, and energy balls within the grid. The character state management module 200 synchronously initializes resource parameters such as the number of moves and superposition state energy of the character, sets the character to a normal state, and places it at the starting position of the maze.

[0054] Once the game begins, learners control their characters to move within the maze grid using commands, consuming one move per grid move. Learners can switch character states based on path requirements; activating the superposition state allows the character to pass through walls, while continuously consuming superposition state energy.

[0055] When the character moves to the grid containing the target quantum state, the active circuit construction module 300 triggers the first trigger event, generating a quantum circuit to be supplemented and displaying it to the learner. The learner selects appropriate quantum gates to fill the empty positions in the circuit, and after submission, the first target quantum circuit is formed.

[0056] When the character's moves are exhausted, the passive circuit construction module 400 triggers a second event, locking the character's movement, generating a blank quantum circuit, and displaying it to the learner. The learner then independently selects quantum gates to build a complete circuit, which, upon submission, forms the second target quantum circuit.

[0057] After receiving the submitted target quantum circuit, the circuit evaluation and execution module 500 executes a validity evaluation process. It calculates the overlap between the final quantum state output by the circuit and the preset target quantum state, compares the calculated overlap with a preset threshold, and performs corresponding resource adjustment operations based on the comparison result. If the evaluation passes, it replenishes the corresponding number of moves and superposition energy, and marks the corresponding target quantum state as collected. If the evaluation fails, it deducts the corresponding number of superposition energy, maintaining the uncollected state of the target quantum state. After completing the resource adjustment, it restores the character's movement permissions, and the game continues.

[0058] Ultimately, learners must collect all target quantum states and reach the maze exit under limited conditions to complete the game.

[0059] Thus, in this embodiment, the concepts related to quantum computing can be transformed into operable game content through an interactive format combining maze exploration and route construction. This avoids entry barriers caused by insufficient prior knowledge and broadens the reach of quantum computing popular science education. The two route construction modes correspond to different learning stages, adaptable to learners with varying knowledge levels, and provide tiered, progressive learning guidance. The route construction results are directly linked to game resources, providing learners with immediate feedback, reinforcing learning effectiveness, and enhancing engagement and sustained motivation throughout the learning process.

[0060] In some implementations, the role status management module 200 is further configured as follows: Set up observer roles within the maze matrix; System 1000 also includes an observer interference module 600, which is configured as follows: Under the preset difficulty level, and when the character in the superposition state and the observer character meet the preset spatial interference conditions, the character in control exits the superposition state and the superposition state energy of the preset value is deducted.

[0061] Specifically, the embodiments of this application further add an observer interference mechanism. The maze generation module 100 is also configured to set observer roles in the maze matrix. The system 1000 also includes an observer interference module 600, which, under a preset difficulty level, and when the role in the superposition state and the observer role meet preset spatial interference conditions, controls the role to exit the superposition state and deducts a preset value of superposition state energy.

[0062] In some implementations, the observer role can be understood as an automated game character deployed within a maze matrix with a specified monitoring range, used to simulate the observation subject in quantum mechanics, and only affecting characters in a superposition state.

[0063] In some implementations, the preset difficulty level can be understood as a pre-set threshold for game difficulty. Only when the game difficulty reaches the corresponding level will the observer role be deployed and execute interference logic. In some examples, the preset difficulty levels include medium and hard difficulties, and no observer role is set up in the easy difficulty to reduce the operational pressure in the introductory stage.

[0064] In some implementations, the preset spatial interference condition can be understood as a pre-defined spatial determination rule used to determine whether a character enters the observer's effective influence range. In some examples, the preset spatial interference condition is that the straight-line distance between the grid where the character is located and the grid where the observer is located is less than or equal to a preset number of grids, or that the character enters the fan-shaped field of view area in the direction the observer is facing.

[0065] To more clearly illustrate the operation of the observer interference mechanism provided in this application's embodiments, please refer to the following exemplary description: When the game is on easy difficulty, the character state management module 200 does not deploy observer characters within the maze matrix, allowing learners to focus on familiarizing themselves with the basic operations of superposition state switching and route construction. When the game progresses to medium difficulty, the character state management module 200 randomly deploys several observer characters within the maze matrix. Each observer has a fixed field of view and slowly patrols along a preset path. When a character is in a normal state, the observers have no effect on the character, and the character can move normally within the observer's field of view. When a character activates a superposition state and enters the observer's field of view, the observer interference module 600 determines that the two meet preset spatial interference conditions, then controls the character to exit the superposition state and return to the normal state, while deducting the corresponding amount of superposition state energy. When the game progresses to hard difficulty, the observer's field of view further expands, the patrol movement speed increases accordingly, and the probability of interfering with characters in the superposition state increases.

[0066] Thus, in this embodiment, the observer effect in quantum mechanics can be transformed into an operable game experience through the interaction mechanism between the observer role and the superposition state. This helps learners intuitively understand the principle that observation behavior triggers changes in the superposition state, supplementing the teaching dimension of the original system regarding quantum measurement concepts. The observer mechanism is directly linked to the difficulty level, enriching the game content at different difficulty levels, enhancing the strategy and challenge of advanced stages, guiding learners to rationally plan the timing of using the superposition state, and strengthening resource management awareness. This mechanism does not require modification of the original maze exploration and route construction process and can be directly extended and implemented on the basis of the original system, possessing good functional compatibility and iteration space.

[0067] In some implementations, the maze generation module 100 is further configured to: Multiple sets of quantum entangled portal pairs are set in the maze matrix. Each set of quantum entangled portal pairs includes two portals with the same color markings. The role status management module 200 is also configured as follows: In response to the event that the character enters the first portal and triggers a secondary confirmation command, the character's position is updated to the second portal with the same color as the first portal. The first portal is the portal in any quantum entangled portal pair that the character enters, and the second portal is the other portal in the quantum entangled portal pair to which the first portal belongs.

[0068] Specifically, this application further adds a quantum entanglement teleportation mechanism. The maze generation module 100 is also configured to set multiple sets of quantum entanglement teleportation gate pairs in the maze matrix. Each set of quantum entanglement teleportation gate pairs includes two teleportations with the same color identifier. In response to an event where a character enters the first teleportation gate and triggers a secondary confirmation command, the character's position is updated to a second teleportation gate with the same color identifier as the first teleportation gate. The first teleportation gate is the teleportation gate in any set of quantum entanglement teleportation gate pairs that the character enters, and the second teleportation gate is the other teleportation gate in the quantum entanglement teleportation gate pair to which the first teleportation gate belongs.

[0069] In some implementations, quantum entanglement portal pairs can be understood as pairs of interactive points arranged at different locations in a maze matrix and having a linkage teleportation function, used to simulate the nonlocal correlation characteristics of two particles in a quantum entanglement system.

[0070] In some implementations, color coding can be understood as visual markers distinguishing different portal pairs. Two portals of the same color belong to the same group of linked units, while different colors correspond to different teleportation links. In some examples, two sets of quantum entangled portal pairs, one red and one blue, can be set up within the maze matrix. Each set is deployed in different areas of the maze, corresponding to different spatial jump paths.

[0071] In some implementations, the secondary confirmation instruction can be understood as a confirmation operation instruction that the learner needs to actively trigger after the character enters the portal, in order to avoid accidental teleportation caused by accidental touch.

[0072] In some implementations, the first portal can be understood as the portal point that the character actively enters, which is the starting point of the teleportation process.

[0073] In some implementations, the second teleportation gate can be understood as a linked teleportation gate point belonging to the same group as the first teleportation gate, which is the destination of the teleportation process.

[0074] To more clearly illustrate the operation of the quantum entanglement portal provided in this application, please refer to the following exemplary description: When the maze generation module 100 generates the maze matrix, it deploys two sets of quantum entangled portal pairs in different areas of the maze. The first set is marked in red, with the two portals located in the northwest and southeast regions of the maze, respectively. The second set is marked in blue, with the two portals located in the northeast and southwest regions of the maze, respectively. During gameplay, characters in either their normal or superimposed states can enter the portal range. When a character moves to the grid containing a red portal, the system does not immediately execute the teleportation but waits for the learner to trigger a secondary confirmation command. If the learner confirms the teleportation operation, the character state management module 200 updates the character's position from the current red portal location to the grid containing the other portal in the quantum entangled portal pair belonging to the first portal, completing the cross-regional spatial jump. If the learner does not trigger a secondary confirmation command, the character can leave the grid containing the portal normally and continue exploring along the original path.

[0075] Thus, in this embodiment, the non-local correlation characteristics of quantum entanglement can be transformed into an interactive game through a paired, interconnected portal mechanism. This helps learners intuitively understand the spatial correspondence between two particles in an entangled system, supplementing the original system's quantum concept teaching dimension. The secondary confirmation mechanism effectively reduces accidental portal activation, ensuring a smooth exploration process and improving the user experience. The multiple portals enrich the maze's path selection space, allowing learners to plan the optimal path using route-building resources, enhancing the game's strategic depth and exploration enjoyment. This mechanism requires no modification to the original maze exploration and route-building processes and can be directly extended from the existing system, possessing good functional compatibility and iteration potential.

[0076] In some implementations, the active circuit construction module 300 is further configured as follows: Set the initial number of qubits and the initial line depth of the quantum circuit to be supplemented; For each target quantum state collected, the number of qubits and the depth of the next generated quantum circuit to be supplemented are incremented by a preset unit, and the circuit construction parameters are updated.

[0077] Specifically, the embodiments of this application further optimize the parameter evolution mechanism of the active circuit construction module 300. The active circuit construction module 300 is further configured to set the initial number of qubits and the initial circuit depth of the quantum circuit to be supplemented, and to update the circuit construction parameters by incrementing the number of qubits and the circuit depth of the next generated quantum circuit by a preset unit each time a target quantum state is collected.

[0078] In some implementations, the initial number of qubits can be understood as the number of qubits included in the circuit when it is first generated after the game starts, and it is one of the fundamental parameters determining the circuit complexity. In some examples, the initial number of qubits is set to 2, corresponding to a basic circuit framework with two qubits, which is suitable for beginners' introductory cognitive level.

[0079] In some implementations, the initial circuit depth can be understood as the number of time steps included in the circuit when it is first generated after the game starts, which determines the number of quantum gate layers that can be arranged in the circuit. In some examples, the initial circuit depth is set to 2, corresponding to a basic structure of two quantum gates, reducing the operational difficulty of the initial circuit construction.

[0080] In some implementations, the preset unit can be understood as the rate of increase in the number of qubits and the line depth with each parameter update, used to control the overall pace of difficulty increase. In some examples, the preset unit is set to 1, meaning that for each target quantum state collected, the number of qubits increases by 1, and the line depth increases by 1 simultaneously.

[0081] In some implementations, the circuit construction parameters can be understood as the set of configuration parameters used to generate the quantum circuit to be supplemented, including three core indicators: the number of qubits, the circuit depth, and the overlap threshold β. These directly determine the complexity and construction difficulty of the circuit. The overlap threshold β is dynamically adjusted according to the game difficulty level and is a critical criterion for determining whether the circuit construction is effective.

[0082] To more clearly illustrate the operation of the line parameter evolution mechanism provided in this application, please refer to the following exemplary description: During the game's initialization phase, the active circuit construction module sets the initial number of qubits to be supplemented in the quantum circuit to be 2, and the initial circuit depth to be 2. When the character moves to the first target quantum state, the active circuit construction module generates the corresponding quantum circuit to be supplemented according to the initial parameters. The circuit includes 2 qubits and 2 time steps, with a small number of empty positions, and the learner only needs to add a few quantum gates to complete the construction. After the learner successfully completes the circuit construction and collects the target quantum state, the active circuit construction module automatically updates the circuit construction parameters, increasing the number of qubits to 3 and the circuit depth to 3. When the character moves to the second target quantum state, the active circuit construction module generates the quantum circuit to be supplemented according to the updated parameters, with the circuit size and the number of empty positions correspondingly increasing, and the construction difficulty increasing accordingly. This continues, with each target quantum state collected by the learner, the parameters of the next generated quantum circuit to be supplemented increase synchronously, and the circuit difficulty gradually increases with the collection progress, corresponding to the learner's ability growth rhythm.

[0083] Thus, in this embodiment, the binding mechanism between the target quantum state collection progress and circuit parameters enables a gradual increase in circuit construction difficulty. This ensures that the complexity of circuit practice matches the learner's real-time progress, avoiding frustration caused by excessive difficulty in the introductory stage while guaranteeing sufficient challenge in the advanced stage. The automatic parameter update mechanism eliminates the need for learners to manually adjust the difficulty, allowing the practice intensity to naturally increase along with the exploration process. This aligns with the step-by-step learning and cognitive patterns, helping learners gradually build their quantum circuit design capabilities. This mechanism, deeply integrated with the active circuit construction mode, optimizes the adaptability of practice without altering the original exploration process, exhibiting good functional compatibility and pedagogical practicality.

[0084] In some implementations, the line evaluation and execution module 500 is further configured as follows: Calculate the first degree of overlap between the final quantum state of the first target quantum circuit and the preset target quantum state, or calculate the second degree of overlap between the final quantum state of the second target quantum circuit and the preset target quantum state; Compare the first overlap or the second overlap with a preset threshold; If the first or second overlap is greater than or equal to a preset threshold, the validity assessment is determined to be passed, and at least one reward operation is performed, such as increasing the number of moves, increasing the superposition energy, or marking the target quantum state as one of the collected ones. If the first or second overlap is less than a preset threshold, the validity assessment is determined to be unsuccessful, and at least one penalty operation is performed, either by deducting the superposition state energy or resetting the target quantum state collection state.

[0085] Specifically, the embodiments of this application further refine the evaluation and feedback logic of the circuit evaluation and execution module 500. The circuit evaluation and execution module 500 is further configured to calculate a first overlap between the final quantum state of the first target quantum circuit and a preset target quantum state, or to calculate a second overlap between the final quantum state of the second target quantum circuit and a preset target quantum state. The circuit evaluation and execution module compares the first overlap or the second overlap with a preset threshold. If the first overlap or the second overlap is greater than or equal to the preset threshold, the validity evaluation is determined to be passed, and at least one reward operation is executed, such as increasing the number of moves, increasing the superposition energy, or marking the target quantum state as collected. If the first overlap or the second overlap is less than the preset threshold, the validity evaluation is determined to be failed, and at least one penalty operation is executed, such as deducting the superposition energy or resetting the target quantum state collection state.

[0086] In some implementations, overlap can be understood as a quantitative indicator of the similarity between two quantum states, used to measure the degree of matching between the circuit output and the target result. The formula for calculating overlap is as follows: .

[0087] In some implementations, the first overlap can be understood as the overlap value between the final quantum state output by the first target quantum circuit and the corresponding preset target quantum state in the active construction mode.

[0088] In some implementations, the second overlap can be understood as the overlap value between the final quantum state output by the second target quantum circuit and the corresponding preset target quantum state in passive construction mode.

[0089] In some implementations, the preset threshold can be understood as a pre-set evaluation threshold used to determine whether the circuit construction meets the valid standard. In some examples, the preset threshold can be dynamically adjusted according to the game difficulty level: 0.6 for easy difficulty, 0.75 for medium difficulty, and 0.9 for hard difficulty, matching the learning requirements of different stages.

[0090] In some implementations, reward operations can be understood as a set of operations that positively adjust game resources after a successful evaluation, providing positive feedback to the learner's correct actions. In some examples, the system employs a tiered reward and punishment mechanism, matching different reward levels based on the degree to which the overlap exceeds a threshold; the higher the overlap, the higher the corresponding number of moves and the higher the superposition state energy reward value.

[0091] In some implementations, penalty operations can be understood as a set of operations that negatively adjust game resources after an evaluation fails, used to alert the learner to deviations in their operations and guide them to correct their cognition. In some examples, penalty operations are also tiered according to the degree of overlap below a threshold; the greater the deviation, the higher the superposition state energy deduction value. When the deviation is small, only a slight energy deduction is performed, while when the deviation is large, the target quantum state collection state is simultaneously reset.

[0092] To more clearly illustrate the operation of the quantitative evaluation and feedback mechanism provided in this application's implementation, please refer to the following exemplary description: When the character moves to the target quantum state location, triggering active circuit construction, the learner supplements the quantum gates to form the first target quantum circuit and submits it. The circuit evaluation and execution module 500 calculates the first overlap between the final quantum state output by the first target quantum circuit and the corresponding preset target quantum state. If the calculated first overlap is 0.82 and the preset threshold is 0.7, then the first overlap is greater than the preset threshold, the circuit evaluation and execution module 500 determines that the validity evaluation has passed, executes the corresponding reward operation, increases the number of moves by 20 and the corresponding superposition energy, and marks the current target quantum state as collected. If the calculated first overlap is 0.52 and less than the preset threshold, then the validity evaluation has failed, the corresponding penalty operation is executed, deducts the corresponding superposition energy, and keeps the current target quantum state in an uncollected state.

[0093] When the character's move count is exhausted, triggering passive circuit construction, the learner completes the construction of the second target quantum circuit and submits it. The circuit evaluation and execution module 500 calculates the second overlap between the final quantum state output by the second target quantum circuit and the corresponding preset target quantum state. If the second overlap is greater than or equal to a preset threshold, the evaluation is deemed successful, the corresponding number of moves and superposition energy are replenished, and the character's movement permission is restored. If the second overlap is less than the preset threshold, the evaluation is deemed unsuccessful, the corresponding number of superposition energy is deducted, the character's movement is locked, and the learner waits to rebuild the circuit.

[0094] Thus, in this embodiment, the overlap degree can be used to quantitatively evaluate the route construction results, allowing learners to intuitively understand the degree of matching between their designed routes and the target results, facilitating the identification of deviations and gradual optimization of route design capabilities. A differentiated reward and punishment mechanism based on threshold judgment can provide corresponding feedback to different levels of completion, forming a tiered learning guidance and strengthening learning effectiveness. The evaluation results are deeply integrated with game resources, allowing learners to instantly perceive the impact of their actions and improving the effectiveness of learning feedback. This mechanism is fully compatible with the original active and passive route construction modes, improving the precision of the evaluation system without changing the basic interaction process, and possesses good functional adaptability and pedagogical value.

[0095] In some implementations, the system further includes a measurement challenge triggering module 700, which is configured to: When the character is in a superposition state and performs a wall-passing operation, a measurement challenge event is triggered according to a preset probability; In response to measurement challenges, it outputs quantum measurement questions for users to answer; If the correct answer is received, the character is allowed to pass through the wall. If an incorrect solution is received, the character exits the superposition state and the superposition state energy is deducted by a preset value.

[0096] Specifically, this application embodiment further adds a measurement challenge triggering mechanism. System 1000 also includes a measurement challenge triggering module 700, which triggers a measurement challenge event according to a preset probability when the character is in a superposition state and performs a wall-penetrating operation. In response to the measurement challenge event, the measurement challenge triggering module outputs a quantum measurement problem for the user to solve. If a correct solution is received, the character is allowed to complete the wall-penetrating operation. If an incorrect solution is received, the character is controlled to exit the superposition state and a preset value of superposition state energy is deducted.

[0097] In some implementations, the measurement challenge triggering module 700 can be understood as a functional unit responsible for monitoring the wall-penetrating operation and randomly initiating knowledge verification, in order to integrate the quantum measurement principle into the wall-penetrating interaction process.

[0098] In some implementations, the preset probability can be understood as a pre-set probability value for triggering the measurement challenge, used to simulate the random characteristics of quantum measurement. In some examples, the preset probability can be set to 30%, meaning that each time a character performs a wall-penetrating operation, there is a corresponding proportion of probability that the measurement challenge event will be triggered.

[0099] In some implementations, the measurement challenge event can be understood as a knowledge verification event triggered during the process of passing through the wall, which is used to pose questions related to quantum measurement to learners and test their mastery of related concepts.

[0100] In some implementations, the quantum measurement problem can be understood as a knowledge question set around the principles of quantum measurement, which may cover related knowledge points such as measurement collapse, measurement basis vectors, and probability distribution.

[0101] To more clearly illustrate the operation of the measurement challenge mechanism provided in this application's embodiments, please refer to the following exemplary description: During the game, the learner controls the character to activate a superposition state and move towards the wall, performing a wall-passing operation. The measurement challenge trigger module 700 monitors the trigger status of the wall-passing operation in real time and makes a random judgment according to a preset probability. If the judgment result is that the challenge is not triggered, the character successfully completes the wall-passing movement, the superposition state continues to operate, and energy is consumed normally. If the judgment result is that a measurement challenge event is triggered, the measurement challenge trigger module 700 pauses the wall-passing process, outputs quantum measurement-related questions on the interface, and waits for the learner to input the answer. If the learner submits a correct answer, the measurement challenge trigger module 700 determines that the answer is correct, resumes the wall-passing process, the character successfully passes through the wall, the superposition state remains unchanged, and the energy consumption of the corresponding wall-passing operation is deducted. If the learner submits an incorrect answer, the measurement challenge trigger module determines that the answer is incorrect, controls the character to exit the superposition state, returns to the normal state, and stays at the edge of the wall, while deducting a preset value of superposition state energy, and this wall-passing operation fails.

[0102] Thus, in this embodiment, the principle of state collapse caused by quantum measurement can be transformed into a perceptible game interaction through random measurement challenges during the wall-passing process. This helps learners intuitively understand the impact of measurement behavior on superposition states and establish a correspondence between quantum physics concepts and game operations. The probabilistic triggering design can simulate the random characteristics of quantum measurement, increase the strategic nature of using superposition states, guide learners to rationally plan the timing of wall-passing, and enhance the strategic depth of the game. Interspersed knowledge questions can strengthen the memory of quantum measurement-related knowledge points during exploration, enrich the teaching dimensions, and achieve a deep integration of exploration and knowledge practice. This mechanism does not require changes to the original maze exploration and route construction process and can be directly extended and implemented on the basis of the original system, possessing good functional compatibility and educational extension value.

[0103] In some embodiments, the educational game system 1000 further includes a dynamic difficulty adjustment module 800, which is configured as follows: Obtain the learner's current cumulative score and route construction success rate; If the current cumulative score and success rate meet the preset upgrade conditions, the game difficulty level will be increased; Based on the increased difficulty level, the wall distribution and portal network of the maze matrix are adaptively reconstructed, and at least one obstacle element from the observer role, quantum traps, or rotating walls is added to the maze matrix.

[0104] Specifically, this application further adds a dynamic difficulty adjustment mechanism. System 1000 also includes a dynamic difficulty adjustment module 800, which acquires the learner's current cumulative score and route construction success rate. If the current cumulative score and success rate meet preset upgrade conditions, the game difficulty level is increased. Based on the increased difficulty level, the wall distribution and portal network of the maze matrix are adaptively reconstructed, and at least one obstacle element from the observer role, quantum traps, or rotating walls is added to the maze matrix.

[0105] In some implementations, the dynamic difficulty adjustment module 800 can be understood as a functional unit responsible for monitoring learner performance and automatically adjusting game difficulty, undertaking related logic such as data statistics, condition judgment, and scene reconstruction. In some examples, the difficulty progression system dynamically adjusts the difficulty based on the learner's cumulative score and route construction success rate, covering three levels: easy, medium, and hard, and can automatically match the appropriate challenge intensity according to the learner's real-time performance.

[0106] In some implementations, the current cumulative score can be understood as the total score accumulated by the learner from the start of the game to the current moment, with the score coming from positive operations such as successfully constructing a route, collecting the target quantum state, and reaching the designated point.

[0107] In some implementations, the circuit construction success rate can be understood as the ratio of the cumulative number of successful circuit constructions by the learner to the total number of circuit constructions, which is used to reflect the learner's mastery of quantum circuit design.

[0108] In some implementations, the preset upgrade conditions can be understood as pre-defined rules for determining difficulty upgrades, including two indicators: a cumulative score threshold and a success rate threshold. A difficulty upgrade is triggered when both indicators are simultaneously met. In some examples, the preset upgrade conditions for upgrading from easy to medium difficulty are a current cumulative score of 150 points and a route construction success rate greater than or equal to 60%, while the preset upgrade conditions for upgrading from medium to hard difficulty are a current cumulative score of 400 points and a route construction success rate greater than or equal to 50%.

[0109] In some implementations, the game difficulty level can be understood as a graded indicator of the overall challenge intensity of the game, with different levels corresponding to different maze complexities, question difficulties, and obstacle element configurations.

[0110] In some implementations, adaptive refactoring can be understood as a process of regenerating the maze layout according to the new difficulty level, including rearranging the positions of walls and adjusting the placement and number of portals to ensure that the maze layout after each upgrade is different from the previous one.

[0111] In some implementations, quantum traps can be understood as obstacle elements placed within a maze grid. When a character triggers a trap, it will have a negative effect that deducts movement count or superposition energy, thus increasing the exploration challenge on difficult difficulty levels.

[0112] In some implementations, rotating walls can be understood as wall elements that can change their orientation at fixed intervals, which dynamically changes the path through the maze and increases the uncertainty of the exploration process.

[0113] To more clearly illustrate the operation of the dynamic difficulty adjustment mechanism provided in this application's implementation, please refer to the following exemplary description: The game initially runs on easy difficulty, with relatively sparse wall distribution and few obstacles in the maze matrix, making the route-building challenges relatively easy. The dynamic difficulty adjustment module continuously monitors the learner's cumulative score and route-building success rate. When the learner's cumulative score reaches 150 points and the route-building success rate reaches 64%, meeting the preset upgrade conditions for upgrading from easy to medium difficulty, the dynamic difficulty adjustment module 800 raises the game difficulty level to medium. After the difficulty increase, the dynamic difficulty adjustment module 800 regenerates the wall distribution of the maze matrix, adjusts the placement of portals, adds observer roles to the maze, and simultaneously increases the difficulty of the route-building challenges.

[0114] Learners continue the game. When their cumulative score reaches 400 points and their route construction success rate reaches 52%, meeting the preset upgrade conditions for upgrading from medium to hard difficulty, the dynamic difficulty adjustment module 800 raises the game's difficulty level to hard. After the difficulty increase, the dynamic difficulty adjustment module 8008 reconstructs the wall distribution and portal network of the maze matrix, further increasing the complexity of the maze. It also adds two new obstacle elements: quantum traps and rotating walls, and the difficulty of the route construction questions is correspondingly increased to a higher level. After the upgrade, learners continue exploring under the new maze layout and challenge configuration.

[0115] Thus, in this embodiment, the difficulty level can be automatically triggered based on the learner's real-time performance, achieving a dynamic match between the game's challenge intensity and the learner's skill development. This avoids the mismatch caused by a fixed difficulty level being too high or too low, ensuring a smooth and comfortable learning process. Each upgrade simultaneously reconstructs the maze layout, preventing learners from memorizing fixed paths, maintaining the freshness of the exploration process, and enhancing the game's continued appeal. The multi-dimensional difficulty upgrade design simultaneously covers multiple aspects such as maze structure, question difficulty, and obstacle elements, forming a three-dimensional challenge gradient that matches the learner's multi-dimensional skill development pace. This mechanism eliminates the need for learners to manually adjust the difficulty; it automatically adapts to the game's progress, resulting in a smoother user experience and improving teaching adaptability and learning efficiency.

[0116] Please see Figure 2 This application also provides a teaching interaction control method based on quantum computing principles. This teaching interaction control method can be applied to the teaching game system 1000 described in this application. Related explanations can be found in the corresponding content of the aforementioned teaching game system 1000, and will not be repeated below.

[0117] The interactive control method for instruction includes the following steps: 01: Generate a maze matrix including walls and resource items; 02: Control the character to dynamically switch between normal state and superposition state; 03: In response to the first trigger event when the character moves to the target quantum state position, generate a quantum circuit with missing quantum gates to be supplemented, and receive user input to construct the first target quantum circuit; 04: In response to the second trigger event where the character's movement count is exhausted, the character's movement is locked and a blank quantum circuit is generated. User input is received to construct the second target quantum circuit. 05: Evaluate the effectiveness of the first or second target quantum circuit and perform game resource adjustment operations corresponding to the resource item based on the evaluation results.

[0118] This application also provides an electronic device, which includes a memory and a processor. The interactive control method for teaching quantum encryption skills according to this application can be implemented by the electronic device according to this application. Specifically, the memory stores a computer program, and the processor is used to generate a maze matrix including walls and resource items, and to control the dynamic switching of the character between a normal state and a superposition state; and to generate a quantum circuit with missing quantum gates to be supplemented in response to a first trigger event in which the character moves to a target quantum state position, and to receive user input to construct a first target quantum circuit; and to lock the character's movement and generate a blank quantum circuit in response to a second trigger event in which the character's movement count is exhausted, and to receive user input to construct a second target quantum circuit; and to evaluate the effectiveness of the first target quantum circuit or the second target quantum circuit, and to perform game resource adjustment operations corresponding to the resource items according to the evaluation results.

[0119] Thus, in this embodiment, the concepts related to quantum computing can be transformed into operable game content through an interactive format combining maze exploration and route construction. This avoids entry barriers caused by insufficient prior knowledge and broadens the reach of quantum computing popular science education. The two route construction modes correspond to different learning stages, adaptable to learners with varying knowledge levels, and provide tiered, progressive learning guidance. The route construction results are directly linked to game resources, providing learners with immediate feedback, reinforcing learning effectiveness, and enhancing engagement and sustained motivation throughout the learning process.

[0120] In some implementations, the method further includes: Set up observer roles within the maze matrix; Under the preset difficulty level, and when the character in the superposition state and the observer character meet the preset spatial interference conditions, the character in control exits the superposition state and the superposition state energy of the preset value is deducted.

[0121] In some implementations, the processor is used to set an observer role in the maze matrix; and to control the role to exit the superposition state and deduct a preset value of superposition energy when the role in the superposition state and the observer role meet the preset spatial interference conditions at a preset difficulty level.

[0122] Thus, in this embodiment, the observer effect in quantum mechanics can be transformed into an operable game experience through the interaction mechanism between the observer role and the superposition state. This helps learners intuitively understand the principle that observation behavior triggers changes in the superposition state, supplementing the teaching dimension of the original system regarding quantum measurement concepts. The observer mechanism is directly linked to the difficulty level, enriching the game content at different difficulty levels, enhancing the strategy and challenge of advanced stages, guiding learners to rationally plan the timing of using the superposition state, and strengthening resource management awareness. This mechanism does not require modification of the original maze exploration and route construction process and can be directly extended and implemented on the basis of the original system, possessing good functional compatibility and iteration space.

[0123] In some implementations, the method further includes: Multiple sets of quantum entangled portal pairs are set up in the maze matrix. Each set of quantum entangled portal pairs includes two portals with the same color markings. In response to an event where a character enters the first portal and triggers a secondary confirmation command, update the character's position to the second portal, which has the same color identifier as the first portal.

[0124] In some implementations, the processor is configured to set multiple sets of quantum entangled portal pairs in a maze matrix, each set including two portals with the same color identifier; and to update the character's position to a second portal with the same color identifier as the first portal in response to an event in which a character enters the first portal and triggers a secondary confirmation command.

[0125] Thus, in this embodiment, the non-local correlation characteristics of quantum entanglement can be transformed into an interactive game through a paired, interconnected portal mechanism. This helps learners intuitively understand the spatial correspondence between two particles in an entangled system, supplementing the original system's quantum concept teaching dimension. The secondary confirmation mechanism effectively reduces accidental portal activation, ensuring a smooth exploration process and improving the user experience. The multiple portals enrich the maze's path selection space, allowing learners to plan the optimal path using route-building resources, enhancing the game's strategic depth and exploration enjoyment. This mechanism requires no modification to the original maze exploration and route-building processes and can be directly extended from the existing system, possessing good functional compatibility and iteration potential.

[0126] In some implementations, the method further includes: Set the initial number of qubits and the initial line depth of the quantum circuit to be supplemented; For each target quantum state collected, the number of qubits and the depth of the next generated quantum circuit to be supplemented are incremented by a preset unit, and the circuit construction parameters are updated.

[0127] In some implementations, the processor is used to set the initial number of qubits and the initial line depth of the quantum circuit to be supplemented, and to update the line construction parameters by incrementing the number of qubits and the line depth of the next generated quantum circuit by a preset unit each time a target quantum state is collected.

[0128] Thus, in this embodiment, the binding mechanism between the target quantum state collection progress and circuit parameters enables a gradual increase in circuit construction difficulty. This ensures that the complexity of circuit practice matches the learner's real-time progress, avoiding frustration caused by excessive difficulty in the introductory stage while guaranteeing sufficient challenge in the advanced stage. The automatic parameter update mechanism eliminates the need for learners to manually adjust the difficulty, allowing the practice intensity to naturally increase along with the exploration process. This aligns with the step-by-step learning and cognitive patterns, helping learners gradually build their quantum circuit design capabilities. This mechanism, deeply integrated with the active circuit construction mode, optimizes the adaptability of practice without altering the original exploration process, exhibiting good functional compatibility and pedagogical practicality.

[0129] In some implementations, step 05 above includes: Calculate the first degree of overlap between the final quantum state of the first target quantum circuit and the preset target quantum state, or calculate the second degree of overlap between the final quantum state of the second target quantum circuit and the preset target quantum state; Compare the first overlap or the second overlap with a preset threshold; If the first or second overlap is greater than or equal to a preset threshold, the validity assessment is determined to be passed, and at least one reward operation is performed, such as increasing the number of moves, increasing the superposition energy, or marking the target quantum state as one of the collected ones. If the first or second overlap is less than a preset threshold, the validity assessment is determined to be unsuccessful, and at least one penalty operation is performed, either by deducting the superposition state energy or resetting the target quantum state collection state.

[0130] In some embodiments, the processor is configured to calculate a first overlap between the final quantum state of a first target quantum circuit and a preset target quantum state, or to calculate a second overlap between the final quantum state of a second target quantum circuit and a preset target quantum state; and to compare the first overlap or the second overlap with a preset threshold; and to determine that the validity assessment has passed if the first overlap or the second overlap is greater than or equal to the preset threshold, and to perform at least one reward operation, such as increasing the number of moves, increasing the superposition energy, or marking the target quantum state as collected; and to determine that the validity assessment has failed if the first overlap or the second overlap is less than the preset threshold, and to perform at least one penalty operation, such as deducting the superposition energy or resetting the target quantum state collection state.

[0131] Thus, in this embodiment, the overlap degree can be used to quantitatively evaluate the route construction results, allowing learners to intuitively understand the degree of matching between their designed routes and the target results, facilitating the identification of deviations and gradual optimization of route design capabilities. A differentiated reward and punishment mechanism based on threshold judgment can provide corresponding feedback to different levels of completion, forming a tiered learning guidance and strengthening learning effectiveness. The evaluation results are deeply integrated with game resources, allowing learners to instantly perceive the impact of their actions and improving the effectiveness of learning feedback. This mechanism is fully compatible with the original active and passive route construction modes, improving the precision of the evaluation system without changing the basic interaction process, and possesses good functional adaptability and pedagogical value.

[0132] In some implementations, the method further includes: When the character is in a superposition state and performs a wall-passing operation, a measurement challenge event is triggered according to a preset probability; In response to measurement challenges, it outputs quantum measurement questions for users to answer; If the correct answer is received, the character is allowed to pass through the wall. If an incorrect solution is received, the character exits the superposition state and the superposition state energy is deducted by a preset value.

[0133] In some implementations, the processor is configured to trigger a measurement challenge event according to a preset probability when the character is in a superposition state and performs a wall-passing operation; and to output a quantum measurement question for the user to answer in response to the measurement challenge event; and to allow the character to complete the wall-passing operation if a correct answer is received; and to control the character to exit the superposition state and deduct a preset value of superposition energy if an incorrect answer is received.

[0134] Thus, in this embodiment, the principle of state collapse caused by quantum measurement can be transformed into a perceptible game interaction through random measurement challenges during the wall-passing process. This helps learners intuitively understand the impact of measurement behavior on superposition states and establish a correspondence between quantum physics concepts and game operations. The probabilistic triggering design can simulate the random characteristics of quantum measurement, increase the strategic nature of using superposition states, guide learners to rationally plan the timing of wall-passing, and enhance the strategic depth of the game. Interspersed knowledge questions can strengthen the memory of quantum measurement-related knowledge points during exploration, enrich the teaching dimensions, and achieve a deep integration of exploration and knowledge practice. This mechanism does not require changes to the original maze exploration and route construction process and can be directly extended and implemented on the basis of the original system, possessing good functional compatibility and educational extension value.

[0135] In some implementations, the method further includes: Obtain the learner's current cumulative score and route construction success rate; If the current cumulative score and success rate meet the preset upgrade conditions, the game difficulty level will be increased; Based on the increased difficulty level, the wall distribution and portal network of the maze matrix are adaptively reconstructed, and at least one obstacle element from the observer role, quantum traps, or rotating walls is added to the maze matrix.

[0136] In some implementations, the processor is used to obtain the learner's current cumulative score and route construction success rate; and to increase the game difficulty level if the current cumulative score and success rate meet preset upgrade conditions; and to adaptively reconstruct the wall distribution and portal network of the maze matrix based on the increased difficulty level, and to add at least one obstacle element among observer roles, quantum traps, or rotating walls to the maze matrix.

[0137] Thus, in this embodiment, the difficulty level can be automatically triggered based on the learner's real-time performance, achieving a dynamic match between the game's challenge intensity and the learner's skill development. This avoids the mismatch caused by a fixed difficulty level being too high or too low, ensuring a smooth and comfortable learning process. Each upgrade simultaneously reconstructs the maze layout, preventing learners from memorizing fixed paths, maintaining the freshness of the exploration process, and enhancing the game's continued appeal. The multi-dimensional difficulty upgrade design simultaneously covers multiple aspects such as maze structure, question difficulty, and obstacle elements, forming a three-dimensional challenge gradient that matches the learner's multi-dimensional skill development pace. This mechanism eliminates the need for learners to manually adjust the difficulty; it automatically adapts to the game's progress, resulting in a smoother user experience and improving teaching adaptability and learning efficiency.

[0138] The following is a complete embodiment to explain the operation process of the quantum computing teaching game according to the present application: The educational game system described in this application covers five main usage stages: initial configuration, maze exploration, route construction practice, dynamic difficulty progression, and level completion settlement. Please refer to [link / reference]. Figure 3 The interface layout, featuring a maze-like interactive area on the left and a status information panel on the right, provides learners with a complete quantum computing enlightenment experience, from basic state cognition to circuit design practice.

[0139] 10 generated during the initial configuration phase The maze is a 10-grid matrix, with dark grids representing walls and white grids representing passable paths. A green exit point is located in the upper left corner. Inside the maze, eight randomly numbered yellow circular points represent target quantum states, corresponding to eight different learning nodes. Multiple blue rhombus-shaped points, representing energy spheres, are scattered throughout the maze grid to replenish superposition energy. Purple square points, marked with numbers, are also present; these are quantum gate items, including various types. Collecting them extends the duration of the superposition state and expands the set of usable gates for route construction. A red circular character is positioned slightly below the center of the maze. This character is currently in a normal state, restricted by the wall's collision rules and can only move along the white passable grid. The right side of the interface features a status information panel that displays, from top to bottom, the remaining game time, target quantum state collection progress, current cumulative score, remaining moves, superposition energy value, cumulative wall-passing attempts, and current difficulty level. Currently, the remaining game time is 288 seconds, target quantum state collection progress is 0 / 8, current cumulative score is 0, remaining moves are 50, superposition energy value is 50, cumulative wall-passing attempts are 0, and the current difficulty level is Easy. Below the panel are areas displaying collected target quantum states, recent event logs, and operation instructions. The instructions indicate that the arrow keys control character movement, the spacebar activates the superposition state, and the R key restarts the game.

[0140] During the maze exploration phase, learners control their character using the arrow keys to move within passable grids, consuming one remaining move attempt for each grid move. Pressing the spacebar switches the character to a superposition state, allowing them to pass through wall grids while continuously consuming superposition energy. When superposition energy is depleted, the character automatically reverts to the normal state. Moving to a blue energy orb automatically replenishes superposition energy, extending the duration of superposition. While in superposition and performing a wall-passing operation, the system triggers a measurement challenge event with a preset probability, displaying quantum measurement-related questions for learners to answer. A correct answer results in successful wall-passing, maintaining the superposition state. An incorrect answer results in exiting superposition and deducting the corresponding amount of superposition energy, invalidating the wall-passing operation. On easy difficulty, the maze contains no additional obstacles, allowing learners to focus on familiarizing themselves with the movement rules and state switching logic, gradually building an intuitive understanding of superposition concepts. Multiple pairs of quantum entangled portals can be set up within the maze. Each pair consists of two portals marked with the same color. When a character enters a portal and triggers a secondary confirmation command, they can jump to the location of the other portal in the same pair, expanding the path selection space. When a character moves to a numbered yellow target quantum state point, an active path construction process is triggered. When all remaining moves are used up, a passive path construction process is triggered.

[0141] The route construction practice phase includes two modes: active construction and passive construction.

[0142] Please see Figure 4 The active route construction mode is triggered by the first event when the character comes into contact with the target quantum state. The system then switches the game interface to the active route construction running interface. The top of the interface indicates the current route construction running mode, and the upper left corner displays the target quantum state corresponding to this exercise. The corresponding target quantum state is target quantum state number 3, specifically in the form 0.71|0. +0.71|1 The target quantum state, also known as the preset target quantum state, serves as the reference benchmark for subsequent circuit effectiveness evaluation. Below the target quantum state are the basic configuration parameters of the circuit, including the number of qubits and the circuit depth. Currently, the number of qubits is 2, and the circuit depth is 2. These parameters, which are also the circuit construction parameters, determine the overall scale of the quantum circuit to be supplemented where some quantum gates are missing. Below the parameters is the circuit editing grid, horizontally divided into two time steps and vertically corresponding to two qubits. Each grid cell is used to place different types of quantum gates. The first time step position corresponding to the first qubit is marked with a question mark, indicating a missing quantum gate in the circuit to be supplemented. Learners need to select the appropriate quantum gate to fill the corresponding position. The upper right corner of the interface features an area for selecting available quantum gates, including H-gates, X-gates, Z-gates, and RX-gates. Learners can select the corresponding quantum gate to fill the missing position in the circuit and complete the construction of the first target quantum circuit. The lower right corner of the interface has a submission control. After confirming the circuit structure, learners can click the submission control to submit the first target quantum circuit and trigger the effectiveness evaluation process.

[0143] The passive construction mode is triggered by the second event when the number of moves is exhausted. The system temporarily locks the character's movement and generates a blank quantum circuit for the learner to build from scratch. After submission, the second target quantum circuit is formed.

[0144] The system evaluates the effectiveness of the target quantum circuits submitted by learners and calculates the overlap between the final quantum state output by the circuit and the preset target quantum state.

[0145] The system evaluates the effectiveness of the target quantum circuits submitted by learners, calculating the overlap between the final quantum state output by the circuit and the preset target quantum state. The system compares the overlap value with a preset threshold. If the overlap is greater than or equal to the preset threshold, the evaluation is considered successful, and corresponding reward actions are executed, including increasing the number of moves, increasing the superposition energy, and marking the current target quantum state as collected. If the overlap is less than the preset threshold, the evaluation is considered unsuccessful, and corresponding penalty actions are executed, including deducting the superposition energy and resetting the target quantum state collection status. After the evaluation is completed, the system automatically returns to the maze exploration interface, synchronously updates the parameter values ​​on the right panel, restores the character's movement permissions, and the game continues. Each time a target quantum state is collected, when the system generates the next quantum circuit to be supplemented, it synchronously increases the number of qubits and the circuit depth by a preset unit, gradually increasing the difficulty of circuit construction practice.

[0146] In the dynamic difficulty progression stage, the system continuously monitors the learner's current cumulative score and route construction success rate. When both indicators simultaneously meet the preset upgrade conditions, the game difficulty level is automatically increased. The conditions for upgrading from easy to medium difficulty are a cumulative score of 150 points and a route construction success rate of 60% or higher. After the upgrade, the system reconstructs the wall distribution and portal network of the maze matrix using a maze topology adaptive algorithm. An observer role is added to the maze, and the difficulty of route construction questions increases accordingly, while simultaneously compressing the available time for a single game, intensifying the challenge pressure in the time constraint dimension. At medium difficulty, if a character in a superposition state enters the observer's effective field of vision, they will automatically exit the superposition state and have the corresponding superposition state energy deducted, guiding learners to rationally plan the timing of using superposition states. The conditions for upgrading from medium to hard difficulty are a cumulative score of 300 points and a route construction success rate of 50% or higher. After the upgrade, the system reconstructs the maze layout again, further increasing wall density and path complexity, while adding two types of obstacle elements: quantum traps and rotating walls. The parameter thresholds for route construction are correspondingly increased, the available time is further shortened, and the time pressure continues to increase. Each time the difficulty level increases, the maze layout is regenerated to prevent learners from memorizing fixed paths and to maintain the freshness and challenge of the exploration process.

[0147] During the level completion and settlement phase, once the learner has collected all 8 target quantum states and reached the green exit point, the system executes a final verification process, calculating the overlap between the current quantum state and the random target state. An overlap greater than or equal to 0.9 is sufficient for successful level completion. If the learner runs out of moves and fails to pass the evaluation for three consecutive passive route construction attempts, the system determines the game as a failure, settles the current progress and learning data, and ends the current game. After completion, the system generates a game summary panel, displaying statistics such as total time, final score, route construction success rate, cumulative wall-crossing attempts, and number of collected quantum states. It also marks performance data for each difficulty level, helping learners review their knowledge mastery. The system allows learners to restart the game for a new round of practice. Each game's maze layout and quantum route questions are randomly generated, ensuring a rich variety of practice content. Learners' game process data and route construction performance can be synchronously recorded locally for reviewing learning progress and skill development, and can also serve as a basis for evaluating learning effectiveness in teaching scenarios.

[0148] This application also provides a computer-readable storage medium storing a computer program that, when executed by one or more processors, implements the method described above.

[0149] This application also provides a computer program product, including a computer program / instructions that, when executed by a processor, implement the method described above.

[0150] It is understood that a computer program includes computer program code. Computer program code can be in the form of source code, object code, executable files, or some intermediate form. Computer-readable storage media can include: any entity or device capable of carrying computer program code, recording media, USB flash drives, portable hard drives, magnetic disks, optical disks, computer memory, read-only memory (ROM), random access memory (RAM), and software distribution media, etc.

[0151] In this specification, the terms "specifically," "furthermore," "particularly," "understandably," etc., refer to specific features, structures, materials, or characteristics described in connection with embodiments or examples that are included in at least one embodiment or example of this application. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples. Moreover, without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described in this specification, as well as the features of different embodiments or examples.

[0152] Any process or method description in the flowchart or otherwise herein can be understood as representing a module, segment, or portion of executable request code comprising one or more steps for implementing a particular logical function or process, and the scope of the preferred embodiments of this application includes additional implementations in which functions may be performed not in the order shown or discussed, including substantially simultaneously or in reverse order according to the functions involved, as should be understood by those skilled in the art to which embodiments of this application pertain.

[0153] Although embodiments of this application have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting this application. Those skilled in the art can make changes, modifications, substitutions and variations to the above embodiments within the scope of this application.

Claims

1. A teaching game system based on quantum computing principles, characterized in that, include: A maze generation module is configured to generate a maze matrix including walls and resource items, wherein the resource items include target quantum state and superposition state energy; The character state management module is configured to control the dynamic switching of the character between a normal state and a superimposed state. When the character is in the normal state, it is restricted by the entity collision rules of the wall. When it is in the superimposed state, it can pass through the wall and continuously consume the superimposed state energy. An active circuit construction module is configured to generate a quantum circuit to be supplemented with some missing quantum gates in response to a first trigger event in which the character moves to the location of the target quantum state, and to receive user input to construct the first target quantum circuit. The passive circuit construction module is configured to, in response to a second trigger event that the character's movement count has been exhausted, lock the character's movement and generate a blank quantum circuit, and receive user input to construct a second target quantum circuit; The circuit evaluation and execution module is configured to evaluate the effectiveness of the first target quantum circuit or the second target quantum circuit, and perform game resource adjustment operations corresponding to the resource item based on the evaluation results.

2. The system according to claim 1, characterized in that, The role status management module is also configured as follows: Set the observer role in the maze matrix; The system also includes an observer interference module, which is configured as follows: Under a preset difficulty level, and when the character in the superposition state and the observer character meet preset spatial interference conditions, the character is controlled to exit the superposition state and a preset value of superposition state energy is deducted.

3. The system according to claim 1, characterized in that, The maze generation module is also configured to: Multiple sets of quantum entangled portal pairs are set in the maze matrix, and each set of quantum entangled portal pairs includes two portals with the same color markings; The role status management module is also configured as follows: In response to the event that the character enters the first portal and triggers a secondary confirmation command, the character's position is updated to a second portal with the same color as the first portal. The first portal is one of the portals in any of the quantum entangled portal pairs that the character enters, and the second portal is another portal in the quantum entangled portal pair to which the first portal belongs.

4. The system according to claim 1, characterized in that, The active route construction module is further configured as follows: Set the initial number of qubits and the initial line depth of the quantum circuit to be supplemented; For each target quantum state collected, the number of qubits and the depth of the next generated quantum circuit to be supplemented are incremented by a preset unit, and the circuit construction parameters are updated.

5. The system according to claim 1, characterized in that, The line evaluation and execution module is further configured as follows: Calculate the first degree of overlap between the final quantum state of the first target quantum circuit and the preset target quantum state, or calculate the second degree of overlap between the final quantum state of the second target quantum circuit and the preset target quantum state; Compare the first overlap or the second overlap with a preset threshold; If the first overlap or the second overlap is greater than or equal to the preset threshold, the validity assessment is determined to be passed, and at least one reward operation is performed, such as increasing the number of moves, increasing the superposition energy, or marking the target quantum state as one of the collected ones. If either the first overlap or the second overlap is less than the preset threshold, the validity assessment is determined to be unsuccessful, and at least one penalty operation is performed, either by deducting the superposition state energy or resetting the target quantum state collection state.

6. The system according to claim 1, characterized in that, The system also includes a measurement challenge triggering module, which is configured as follows: When the character is in the superposition state and performs a wall-penetrating operation, a measurement challenge event is triggered according to a preset probability; In response to the aforementioned measurement challenge event, a quantum measurement question is output for the user to answer; If the correct answer is received, the character is allowed to complete the wall-penetrating operation; If an incorrect solution is received, the character is controlled to exit the superposition state and the superposition state energy of a preset value is deducted.

7. The system according to claim 1, characterized in that, The system also includes a dynamic difficulty adjustment module, which is configured as follows: Obtain the learner's current cumulative score and route construction success rate; If the current cumulative score and the success rate meet the preset upgrade conditions, the game difficulty level will be increased; Based on the increased difficulty level, the wall distribution and portal network of the maze matrix are adaptively reconstructed, and at least one obstacle element from the categories of observer role, quantum trap, or rotating wall is added to the maze matrix.

8. A teaching interaction control method based on quantum computing principles, used in the teaching game system as described in any one of claims 1-7, characterized in that, include: Generate a maze matrix including walls and resource items, wherein the resource items include the target quantum state and superposition state energy; The character is controlled to dynamically switch between a normal state and a superposition state. When the character is in the normal state, it is restricted by the entity collision rules of the wall. When the character is in the superposition state, it can pass through the wall and continuously consume the superposition state energy. In response to a first triggering event where the character moves to the location of the target quantum state, a quantum circuit with missing quantum gates is generated to be supplemented, and user input is received to construct the first target quantum circuit; In response to a second trigger event that the character has exhausted its move count, the character's movement is locked and a blank quantum circuit is generated, and user input is received to construct a second target quantum circuit; The effectiveness of the first target quantum circuit or the second target quantum circuit is evaluated, and the game resource adjustment operation corresponding to the resource item is executed according to the evaluation result.

9. The method according to claim 8, characterized in that, The step of evaluating the effectiveness of the first target quantum circuit or the second target quantum circuit, and performing game resource adjustment operations corresponding to the resource item based on the evaluation results, includes: Calculate the first degree of overlap between the final quantum state of the first target quantum circuit and the preset target quantum state, or calculate the second degree of overlap between the final quantum state of the second target quantum circuit and the preset target quantum state; Compare the first overlap or the second overlap with a preset threshold; If the first overlap or the second overlap is greater than or equal to the preset threshold, the validity assessment is determined to be passed, and at least one reward operation is performed, such as increasing the number of moves, increasing the superposition energy, or marking the target quantum state as one of the collected ones. If either the first overlap or the second overlap is less than the preset threshold, the validity assessment is determined to be unsuccessful, and at least one penalty operation is performed, either by deducting the superposition state energy or resetting the target quantum state collection state.

10. An electronic device, characterized in that, It includes a memory and a processor, wherein the memory stores a computer program, which, when executed by the processor, implements the method of claim 8 or 9.

11. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that, when executed by one or more processors, implements the method of claim 8 or 9.

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