Abstract: A computer implemented method for compressing pre-trained of a large language model (LLM) comprising identifying (S101) layers of the LLM (47) with the weight matrices (48), decomposing (S104a) the weight matrices (48) of the LLM (47) into a tensor network (49), compressing (S104b) the tensor network (49), and storing (S104c) the compressed tensor network (49) in a data storage unit (40).

Abstract: A computer implemented method for solving a classical optimization problem of integer factorization implemented on a digital computer system is described. The method is implemented on a classical processor adapted to execute a time evolving block decimation algorithm. The method comprises in a first step an inputting a lattice basis and a target lattice vector to an input device of the classical processor followed by an implementing a lattice basis reduction algorithm on the lattice basis in an implementation module, thereby obtaining a reduced orthogonal lattice basis. The method further comprises a projecting the target lattice vector on the reduced orthogonal lattice basis followed by a building a closest vector to the target lattice vector and optimizing the closest vector using a tropical time-evolving block decimation algorithm by the classical processor and finally outputting an integer vector.

Abstract: A system and method for data compression using quantum computing are provided. The system receives an initial set of assets and corresponding asset weights. The asset weights are encoded using binary asset holding variables. Cardinality constraints are generated for the asset weights. The cardinality constraints are encoded into qubits. An optimization objective function is minimized using the qubits encoding the cardinality constraints. A subset of assets that replicates the behavior of the initial set of assets is obtained based on the minimized optimization objective function.

Abstract: A method including the following steps of adjusting weight values of a boosted classifier, the boosted classifier being a classifier for calculating whether data is indicative of an intrusion; wherein the boosted classifier includes the weight values and a number of classifiers. Each classifier of the number of classifiers being associated with a weight value of the weight values. The method further includes the step of adjusting the weight values including adjusting, by at least one quantum computing device, states of qubits for reducing a value of a cost function of the boosted classifier; wherein the adjusted weight values are based on the adjusted states of qubits. Also related is a device or system for performing the method, to the boosted classifier and to execution of the boosted classifier.

Abstract: A method including the following steps adjusting weight values of a boosted classifier for reducing a value of a cost function of the boosted classifier, the boosted classifier being a classifier for calculating whether data is indicative of an intrusion into a computing entity. The boosted classifier includes the weight values and a number of classifiers, each classifier being associated with a weight value of the weight values. The method also includes the step of adjusting the weight values including executing, by at least one digital computing device, a quantum-inspired algorithm. Also related is a device or system for performing the method, to the boosted classifier and to execution of the boosted classifier.

Abstract: This document teaches a computer implemented method for solving a combinatorial optimization problem of a cost function implemented on a digital computer system comprising a processor (10) adapted to execute a time evolving block decimation (TEBD) algorithm. The method comprises mapping the cost function to a Hamiltonian (H(x1, x2, . . . xn)) in a mapping module (80), choosing an initial state of a vector space (V) representative of the cost function, applying a time-evolution operator (O) to the state to produce an updated state, iteratively applying the time-evolution operator to the updated state to produce a further updated state until a ground state is reached, and determining the cost function from the ground state of the Hamiltonian.

Abstract: A method for modifying a variable string (330) in a document (300) and generating a required hash value, wherein the document (300) comprises the variable string (330) and a fixed string (320). The method comprises constructing (S220) a Hamiltonian based on the variable string (330), encoding (S230) the variable string (330) into a quantum circuit (310) comprising a plurality of qubits, generating in a hash function generator (350) a hash value from the fixed string (320) and the output of the quantum circuit (310), comparing the generated hash values with a true hash value (370) in a comparator (360), determining (S280) an overlap between the generated hash value and a true hash value (370), and, on reaching a zero overlap value, determining the variable string (330), otherwise optimising (S270) parameters of the quantum circuit (310).

Abstract: A method for modifying a variable string (330) in a document (300) and generating a required hash value, wherein the document (300) comprises the variable string (330) and a fixed string (320). The method comprises constructing (S220) a Hamiltonian based on the variable string (330), encoding (S230) the variable string (330) into a quantum circuit (310), generating in a hash function generator (350) a hash value from the fixed string (320) and the output of the quantum circuit (310), determining (S280) an overlap between the generated hash value and a true hash value (370), and, on reaching a zero overlap value, determining the variable string (330), otherwise optimising (S270) parameters of the quantum circuit (310).

Abstract: A system and method for determining a probability distribution of a sentence are described herein. The system includes a processor determining a syntactic tensor network, which includes correlated syntactic elements, for the sentence. Each syntactic element includes words and linguistic information for each syntactic element. The processor determines a probability tensor which includes a probability distribution for each syntactic element in the sentence based on the linguistic information for the syntactic element. The processor determines the probability distribution of the sentence based on the probability tensor of each syntactic element in the sentence. In an embodiment, the processor determines a probability tensor of a word in the sentence, based on a syntactic neighborhood of the word and a linguistic group associated with its immediate neighbors.

Abstract: The disclosure relates to a method (100) for generating data indicative of a digital twin of a physical system (2), the method including the steps oft receiving (120), by at least one classical computing device (11), data acquirable from the physical system (2); and processing (130), by the at least one classical computing device (11), the data acquirable from the physical system (2) to provide the data indicative of the digital twin, the processing (130) comprising digitally computing a quantum-inspired algorithm. The disclosure also relates to a device or system adapted to execute the method, to at least one computing device configured to execute the steps of the method and to a computer program product having instructions for executing the method.

Abstract: The disclosure relates to a method for generating data indicative of a digital twin of a physical system, the method: including the steps of receiving, by at least one quantum computing device, data acquirable from the physical system; and processing, by the at least one quantum computing device, the data acquirable from the physical system to provide the data indicative of the digital twin. The disclosure also relates to a device or system adapted to execute the method, to at least one computing device configured to execute the steps of the method and to a computer program product having instructions for executing the method.

Abstract: A method for optimization of multidimensional continuous functions using a quantum processor (50) is disclosed. The method comprises initializing (S400) quantum circuit parameters ({right arrow over (?)}) to set an initial guess |?) of a function f({right arrow over (x)}), running (S410) a variational quantum circuit U({right arrow over (?)}) over n qubits of the quantum processor (50), implementing (S420) a quantum state tomography function of the individual qubits to estimate qubit parameters [?, ?, r] of the function f({right arrow over (x)}) for each qubit.

Abstract: A computer-implemented method includes processing a predetermined machine learning routine of a tensor network that defines layers of tensors in the routine, which is adapted for a regression problem of fermionic systems that are molecules or chemical reactions. Each tensor of the tensor network of the predetermined machine learning routine is converted into a parity preserving tensor. A sign swap tensor is introduced in the tensor network at each crossing of legs of different tensors in the tensor network. Thus, implementing anticommutation fermionic operator; inputting a first many-body problem modeling a first fermionic system in the processed predetermined machine learning routine, the first fermionic system being a molecule or a chemical reaction; and outputting from the processed predetermined machine learning routine at least one parameter for the first fermionic system after having inputted the first many-body problem.

Abstract: A system and method for improving a convolutional neural network (CNN) are described herein. The system includes a processor receiving a weight tensor having N parameters, the weight tensor corresponding to a convolutional layer of the CNN. The processor factorizes the weight tensor to obtain a corresponding factorized weight tensor, the factorized weight tensor having M parameters, where M<N. The processor supplies the factorized weight tensor to a classification layer of the CNN, thereby generating an improved CNN. In an embodiment, the processor (a) determines a rank of the weight tensor and (b) decomposes the weight tensor into a core tensor and a number R of factor matrices, where R corresponds to the rank of the weight tensor. In another embodiment, the processor (a) determines a decomposition rank R and (b) factorizes the weight tensor as a sum of a number R of tensor products.

Abstract: A method for determining an encryption key in a key space for encrypting a plain text to a corresponding encrypted ciphertext. The method comprises constructing (S220) a Hamiltonian based on the encrypted ciphertext, encoding (S230) the key space (320) into a quantum circuit (310), encrypting (S240) the plain text using the quantum circuit (310) to obtain a superposition of ciphertexts and measuring the superposition of ciphertexts to determining (S280) an overlap between the measured superposition of ciphertexts and the encrypted cyphertext. On reaching a pre-determined overlap value, the key space (320) is collapsed (S290) to determine the encryption key, or otherwise parameters of the quantum circuit (310) are adjusted.

Abstract: A quantum-based extreme learning machine and a method for training a quantum-based extreme learning machine using a quantum processor implementing a quantum substrate and a set of training data is disclosed. The training data comprises input features vectors with a plurality of N parameters and true labels vector. The method comprises uploading the training data to the quantum processor, encoding the uploaded training data, passing a plurality of subsets of the input features vector from the training data through the quantum substrate to obtain a plurality of output vectors of expectation values, concatenation of the plurality of output vectors of expectation values to construct a matrix, computation of an inverse matrix from the matrix and multiplication of the inverse matrix by the true labels vector to obtain a vector of optimal weights ?.

Abstract: A method for quantum classification and operation control includes radiating a vacuum chamber having an ensemble of neutral atoms with laser so as to trap atoms and form a quantum register. The method further includes the step of configuring a laser controlling function with M unitary operations based on a cost function for classification problems and a training dataset about a monitored target, radiating the ensemble of atoms accordingly, reading the quantum register, and setting a quantum classifier if the cost function with the values of the quantum register meet a condition, keep changing the laser controlling function and radiating the ensemble of atoms otherwise until a convergence condition is met, at which point the quantum classifier is set.

Abstract: A quantum-based extreme learning machine and a method for training a quantum-based extreme learning machine using a quantum processor (50) implementing a quantum substrate (220) and a set of training data (260) is disclosed. The training data comprises input features vectors (400) with a plurality of N parameters (262) and true labels vector (265). The method comprises uploading (S610) the training data (260) to the quantum processor (50), encoding (S620) the uploaded training data (260), passing (S410, S630) a plurality of subsets of the input features vector (400) from the training data (260) through the quantum substrate (220) to obtain a plurality of output vectors of expectation values (420), concatenation (S420) of the plurality of output vectors of expectation values (420) to construct (S430) a matrix (430), computation (S440) of an inverse matrix (H) from the matrix (430) and multiplication (S450) of the inverse matrix (H) by the true labels vector to obtain a vector (470) of optimal weights ?.

Abstract: A method including the steps of digitally introducing data associated with at least one patient into a quantum classifier for classifying patients such that patients having a predetermined medical condition or being in risk of having the predetermined medical condition in a predetermined time span are classified into a predetermined risk group. The data includes biometrics of the at least one patient. The method further includes digitally commanding a quantum device or system to run the quantum classifier to classify the at least one patient; and digitally determining an action to be taken with respect to the at least one patient when the at least one patient has been classified into the predetermined risk group. Also, devices, systems and computer program products adapted to carry out the method are related.

Abstract: A method and system for establishing a circuit configuration for a data set to carry out a quantum computation on a quantum processor (50) is disclosed. The method comprises ingesting (S205) data values of the data set, fitting (S220) the data values to a function f(i) and converting (S230) the data values to qubits (q0-q3) by rotations.