CN115660091A - Method for bit quantum overturning early warning and threshold setting of quantum computer - Google Patents
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Abstract
The invention discloses a bit quantum turning early warning and threshold setting method for a quantum computer, which comprises the steps of setting a quantum bit turning initial threshold of the quantum computer based on a quantum turning prediction model; dynamically setting a quantum turnover threshold by adopting a threshold prediction model, and realizing quantum turnover early warning and initial threshold substitution; and when the historical database quantum turnover threshold value is used once by the threshold value prediction model according with the threshold value predicted by the quantum turnover prediction model, a threshold value adjustment model is constructed, and a new dynamic threshold value is generated to replace the current threshold value. The invention adopts a Bayesian threshold prediction model to adjust the monitoring threshold to solve the problem that the quantum inversion prediction model steady-state distribution method based on the Markov chain cannot realize the calculation of the continuous time Markov chain, thereby causing the prediction distortion of the network early warning model.
Description
Technical Field
The invention belongs to the technical field of quantum computers, and particularly relates to a bit quantum turning early warning and threshold setting method for a quantum computer.
Background
With the continuous development of the field of big data, more and more concepts are proposed and applied to production and quantum computers can be used for searching a large amount of data. Assuming that a traditional computer needs to execute tens of millions of instructions to complete a large data search, the efficiency is extremely low. Quantum computers often need to execute thousands of instructions to complete the same task, thereby bringing new revolutionary changes to the big data industry. The quantum computer is a machine capable of realizing quantum computation, and realizes mathematical and logical operations, processes and stores information through quantum mechanical laws. The quantum state is used as a memory unit and an information storage form, quantum dynamics evolution is used as quantum communication and quantum computation based on information transmission and processing, and the sizes of various elements of hardware in a quantum computer reach the magnitude of atoms or molecules. A quantum computer is a physical system that can store and process information represented by quantum bits.
So far, quantum systems do not have any unwanted interaction with the outside world, i.e. almost exclusively deal with the dynamics of closed quantum systems. Although it is possible to draw an attractive conclusion on the information processing tasks that can in principle be achieved in this ideal system, this observation is influenced by the fact that there are no completely closed systems in the real world, except for the entire universe. Real systems suffer from unwanted interactions with the outside world. These unwanted interactions appear as noise in quantum information processing systems.
Disclosure of Invention
The technical problem to be solved by the present invention is to provide a method for bit quantum flipping warning and threshold setting of a quantum computer, where a bit of the quantum computer is on a disk memory, the bit starts from state 0 or 1, but after a long period of time, a scattered magnetic field may possibly cause the bit to be disturbed, and the state may possibly be flipped.
In order to achieve the technical purpose, the technical scheme adopted by the invention is as follows:
a quantum computer bit quantum turn-over early warning and threshold setting method comprises the following steps:
step one, setting a quantum bit flipping initial threshold of a quantum computer based on a quantum flipping prediction model;
step two, dynamically setting a quantum turnover threshold value by adopting a threshold value prediction model, and realizing quantum turnover early warning and initial threshold value substitution;
and step three, after the threshold prediction model uses the thresholds of the historical database, the quantum turnover threshold of which accords with the quantum turnover prediction model, a threshold adjustment model is constructed, and a new dynamic threshold is generated to replace the current threshold.
In order to optimize the technical scheme, the specific measures adopted further comprise:
the first step comprises:
firstly, a plurality of test bits are created aiming at a target bit, and each test bit has the superposition characteristic and the state before inversion of the target bit;
secondly, constructing a quantum inversion prediction model, performing inversion prediction by using bit quanta of different numbers of test bits, performing quantum inversion prediction model analysis by using stored historical bit inversion data, and performing inversion prediction on the quantum bits to obtain prediction probability;
and determining whether the target bit is turned or not according to the prediction probability, and performing weighted average on the multiple groups of data probability values to obtain a more optimal turning probability serving as a quantum bit turning threshold, namely the quantum bit turning initial threshold of the quantum computer.
The quantum inversion prediction model formula is as follows: x (k + 1) = X (k) × P
In the formula: x (k) represents a state vector of the trend analysis and prediction object at the time t = k, P represents a one-step transition probability matrix, and X (k + 1) represents a state vector of the trend analysis and prediction object at the time t = k + 1.
And step two, establishing a threshold prediction model by adopting Bayes.
And step two, putting each threshold which is successfully overturned in the historical database quantum overturning thresholds except the current quantum bit into a Bayesian threshold prediction model one by one to obtain threshold use probability, and replacing the initial threshold in use after the threshold with the maximum value is taken as the stable state distribution of the Markov chain network early warning model to finish the continuous operation of the threshold prediction model.
And thirdly, constructing a threshold adjustment model by using a ridge regression method.
Generating a new dynamic threshold to replace the current threshold in the third step includes:
performing analog turnover test on ten groups of quantum bits with different numbers, setting an initial threshold value of each group as a threshold value with the current threshold value floating up and down by 10%, forming a threshold value interval with fluctuation up and down of 20%, and dividing the interval into 10 parts by 2%;
obtaining historical data by taking each current threshold plus or minus 2% of threshold as a condition, combining the historical data with the threshold adjustment model to calculate to obtain a drilling fitting value, then performing difference calculation with the actual fitting value obtained by combining the historical data which is subjected to last quantum bit flipping with the threshold adjustment model, and identifying the group of data threshold values of which the difference value between the drilling fitting value and the actual fitting value is greater than 10%;
and sequentially carrying out model training and difference comparison on 10 groups of different threshold data, marking the threshold, and carrying out weighted average on a plurality of marked thresholds to generate a new dynamic threshold to replace the current threshold.
The invention has the following beneficial effects:
the invention highlights the setting status of artificial intelligence on the quantum computer threshold, and creatively adopts a Bayesian threshold prediction model to adjust the monitoring threshold to solve the problem that the continuous time Markov chain-based method for quantum inversion prediction model steady-state distribution cannot realize the calculation of the continuous time Markov chain, thereby causing the prediction distortion of the network early warning model.
The quantum bit flipping is comprehensively analyzed and predicted from the aspect of vertical and horizontal by using the Markov chain and the Bayesian network, and the defect of the Markov chain in the aspect of processing the upper-layer index loss is innovatively processed. Markov chains are methods for exploring the probability distributions of variables over future times, as determined by a sample, and are a method of vertical prediction. The Bayesian quantum bit flipping threshold shows the interaction relation between the flipping prediction and the threshold index, and is a transverse prediction method. The combination of the two methods has an advantage that the problem of non-bottom layer index data shortage of a multi-layer index system can be solved, so as to realize quantum bit inversion prediction in a macroscopic sense. The reverse-deducing function of the Bayes quantum bit reversal threshold value also provides a basis for network fault risk control.
In addition, when the threshold predicted by the back-stepping function of the Bayes quantum bit flipping threshold reaches the threshold with no available threshold, a smooth separation situation is prevented from occurring. And establishing a threshold fluctuation interval, and generating a new dynamic threshold as a standby threshold when no threshold is available after weighted averaging of a test fitting value and an actual fitting value is obtained according to a training result of a threshold adjustment model established by a ridge regression method.
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FIG. 1 is a schematic diagram of the method of the present invention.
Detailed Description
Embodiments of the present invention are described in further detail below with reference to the accompanying drawings.
As shown in fig. 1, a method for bit quantum rollover warning and threshold setting of a quantum computer includes:
step one, setting a quantum bit flipping initial threshold of a quantum computer based on a quantum flipping prediction model;
first, a plurality of test bits are created for the target bits. Each test bit has the superposition characteristic and the pre-inversion state of the target bit. And secondly, constructing a quantum roll-over prediction model, respectively carrying out roll-over prediction by using bit quanta of different numbers of test bits, analyzing the quantum roll-over prediction model by using stored historical bit roll-over data, carrying out roll-over prediction on the quantum bits, and acquiring the final state of the target bits according to the predicted probability, namely whether the large probability is roll-over or not. And carrying out weighted average on the multiple groups of data test probability values to obtain a more optimal turnover probability as a bit quantum turnover threshold value which is used as a bit turnover initial threshold value of the quantum computer.
Quantum inversion prediction model formula: x (k + 1) = X (k) × P
In the formula: x (k) represents a state vector of the trend analysis and prediction object at the time t = k, P represents a one-step transition probability matrix, and X (k + 1) represents a state vector of the trend analysis and prediction object at the time t = k + 1.
Historical bit flipping initial transition probabilities [ 0.3, 0.7 ]
The probability of state transition (0.6, 0.4) occurs when the current bit is turned from 0 to 1
The probability of state transition (0.3, 0.7) when the current bit is turned from 1 to 0
The set of rectangles is:
the calculation of the model shows that X (k + 1) = X (k) multiplied by P
Lower period threshold unchanged qubit inversion occurs with probability of 0.3x0.6+0.3x0.7=0.39 from 0 revolution to 1
Probability of occurrence of 0.3x0.4+0.7x0.7=0.61 for lower period threshold invariant qubit flipping from 1 to 0
Probability of bit flipping occurring [ 0.39.0.61 ] under the condition of constant lower period threshold
Step two, dynamically setting a quantum turnover threshold by adopting a threshold prediction model, and realizing quantum turnover early warning and initial threshold substitution;
aiming at the problem that the distortion of the turnover prediction probability is stably issued and the quantum turnover is repaired, a quantum turnover threshold value is dynamically set so as to replace the initial threshold value.
Bayes (threshold prediction model) construction
Firstly, accessing a historical alarm database for combined analysis to obtain model parameters (prior probability) (conditional probability) (adjustment factor).
Secondly, putting the prior probability condition probability regulating factor into the model operation. After the operation result is taken as the stable state distribution of the Markov chain (quantum flipping prediction model) in the next period, the flipping and non-flipping proportion is continuously similar to (0.50.5) in numerical value and is continuously the same. And thus the computation of a continuous-time markov chain cannot be achieved. Therefore, threshold value change is carried out on the number of successful test quantum roll-over by adjustment so as to generate new unstable and unchangeable test quantum roll-over data, and the initial threshold value is adjusted so as to obtain a roll-over threshold value which is more in line with the current service characteristics by combining Bayes (threshold value prediction model) analysis historical data.
Model formulas and examples:
P(A|B)＝(P(B|A)*P(A))/P(B|A)P(A)+P(B|A')P(A')
-prior probability = P (a) [ conditional probability ] = P (B) [ adjustment factor ] = P (B | a) x P (a)
P (a) is the total number of faults/total number of historical faults using the current threshold, ignoring other factors such as: 40 percent;
p (A') 1-P (A), here 60%;
p (B | a) [ network early warning model ] is the probability of the total number of results of the current threshold/threshold database once used several times in the continuous learning process, which is 50% here;
the probability that the threshold value of the P (B | A') threshold value database appears in the historical fault database is 100% if the historical threshold values are all applied in the historical fault database;
p (B) is a probability formula directly considering threshold value to use by ignoring other factors
P (B) = P (B | a) P (a) + P (B | a ') P (a'), here 0.5 × 0.4+1 × 0.6=0.8;
then it can be calculated according to the Bayesian formula, i.e., it is calculated
P(A|B)＝(0.5*0.4)/(0.8)＝0.25
And (3) putting each threshold value which is successfully overturned in the historical database quantum overturning threshold values except the current quantum bit use into Bayes (threshold value prediction model) one by one to obtain threshold value use probability, and after the threshold value with the maximum numerical value is used as a Markov chain (network early warning model) to be distributed in a stable state, the calculation of the continuous time Markov chain cannot be realized. Replacing the initial threshold in use. And (4) finishing the continuous operation of the threshold prediction model.
And step three, after the threshold prediction model uses the thresholds of the historical database, the quantum turnover threshold of which accords with the quantum turnover prediction model, a threshold adjustment model is constructed, and a new dynamic threshold is generated to replace the current threshold.
A ridge regression method is adopted to construct a threshold adjustment model, ten groups of quanta with different numbers are subjected to simulation overturning testing, an initial threshold is set in each group to be a threshold with the current threshold floating up and down by 10%, a 20% up-down fluctuation threshold interval is formed, and the interval is divided into 10 parts by 2%. And obtaining a drilling fitting value by combining historical data obtained by taking each current threshold plus or minus 2% of the threshold as a condition and calculating the drilling fitting value with the threshold adjusting model, and then performing difference calculation on the drilling fitting value and an actual fitting value obtained by combining the drilling fitting value and the data of the latest quantum bit inversion in the history with the threshold adjusting model, wherein the difference between the drilling fitting value and the actual fitting value is greater than 10%, and the threshold fluctuation possibility of the drilling fitting value and the actual fitting value is high. The set of data thresholds is identified and is not operated. And sequentially carrying out model training and difference comparison on 10 groups of different threshold data, and marking the threshold. And after the multiple marking threshold values are weighted and averaged, generating a new dynamic threshold value to replace the current threshold value.
[ threshold adjustment model ] formula: | | X theta-y | charging 2 +||Γθ|| 2
The over-fitting prevention operation formula is as follows: θ (a) = (X) T X+aI) -1 X T y
Wherein X represents an input; y represents the output prediction result; | | represents a canonical operation; i represents an identity matrix; theta is a fitting hyper-parameter; Γ is a weight constant; a is the weight of the identity matrix; θ (a) represents the value of θ obtained when a is determined.
The quantum flipping process example is as follows:
the states of a single qubit of 30% 0 and 70% 1 can be dispersed into three qubits, so that as a population, the qubits are in such a state: 30% of all three qubits are 0 and 70% are 1. This larger but equally efficient quantum state helps researchers correct errors.
These qubits can be connected by two gates in the quantum circuit. One gate checks the "parity" of the first and second physical bits (note: parity here means whether the two bits are in the same state, e.g., 00, 11, parity is the same, 01, 10 are different) — the same or different — the other gate checks the "parity" of the first and third physical bits. In the absence of an error (i.e., qubits in superposition state |000> + |111 >), the gate measuring parity tells that parity for both two and one three bits is the same. However, if the first physical bit is inadvertently flipped, causing the system state to become |100> + |011>, the two gates will detect that the parity of the two pairs of qubits is different. If the second qubit is flipped, resulting in a system state of |010> + |101>, the gate of the parity measurement finds that two parity are different, but one parity is the same. Similarly, if the third qubit is inverted, the detection results are one and two identical, and one and three different. This unique result tells what corrective measures should be taken to flip back the first second or third qubits if necessary without collapsing the entire logical qubit.
The above is only a preferred embodiment of the present invention, and the protection scope of the present invention is not limited to the above-mentioned embodiments, and all technical solutions belonging to the idea of the present invention belong to the protection scope of the present invention. It should be noted that modifications and adaptations to those skilled in the art without departing from the principles of the present invention may be apparent to those skilled in the relevant art and are intended to be within the scope of the present invention.
Claims (7)
1. A method for quantum computer bit quantum turn-over early warning and threshold setting is characterized by comprising the following steps:
step one, setting a quantum bit flipping initial threshold of a quantum computer based on a quantum flipping prediction model;
step two, dynamically setting a quantum turnover threshold value by adopting a threshold value prediction model, and realizing quantum turnover early warning and initial threshold value substitution;
and step three, after the threshold prediction model uses the thresholds of the historical database, the quantum turnover threshold of which accords with the quantum turnover prediction model, a threshold adjustment model is constructed, and a new dynamic threshold is generated to replace the current threshold.
2. The method for quantum computer bit quantum rollover warning and threshold setting according to claim 1, wherein the first step comprises:
firstly, a plurality of test bits are created aiming at a target bit, and each test bit has the superposition characteristic and the state before inversion of the target bit;
secondly, constructing a quantum inversion prediction model, respectively performing inversion prediction by using bit quanta of different numbers of test bits, and performing quantum inversion prediction model analysis by using stored historical bit inversion data, and performing inversion prediction on the quantum bits to obtain prediction probability;
and determining whether the target bit is turned or not according to the prediction probability, and performing weighted average on the multiple groups of data probability values to obtain a more optimal turning probability as a quantum bit turning threshold, namely the quantum bit turning initial threshold of the quantum computer.
3. The method of claim 1, wherein the quantum computer bit quantum rollover warning and threshold setting is characterized in that the quantum rollover prediction model formula is: x (k + 1) = X (k) × P
In the formula: x (k) represents a state vector of the trend analysis and prediction object at the time t = k, P represents a one-step transition probability matrix, and X (k + 1) represents a state vector of the trend analysis and prediction object at the time t = k + 1.
4. The method for quantum computer bit quantum rollover warning and threshold setting according to claim 1, wherein Bayes is employed in the second step to construct a threshold prediction model.
5. The method for bit quantum rollover warning and threshold setting for a quantum computer according to claim 1, wherein in step two, threshold values that have been successfully rolled over except for the current quantum bit in the quantum rollover threshold values of the historical database are put into a Bayesian threshold prediction model one by one to obtain threshold value use probability, and the threshold value with the largest value is used as the stationary state distribution of the Markov chain network warning model, and then replaces the initial threshold value in use, thereby completing the continuous operation of the threshold prediction model.
6. The method for quantum computer bit quantum rollover warning and thresholding as claimed in claim 1, wherein said third step employs a ridge regression method to construct a threshold adjustment model.
7. The method for quantum computer bit quantum rollover warning and threshold setting according to claim 1, wherein step three generates a new dynamic threshold to replace a current threshold, specifically:
performing simulation turnover test on ten groups of quantum bits with different numbers, setting an initial threshold value of each group as a threshold value with the current threshold value floating up and down by 10%, forming a 20% fluctuation threshold value interval, and dividing into 10 parts by 2%;
obtaining historical data by taking each current threshold plus or minus 2% of threshold as a condition, combining the historical data with the threshold adjustment model to calculate to obtain a drilling fitting value, then performing difference calculation with the actual fitting value obtained by combining the historical data which is subjected to last quantum bit flipping with the threshold adjustment model, and identifying the group of data threshold values of which the difference value between the drilling fitting value and the actual fitting value is greater than 10%;
and sequentially carrying out model training and difference comparison on 10 groups of different threshold data, marking the threshold, and carrying out weighted average on a plurality of marked thresholds to generate a new dynamic threshold to replace the current threshold.
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