QUANTUM BIOMETRIC CODES FOR SECURE IDENTIFICATION

Abstract

A method for granting or denying secure anonymized access to a restricted location, database, and / or computer system to a person is disclosed, the method comprising: constructing a one-way function for encoding biometric data, wherein the one-way function is constructed, in particular, using quantum data fitting based on vertices, the vertices being obtained by solving an optimization problem, in particular a bin-packing optimization problem; adding entries to a database, wherein the entries comprise hashes of encoded biometric data and PINs; processing an access request from the person; and, based on the result of the processing, granting or denying secure anonymized access to the restricted location, database, and / or computer system to the person.

Description

AREA OF INVENTION

[0001] The invention relates to the field of encryption and data security, in particular to the identification and verification of persons who are authorized to access a specific location, database and / or computer system. BACKGROUND

[0002] Computer systems or databases, as well as locations such as building areas in government or military facilities, may have restricted access, granting entry only to authorized personnel. The need for secure identification of authorized individuals arises, for example, from the necessity of limiting access to sensitive information stored in these restricted locations, databases, and / or computer systems.

[0003] The article “Comparative Benchmark of a Quantum Algorithm for the Bin Packing Problem” by Garcia de Andoin et al., published on arXiv on July 15, 2022, describes the Bin Packing Problem as a combinatorial optimization problem.

[0004] The article “QAL-BP: an augmented Lagrangian quantum approach for bin packing” by Cellini et al., published in Scientific Reports on 01.03.2024, describes an implementation of a bin-packing algorithm for quantum computers.

[0005] The article “Hybrid Approach for Solving Real-World Bin Packing Problem Instances Using Quantum Annealers” by Romero et al., published on arXiv on May 25, 2023, describes an implementation of a bin-packing algorithm for quantum annealers.

[0006] The article “Quantum Data-Fitting” by Wiebe et al., published on arXiv on July 3, 2012, describes a quantum algorithm that efficiently determines the quality of a least-squares fit over an exponentially large dataset. SUMMARY OF THE INVENTION

[0007] It is a goal, a procedure, a system and a computer program product to provide a person with secure anonymized access to a restricted location, database and / or computer system when the person has the necessary access rights to the restricted location, database and / or computer system.

[0008] The problems underlying the invention are solved by the features of the independent claims. Embodiments are specified in the dependent claims.

[0009] In one aspect of the invention, a method for granting or denying secure anonymized access to a restricted location, database and / or computer system to a person is disclosed.

[0010] The procedure comprises granting or denying secure anonymized access to a restricted location, database and / or computer system to an individual based on biometric data, wherein the procedure comprises: constructing a one-way function for encoding biometric data, adding entries to a database, wherein the entries comprise hashes of encoded biometric data and PINs, processing an access request from the individual, and based on the result of processing the access request, granting or denying secure anonymized access to the restricted location, database and / or computer system to the individual.

[0011] A person's biometric data includes at least one image of the person's face, at least one image of the person's fingerprint, at least one image of the person's iris and / or an audio recording of the person's voice.

[0012] The use of biometric data can increase security compared to other identification methods, such as the use of a password or PIN (personal identification number). Advantageously, biometric data can be more difficult for a malicious person seeking unauthorized access to forge. In a preferred embodiment, the biometric data is used in combination with a password or PIN to identify individuals, further enhancing security.

[0013] The term biometric data, as used here, includes biometric data used as training data, e.g., for the construction of the one-way function, biometric data added to the database, and / or biometric data provided by a person, e.g., upon receiving an access request from that person.

[0014] The use of the encoding described in this document is also advantageous because it avoids the need to store biometric data directly in a database or other storage medium where it could be accessed by a malicious entity. In particular, it is not necessary for the biometric data to be transferred or stored by a cloud service provider, which further enhances data security and facilitates privacy protection and compliance with legal regulations.

[0015] The construction of a one-way function, particularly a continuous one-way function, advantageously increases security because the one-way function is characterized by being computationally easy to evaluate but computationally difficult to reverse or invert, where "easy" and "difficult" are to be understood in the sense of complexity theory relative to each other. This difficulty in reversing the one-way function prevents reverse engineering of the biometric data, where reverse engineering describes attempts by malicious persons or entities to obtain a template, i.e., the biometric data, from the image associated with the template, i.e., by evaluating the encrypted biometric data obtained from the one-way function.

[0016] Constructing the one-way function involves: receiving biometric training data; solving an optimization problem using the received biometric training data as input, resulting in vertices as output; and applying a data fitting procedure using the vertices obtained as output of the optimization problem to construct the one-way function. Subsequent evaluation of this one-way function maps biometric data to coded biometric data, where the coded biometric data comprises coded biometric features.

[0017] The construction of the one-way function is achieved through a combination of solving an optimization problem using biometric data and applying a data fitting method to the biometric metric at the data points provided by the optimization problem. This combination of solving an optimization problem followed by applying a data fitting method can further enhance the reliability of the resulting mapping of biometric data to irreversible output values ​​of the constructed one-way function.

[0018] In a preferred embodiment, the one-way function is a continuous one-way function. Advantageously, the one-way function can enable a unique mapping of a specific portion of the biometric data to specific coded biometric data. A further advantage is that the mapping can enable a stable mapping of two different biometric data points, assigned to the same person and differing only by minor variations, to two points in a high-dimensional feature space of the coded biometric data, which also differ only by a small distance within this feature space. The dimension of the feature space is determined by a coding length.

[0019] Minor discrepancies that can link two different biometric data to the same person may include, for example, angular differences between images of the same person's face, aging effects between images of the same person's face, or noise in two images of the same person's face, iris, or fingerprint.

[0020] In a preferred embodiment, the optimization problem solved to construct the one-way function is a bin-packing optimization problem. This bin-packing optimization problem can use uncoded raw biometric data, particularly biometric training data, as input. The output obtained as the solution to the bin-packing optimization problem comprises vertices in the high-dimensional feature space determined by the coding length. Advantageously, the optimal solutions obtained as outputs of the bin-packing optimization problem provide a way to define features of the biometric data such that the biometric features homogeneously fill the high-dimensional feature space and have approximately maximum distances to all other features in the high-dimensional feature space.

[0021] The homogeneous filling of the high-dimensional feature space with near-maximum distances increases the difficulty of adversary attacks. Examples of adversary attacks include image morphing, where, for instance, the image of a person's face who is not authorized to access a secure location or computer system is altered to resemble the face of another person who does have such authorization. Another example of an adversary attack is partial masking, where certain parts of an image, such as an image of a person's face, iris, or fingerprint, are selectively hidden or altered, for example, by changing colors, shapes, or intensities.

[0022] The solution to the bin-packing problem involves providing support points f j (x) for input biometric data x, where the biometric data x has a dimension N.

[0023] In a preferred embodiment, the bin-packing optimization problem is solved on a quantum information processing system (QIPS) as a quantum bin-packing optimization problem. Advantageously, the dimension of the high-dimensional feature space accessible to a quantum information processing system, i.e., the Hilbert space of the QIPS, scales exponentially with the number of qubits contained in the QIPS, so that the bin-packing optimization problem can be solved in a higher-dimensional feature space than would be possible with classical, i.e., non-quantum, approaches. The larger dimension of the feature space, in turn, allows for the encoding of a greater number of features of the biometric data.

[0024] In a preferred embodiment, the data fitting for constructing the one-way function comprises quantum data fitting, wherein biometric data is used as input and evaluated at the support points. The support points can be obtained as the output of the solution to the optimization problem, where biometric data, in particular biometric training data, was used as input for the optimization problem.

[0025] If the biometric data includes, for example, images of fingerprints, the features of this biometric data can refer to specific combinations or relative positions of whorls, grooves, or arcs in a fingerprint. Due to the continuity of the continuous one-way function, two images of the same fingerprint taken from slightly different angles would then be assigned two points in feature space that are closer together than, for example, two images of fingerprints from different fingers.

[0026] Another example: If the biometric data includes images of a person's face, the features of this biometric data can refer to relative positions of key areas of the human face, such as the position of the eyes, eyebrows, nose, and / or mouth, or the color of the eyes. Due to the continuity of the continuous one-way function, two images of the same person's face that differ by only a small number of pixels in terms of image resolution would be mapped to points closer together in feature space than, for example, two faces of different people.

[0027] If the biometric data includes, for example, a recording of a person's voice, the features of the biometric data can relate to the presence and / or relative amplitude of certain frequencies in the recording. Due to the continuity of the continuous one-way function, two recordings of the same person's voice would be mapped to points closer together in the feature space than, for example, two recordings of the voices of different people.

[0028] Quantum data fitting can further include learning a set of learnable parameters of a vector λ, which contains elements λ. j The set of variables j ∈ {1, ..., M} is used as fitting parameters. The fitting parameters of the vector λ, in combination with the biometric data x and a dimension denoted as N, specify the one-way function. f(x,λ):=∑j=1M fj(x)λj, where f j(x) for j ∈ {1, ..., M} specifies the support points on the basis of which the one-way function is constructed, using the biometric data as input.

[0029] Quantum data fitting advantageously utilizes possible complexity accelerations compared to classical data fitting algorithms, including possible exponential advantages in algorithmic complexity and the higher dimension of the feature space enabled by quantum information processing systems.

[0030] In a preferred embodiment, the one-way function is a continuous one-way function. The continuity of the one-way function can advantageously enable a stable mapping of features of the biometric data.

[0031] The solution to the optimization problem corresponds to a categorization and organization of the biometric data based on their features, which can advantageously facilitate the search for an optimal continuous one-way function using quantum data fitting.

[0032] In general, the probabilistic nature of quantum computing, as performed on a quantum information processing system, allows the construction of solutions that do not correspond to a mathematical or mechanical, and therefore comprehensible, design principle. Instead, the obtained solutions resemble maximally entropic states, such as those resulting from Brownian motion.

[0033] In a preferred embodiment, the database can be populated with entries containing data that can be used by the computer system to evaluate whether a person requesting access should be granted or denied such access. Entries can be added to the database securely and anonymously, the addition of which includes: receiving biometric data and a PIN associated with a person; encoding the received biometric data by evaluating the continuous one-way function, resulting in encoded biometric data; hashing the encoded biometric data, resulting in a hash; and storing the hash in the database, resulting in an added entry in the database.

[0034] In a preferred embodiment, the hashing of the biometric data comprises a classic hashing of a string, wherein the string results from a combination or concatenation of the encrypted biometric data and the PIN.

[0035] In a preferred embodiment, persons can send access requests to the computer system, for example by interacting with the access control device and / or the I / O interface of the computer system.These access requests are processed by the computer system, with the processing including the following: receiving the access request from the individual; requesting biometric data and a PIN from the individual to identify the individual; receiving the biometric data and PIN from the individual; encoding the received biometric data by evaluating the continuous one-way function, resulting in encoded biometric data; hashing the encoded biometric data and PIN, resulting in a hash; comparing the hash with entries in the database; and, based on the result of the comparison, sending a response to the individual, the response indicating whether secure anonymized access to the restricted location, database, and / or computer system is granted or denied to the individual based on the result of the comparison.

[0036] Hashing significantly improves data security because the hashes result from an irreversible output of the one-way function. Therefore, storing hashes can be more secure than storing biometric data or identifying information such as people's names.

[0037] In a preferred embodiment, the database is a two-level database with a first and a second level, wherein the first level stores encoded biometric data and the second level stores hashes of the encoded biometric data and a PIN. In one embodiment, the second level of the database also includes entries that define the access rights of individuals.

[0038] The process steps, which include constructing the one-way function, solving the optimization problem, and / or fitting the data, can be performed in a purely classical manner on a classical computer system, in a purely quantum mechanical manner on a quantum information processing system (QIPS), or in a hybrid quantum mechanical-classical manner on a combination of the classical computer system and the quantum information processing system. The quantum information processing system can be communicatively coupled to the classical computer system.

[0039] The quantum information processing system comprises a quantum processing unit. In a preferred embodiment, the quantum processing unit comprises a gate-based quantum computer capable of executing quantum circuits comprising a set of quantum gates or quantum operations, wherein the quantum gates or quantum operations act on at least one qubit. In an alternative embodiment, the quantum processing unit comprises a quantum annealer capable of executing a quantum annealing procedure comprising a set of quantum operations, wherein the quantum operations act on at least one qubit.

[0040] In one embodiment, the quantum information processing system further comprises the use of a quantum cloud service provider, wherein the quantum cloud service provider is communicatively coupled to both the quantum processing unit and the classical computer system.

[0041] In another aspect of the invention, a computer system is disclosed, wherein the computer system is configured to execute the method disclosed herein for granting or denying secure, anonymized access to a restricted location, a database, and / or a computer system to a person. The computer may, in particular, be a conventional computer system.

[0042] In a further aspect of the invention, a computer program product is disclosed, in particular a computer-readable storage medium, wherein the computer program product comprises a computer-executable code, wherein the code can be executed by at least one processor of a computer system, wherein the execution of the code causes the computer system to perform the method disclosed herein for granting or denying secure anonymized access to a restricted location, a database and / or a computer system for a person.

[0043] In a preferred embodiment, the computer program product comprises instructions to be executed by the quantum information processing system (600), wherein the instructions include instructions for one or more quantum gates and / or quantum operations to be applied to one or more qubits on the quantum processing unit (602), instructions for a transpilation and / or optimization of a quantum circuit to be performed on the quantum processing unit, and / or instructions for a number of measurements to be performed on the quantum processing unit.

[0044] These instructions can be advantageously used to optimize a quantum computation, such as a quantum circuit or a quantum annealing process, for its execution on the quantum processing unit. This optimization can, for example, lead to a reduction in quantum gates or quantum operations, an optimized mapping of logical qubits to physical qubits on the quantum processing unit, and / or a mitigation and / or correction of errors.

[0045] It is understood that one or more of the aforementioned examples can be combined, as long as the combined examples do not exclude each other. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] The following examples describe in more detail with reference to the drawings, in which: Fig. 1 is a block diagram of the components and units involved in the execution of the method according to the invention. Fig. 2 is a flowchart of an exemplary embodiment of the inventive method for constructing a one-way function using bin-packing optimization and quantum data fitting. Fig. Figure 3 is a flowchart of the inventive method for adding entries of coded biometric features to the database. Fig. Figure 4 shows a flowchart of the inventive method for processing an access request from a person, taking into account the one-way function and the database with coded biometric data. Fig. 5 is a block diagram of an exemplary (classical) computer system for implementing at least part of the method according to the invention in accordance with an example of the present subject matter. Fig. 6. A block diagram of an exemplary quantum processing unit and its interactions with the classical computer system according to Fig. 5, using direct communication or communication via a quantum cloud service provider (QCSP). DETAILED DESCRIPTION

[0047] In the following, similar elements will be designated with the same reference symbols.

[0048] Fig. Figure 1 shows a block diagram of the components and units involved in carrying out the method according to the invention.

[0049] In a preferred embodiment, the computer system 502 is a conventional computer system comprising an I / O interface 519 that is communicatively coupled to an access control device 120. The access control device 120 may include a camera, a fingerprint scanner, an iris scanner, an audio recording device, and / or another device for capturing the biometric data 100 provided by a person 104 interacting with the access control device 120.

[0050] In a preferred embodiment, the computer system 502 is communicatively coupled to a database 114. In an alternative embodiment, the computer system 502 comprises the database 114. In particular, the database 114 can be structured as a two-level database comprising a first level 116 and a second level 118.

[0051] The first level 116, for example, includes entries with coded biometric data 108. The second level 118 includes, for example, entries containing hashes, where the hashes result from hashing the coded biometric data and a PIN. The hashing can be hashing a string from a combination or concatenation of the coded biometric data and the PIN.

[0052] In a preferred embodiment, the computer system 502 is communicatively coupled to a quantum information processing system (QIPS) 600, which includes a quantum processing unit (QPU) 602. The computer system 502 is configured to send instructions for the execution of classical (i.e., non-quantum) and / or quantum algorithms. In particular, the algorithms include classical, hybrid, or quantum algorithms for solving 203 the optimization problem, e.g., solving the bin-packing problem 204 as a quantum algorithm, and / or classical, hybrid, or quantum algorithms for data fitting 208, e.g., quantum data fitting. The computer system 502 is further configured to receive output from the QIPS 600.This output can take the form of one or more observations of basis states of a quantum system, resulting from a certain number of measurements to be performed on the QPU 602 of the QIPS.

[0053] In one embodiment, the computer system 502 is configured to apply post-processing steps to all output received from QIPS 600. These post-processing steps may include arithmetic instructions, instructions for normalizing or formatting results, or instructions for converting data.

[0054] Fig. Figure 2 is a flowchart of an exemplary embodiment of the inventive method for constructing a one-way function using bin-packing optimization and quantum data fitting.

[0055] In step 201, the biometric data 100 are received by the computer system 502. The biometric data 100 may, in particular, be biometric training data 100'.

[0056] In step 203, an optimization problem 204 is solved on the computer system 502 and / or on the QIPS 600, which includes the QPU 602. The optimization problem is solved using the biometric (training) data 100, 100' received in step 201 as input and yields an output, the output comprising vertices 205. In a preferred embodiment, the optimization problem 204 is a bin-packing optimization problem. In particular, the bin-packing optimization problem 204 can be solved using the QIPS 600 as a quantum bin-packing optimization problem.

[0057] The bin-packing problem 204 can be used to minimize the number of bins into which a set of items must be packed, where the bins have a fixed capacity and the items correspond to the biometric (training) data 100, 100' provided as input. The optimization problem 204 can be formulated as a combinatorial optimization problem suitable for quantum computing. In one embodiment, the bin-packing problem 204 is a one-dimensional problem. In a preferred embodiment, the bin-packing problem 204 is a multidimensional problem. In particular, the dimension of the bin-packing problem 204 can be determined by, dependent on, or equal to the dimension of the Hilbert space of the quantum system of the QPU 602.

[0058] In one embodiment, the quantum bin-packing optimization problem 204 is formulated as a quadratic unconstrained binary optimization (QUBO) problem, which allows for implementation as a quantum annealing procedure on a QPU 602 with a quantum annealer. In one embodiment, the features of the biometric data (training data) 100, 100' are binarized, for example, by the computer system 502 using a binarization algorithm. The binary variables of the QUBO problem then correspond to the presence or absence of the aforementioned features of the biometric (training) data 100, 100'.

[0059] In an alternative embodiment, the combinatorial optimization problem of the quantum bin-packing optimization problem 204 can be implemented on a QPU 602, which comprises a gate-based quantum computer, by implementing a quantum circuit for performing a quantum approximate optimization algorithm (QAOA). In one embodiment, the features of the biometric (training) data 100, 100' are encoded in basis states of a high-dimensional Hilbert space of the QPU 602. The amplitudes of these basis states represent, for example, the presence or absence of certain features, such as the presence of an arc at a specific position in the image of a fingerprint.

[0060] In one embodiment, the quantum bin-packing optimization problem is executed as a hybrid algorithm that utilizes both the classical computer system 502 and the QIPS 600. For example, the classical computer system 502 can be used to perform preprocessing and / or postprocessing steps on the input and / or output of the QIPS 600, respectively. The classical computer system 502 can also provide additional instructions and / or parameters, such as a number of measurements to be performed or an annealing time for which the quantum annealing procedure should be carried out. As another example, a classical optimization problem 204 can first be solved, using the output of the classical optimization problem as input for the quantum bin-packing optimization problem.

[0061] After performing the quantum annealing procedure or executing the quantum circuit implementing the quantum bin-packing optimization problem 204, one or more measurements of the QPU 602 are performed. In one embodiment, the measurements are repeated to generate a statistical distribution of the frequencies of the basis states of the quantum system of the QPU 602. This statistical distribution can then correspond to a distribution of maximally entropic states representing encodings of the input biometric (training) data 100, 100', distributed approximately uniformly and with approximately maximal spacing in the feature space provided by the Hilbert space of the quantum system of the QPU 602. These maximally entropic states can be used to define a set of one or more support points 205.Coding with near uniformity and maximum intervals enables a stable representation of the biometric features.

[0062] In step 207, a data fitting method 208 is applied to construct a one-way function 110 using the output of step 203, in particular using the vertices 205.

[0063] In a preferred embodiment, the one-way function 110 is configured as f(x,λ):=∑j=1Mfj(x)λj constructed, wherein f j (x), for j ∈ {1, ...,M}, specifies the support points 205, using the biometric (training) data 100, 100' as input. The vector λ comprises M learnable adaptation parameters λ j .

[0064] The basic functions f j (x) are defined by the support points 205 from step 203, for example, if a solution to the bin-packing problem 204 has a set of features x1, ..., x kthe biometric data x 100,100' into a first bin b1 and a set of features x k+1 , ..., x1 placed in a second bin b2, this set of features can be functions f j (x) with j = {1,2}, define, where f1(x) defines all features of x depending on the set of features x1, ..., x k of the first bin b1 maps, while f2(x) maps all features of x depending on the set of features x k+1 , ... , x l depicts.

[0065] For example, f1(x) can be defined as f1(x) = 1 if the sum of the features x1, ..., x k The capacity of the first container b1 exceeds the capacity of the first container, and otherwise f1(x) = 0. These definitions can be inverted or adapted, for example by multiplication by a constant factor.

[0066] Data fitting method 208 can be a classical data fitting method. For example, a polynomial can be fitted to the input biometric (training) data 100, 100', where the polynomial has a number of fitting parameters. The degree of the polynomial, and thus the number of fitting parameters, can be related, for example, to the number of features in the biometric (training) data, i.e., to the dimension of the biometric (training) data. The quality of the resulting fit can be assessed, for example, using standard statistical measures such as R². 2 or evaluated using "goodness of fit" values. The one-way function can be linear or nonlinear.

[0067] In a preferred embodiment, the data fitting method 208 is a quantum data fitting method. In this preferred embodiment, the one-way function can be defined using a set of qubits on a QPU 602 in a Hilbert space with a dimension of at least N + M, where N is the dimension of the biometric (training) data 100, 100' and M is the number of fitting parameters.

[0068] In a preferred embodiment, the biometric (training) data 100, 100' are encoded into quantum states using amplitude encoding, such that 2 N Features can be encoded in a set of N qubits.

[0069] In a preferred embodiment, the quantum computation to be performed for quantum data fitting comprises the use of an improved version of the Harrow-Hassidim-Lloyd (HHL) quantum algorithm, as described in the publication "Quantum Data-Fitting" by Wiebe et al., wherein repeated measurements of the quantum system of the QPU 602, on which the quantum data fitting algorithm is executed, lead to the construction of a quantum state |λ〉, wherein the quantum state |λ〉 determines the fitting parameters λ1, ..., λ M enabled. In one embodiment, the determination of the fitting parameters from the quantum state |λ〉 comprises a post-processing step on the classical computer system 502.

[0070] In a preferred embodiment, the one-way function 110 is a continuous one-way function. The continuity of the one-way function 110 also enables a stable assignment of similar biometric data to closely spaced points in the feature space.

[0071] In step 209, the one-way function 110, constructed by applying the data fitting method 208 in step 207, is evaluated on biometric data 100, 100', resulting in coded biometric data 108, 108'.

[0072] Fig. Figure 3 is a flowchart of the inventive method for adding entries of coded biometric features to the database.

[0073] In a preferred embodiment, the database contains 114 entries of biometric data 100, which are used as biometric training data for the construction of the one-way function 110, wherein the in Fig. The steps described in section 3 append further entries to database 114. In an alternative embodiment, database 114 contains no entries, where the entries described in Fig. 3 described steps to append entries to the empty database 114, e.g. entries that correspond to the biometric training data 100, or entries that were copied from another, already existing database.

[0074] In step 305, biometric data 100 are received by the computer system 502. In one embodiment, the biometric data are biometric training data 100', which were used for the construction of the one-way function 110. In an alternative embodiment, the biometric data 100 are received as a result of an access request from a person 104.

[0075] In one embodiment, entries or features of the biometric training data 100' can be included in the biometric data 100. For example, the fingerprint of a specific person 104 is used for the construction of the one-way function, while the same person requests access at a later time using the same fingerprint.

[0076] In step 307, the biometric (training) data 100, 100' received in step 305 are encoded by evaluating 209 the one-way function 110, resulting in encoded biometric (training) data 108, 108'. In a preferred embodiment, the encoded biometric (training) data 108, 108' are stored on the computer system during the execution of step 307.

[0077] In step 309, the encoded biometric (training) data 108, 108' obtained in step 307 are hashed. In a preferred embodiment, the hashing is performed on both the encoded biometric data 108, 108' and a PIN 102, resulting in a hash 112, where the PIN 102 and the encoded biometric (training) data 108, 108' are associated with the same person 104. In an alternative embodiment, the encoded biometric (training) data 108, 108' can be hashed, resulting in a first hash, and the PIN 102 can be hashed, resulting in a second hash, where the first hash and the second hash can be combined, for example by concatenation, to obtain the hash 112.

[0078] In step 311, the hash value 112, obtained as a result of the hashing in step 309, is stored in database 114. In a preferred embodiment, the encoded biometric data 108, 108' are stored as an entry in the first level 116 of the two-level database 114. In said preferred embodiment, the hash 112 is stored in the second level 118 of the two-level database 114. In one embodiment, additional access rights for person 104, associated with the encoded biometric data 108, 108' and the PIN 102, are stored in the second level 118 of the two-level database 114. These access rights can, for example, include an access level selected from a set of one or more access levels, each access level of said set granting person 104 access to increasingly restricted locations, computer systems, and / or databases.

[0079] Fig. Figure 4 is a flowchart of the inventive method for processing a person's access request using the one-way function and a database containing coded biometric data.

[0080] In step 401, the computer system 502 receives an access request from person 104. This access request can be initiated by person 104, for example, by interacting with the access control device 120 and / or the I / O / interface 519 of the computer system 502, for example by pressing a key or scanning a security document, where the security document may include an image of the person's face to be used as biometric data.

[0081] In step 403, the biometric data 100 and the PIN 102 of person 104 are requested by the computer system 502 to identify person 104.

[0082] In step 405, the biometric data 100 and the PIN 102 of person 104 are received by the computer system 502. In a preferred embodiment, the PIN 102 is received by person 104 by entering it into the I / O interface 519 of the computer system 502, wherein the I / O interface comprises a numeric keypad and / or a keyboard.

[0083] The biometric data required to process the access request can be received, for example, at the moment the access request is received. This step can be automated, for example, by automatically scanning a person's face, iris, or fingerprint, or by scanning a security document presented by the person as identification. Examples of security documents for identifying the person include national identity cards, employee ID cards, passports, and / or driver's licenses. Alternatively, the identification document can also be presented via a mobile device, for example, by displaying identification information on the mobile device's screen.

[0084] In step 407, the biometric data 100 received in step 405 are encoded by evaluating 209 the one-way function 110, resulting in encoded biometric (training) data 108, 108'. In a preferred embodiment, the encoded biometric (training) data 108, 108' are stored on the computer system during the execution of step 407.

[0085] In step 409, the encoded biometric data 108 obtained in step 407 are hashed. In a preferred embodiment, the hashing is performed on both the encoded biometric data 108 and a PIN 102, resulting in a hash 112, wherein the PIN 102 and the encoded biometric data 108 are associated with the same person 104. In an alternative embodiment, the encoded biometric data 108 can be hashed, resulting in a first hash, and the PIN 102 can be hashed, resulting in a second hash, wherein the first hash and the second hash can be combined, for example by concatenation, to obtain the hash 112.

[0086] In step 411, the hash 112, obtained as a result of the hashing in step 409, is compared with existing entries in database 114. In a preferred embodiment, the hash 112 is compared with all hashes stored as entries in database 114, while a variable indicating the presence of a match in the database is set to the Boolean value "false" by default. In a preferred embodiment, the variable is reset to its default value each time an access request is processed. If a hash is found in database 114 that matches the hash 112 obtained by hashing the biometric data 100 and the person's PIN 102 in step 409, the variable indicating the presence of a match in the database is set to the Boolean value "true".

[0087] In a preferred embodiment, when a match is found in database 114 and the variable indicating the presence of this match is set to the value “true”, the access rights assigned to the individual 104 associated with the matching hash 112 are retrieved from the second level 118 of database 114.

[0088] Instead of a Boolean value, other indicators for a match in database 114 can also be used, e.g., "0" and "1" or "not found" and "found".

[0089] In step 413, based on the result of the comparison in step 411, a response is sent to person 104, either granting or denying access.

[0090] If a match is found, the response indicates that the person will be granted access to the restricted location, computer system, and / or database. If no match is found, the response indicates that the person will be denied access.

[0091] In a preferred embodiment, the response, if a match is found, can vary based on the access level retrieved from the access rights stored in the second level 118 of database 114. For example, the person can be granted full or partial access depending on whether the access level is classified as high or low.

[0092] Fig. Figure 5 is a block diagram of an exemplary computer system 502 for implementing the present method according to an example of the present subject matter. The computer system may, for example, include a server, a desktop computer, a laptop, a tablet, or a mobile device.

[0093] During operation, the computer system 502 can be configured to execute the method according to the invention.

[0094] The components of the 502 computer system can include one or more processors or processing units 503, a storage system 511, a memory unit 505, and a bus 507 that connects various system components, including the memory unit 505, to the processor 503. The storage system 511 can, for example, contain a hard disk drive (HDD). The memory unit 505 can contain computer-readable media in the form of volatile memory, such as random-access memory (RAM) and / or cache memory.

[0095] The computer system 502 can also communicate with one or more external devices, such as a keyboard, a pointing device, a display 513, etc.—one or more devices that allow a person to interact with the computer system 502—and / or with any devices (e.g., network card, modem, etc.) that allow the computer system 502 to communicate with one or more other computer devices. Such communication can occur via the I / O interface(s) 519. Furthermore, the computer system 502 can communicate with one or more networks, such as a local area network (LAN), a wide area network (WAN), and / or a public network (e.g., the Internet), via a network adapter 509. As shown in the figure, the network adapter 509 communicates with the other components of the client system 502 via the bus 507.

[0096] The external devices 513 may further comprise an access control device 120, wherein the access control device 120 may, for example, include a camera, a fingerprint scanner, and / or an audio recording device. In a preferred embodiment, the access control device is used to obtain the biometric data 100, 100' of persons 104.

[0097] The 505 memory unit is configured to store applications that can run on the 503 processor. For example, the 505 memory unit can contain an operating system and one or more application programs.

[0098] As will be clear to those skilled in the art, aspects of the present invention can be embodied as a device, a method, a computer program, or a computer program product. Accordingly, aspects of the present invention can take the form of a purely hardware variant, a purely software variant (including firmware, resident software, microcode, etc.), or a variant that combines software and hardware aspects, which may be generally referred to here as a "circuit," "module," or "system." Furthermore, aspects of the present invention can take the form of a computer program product embodied in one or more computer-readable media containing computer-executable code. A computer program comprises the computer-executable code or "program instructions."

[0099] The term "computer system" refers to data processing hardware and encompasses all types of devices, equipment, and machines for processing data, including, for example, a programmable processor, a computer, or multiple processors or computers. The equipment may also be or include specialized logic circuitry, such as a central processing unit (CPU), a field-programmable gate array (FPGA), or an application-specific integrated circuit (ASIC). In some implementations, the data processing equipment and / or the specialized logic circuitry may be hardware-based and / or software-based. The equipment may optionally include code that creates an execution environment for computer programs, such as code representing processor firmware, a protocol stack, a database management system, an operating system, or a combination of one or more of these.The present disclosure considers the use of data processing equipment with or without conventional operating systems, for example LINUX, UNIX, WINDOWS, MAC OS, ANDROID, IOS or any other suitable conventional operating system.

[0100] Any combination of one or more computer-readable media can be used. The computer-readable medium can be a computer-readable storage medium. A "computer-readable storage medium," as used here, includes any tangible storage medium capable of storing instructions that can be executed by a processor of a computing device. The computer-readable storage medium can be referred to as a computer-readable non-transitory storage medium. The computer-readable storage medium can also be referred to as a concrete computer-readable medium. In some embodiments, a computer-readable storage medium may also be capable of storing data that can be accessed by the processor of the data processing system.

[0101] Computer memory is an example of a computer-readable storage medium. Computer memory is any memory that a processor can directly access. Another example of a computer-readable storage medium is computer mass storage. Computer mass storage is any non-volatile, computer-readable storage medium. In some embodiments, computer memory can also be computer mass storage, or vice versa.

[0102] A "processor," as used here, comprises an electronic component capable of executing a program, a machine-executable instruction, or computer-executable code. When the data processing system is described as comprising "a processor," this should be understood to mean that it may contain more than one processor or processing core. The processor may, for example, be a multi-core processor. A processor may also refer to a collection of processors within a single computer system or distributed across multiple computer systems. The term "computing device" should also be interpreted as potentially referring to a collection or network of computing devices, each comprising one or more processors.The executable computer code can be executed by multiple processors, which may be located in the same computer device or even distributed across multiple computer devices.

[0103] Computer-executable code may comprise machine-executable instructions or a program that causes a processor to execute an aspect of the present invention. Computer-executable code for performing operations for aspects of the present invention may be written in any combination of one or more programming languages, including an object-oriented programming language such as Java, Smalltalk, C++, or a similar language, and conventional procedural programming languages ​​such as the programming language "C" or similar programming languages, and compiled into machine-executable instructions. In some cases, the computer-executable code may be in the form of a high-level language or in pre-compiled form and used in conjunction with an interpreter that generates the machine-executable instructions on the fly.

[0104] In general, program instructions can be executed on one processor or on multiple processors. In the case of multiple processors, they can be distributed across several different units. Each processor could execute a portion of the instructions intended for that unit. Therefore, when referring to a system or process involving multiple units, the computer program or program instructions are to be understood as being capable of being executed by a processor assigned to or associated with the respective unit.

[0105] Fig. Figure 6 is a block diagram of an exemplary quantum processing unit and its interaction with the classical computer system. Fig. 5, using direct communication or communication via a Quantum Cloud Service Provider (QCSP).

[0106] In one embodiment, the (classical) computer system 502 is directly communicatively coupled to the quantum processing unit (QPU) 602 of the quantum information processing system (QIPS) 600, wherein the computer system 502 is configured to send instructions to the QPU 602 for the execution of quantum operations.

[0107] The instructions sent by Computer System 502 may include instructions for one or more quantum gates and / or quantum operations to be applied to one or more qubits on the QPU 602, instructions for transpiling and / or optimizing a quantum circuit or quantum computation to be performed on the QPU 602, e.g., instructions for error reduction, error correction, and / or optimal mapping of logic qubits to physical qubits on the QPU 602, and / or instructions for a number of measurements to be performed on the QPU 602.

[0108] The instructions that the computer system 502 receives from the QPU 602 after the execution of a quantum circuit or quantum computation can include measurement results, information about the runtime of the executed quantum circuit or quantum computation, the quantum gates or quantum operations performed, and / or the order in which the quantum gates or quantum operations were executed. In a preferred embodiment, the measurement results are provided to the classical computer 502 as a dictionary, wherein the keys of the dictionary correspond to the basis states of the quantum system of the QPU, and the values ​​of the dictionary correspond to a number of measurements of the basis state of the corresponding key of the dictionary. The keys can, for example, be provided as bit strings, with each bit corresponding to the state in which a particular qubit was measured.

[0109] In an alternative embodiment, the (classical) computer system 502 is communicatively coupled with the Quantum Cloud Service Provider (QCSP) 604, the QCSP 604 in turn being communicatively coupled with the QPU 602. In this alternative embodiment, the instructions that the computer system 502 sends to the QPU 602 and that the computer system 502 receives from the QPU 602 are instead sent to and received by the QCSP 604.

[0110] Qubits can be in states that are basis states of the quantum system, where the basis states are denoted as |0〉 and |1〉 and are analogous to the 0 and 1 states of a classical bit. Qubits can also be in states that are superpositions of several basis states of the quantum system, where each basis state is associated with an amplitude, and the amplitude is linked to a probability of measuring the qubit in the basis state associated with that amplitude. For example, the superposition state could be |ψ〉=1 / 4|0〉+3 / 4|1〉 The amplitudes of the basis states can be measured as |0〉 in 25% of measurements and as |1 in 75% of measurements if a sufficiently large number of measurements are performed. These amplitudes can be updated or changed in a quantum computation consisting of a series of quantum gates in a quantum circuit or a series of quantum operations. The process, which includes the initialization and execution of a quantum computation followed by a measurement, can be referred to as the "shot" or "run" of that quantum computation.

[0111] In one embodiment, the QPU 602 is a gate-based quantum computer configured to perform quantum operations using quantum gates on a set of one or more qubits. A configuration of quantum gates applied in a specific sequence to a particular group of qubits is called a quantum circuit. The execution of a quantum circuit may also include an initialization and readout or measurement step. Quantum gates include single-qubit and multi-qubit quantum gates.

[0112] Examples of single-qubit quantum gates include the X, Y, and Z Pauli gates, the R X (θ), R Y (θ) and R Z (θ) Rotary gates parameterized by an angle θ, the H (also Hadamard) gate and the P ϕ Phase shift gate parameterized by an angle ϕ.

[0113] Entanglement between qubits can be created by executing multi-qubit gates. Examples of multi-qubit gates include the SWAP gate, which swaps the states of two qubits, and controlled gates, such as the CX gate (also called CNOT). Controlled gates can include any number of control qubits and one target qubit. A quantum operation is performed on the target qubit when all control qubits are in the state |1〉. For example, the CCX gate consists of two control qubits and only acts as an X gate on the target qubit when both control qubits are in the state |1〉.

[0114] The QPU 602 can be restricted to the execution of a specific group of gates, referred to here as natively available gates. Any quantum circuit can then be decomposed into a number of these specific natively available gates.

[0115] In an alternative embodiment, the QPU 602 is a quantum annealer configured to perform a quantum annealing procedure. Quantum annealers can be used, for example, to solve computational problems, particularly problems formulated as combinatorial optimization problems, and especially quadratic unconstrained binary optimization (QUBO) problems.

[0116] In solving a QUBO problem, binary variables or features are mapped to the states of qubits, and interactions and / or constraints of the variables or features are mapped to interactions between the qubits. The measurement of the qubits used to solve the QUBO problem leads to a solution in the form of a binary bit string, where each bit corresponds to the state of a qubit and thus to the value of an associated binary variable. Ideally, the solution corresponds to the ground state of the quantum system consisting of the qubits of the quantum annealer.

[0117] In another embodiment, optimization problems can be solved with a gate-based quantum computer as QPU 602 using the quantum approximate optimization algorithm (QAOA).

[0118] The implementation of the states |0〉 and |1〉 of the qubits, the structure of the quantum operations to be applied to the qubits, and the performance of measurements of the qubits can vary depending on the physical quantum system used in the QPU 602.

[0119] In one embodiment, qubits comprising one or more superconducting Josephson junctions are represented by a nonlinear inductance enabling the formation of discrete energy levels, which are exemplified by the states |0〉 and |1〉 of a qubit. Qubits can be implemented, in particular, as transmon qubits, flux qubits, charge qubits, and / or phase qubits. For example, in a flux qubit, the states |0〉 and |1〉 correspond to different persistent current states circulating through a superconducting loop. In another example, in phase qubits, a phase difference across a Josephson junction is used to define the qubit states |0〉 and |1〉. The states of the qubit can be manipulated using microwave pulses tuned to the qubit's transition frequency.The phase, frequency, and / or amplitude of the microwave pulses can be varied to perform different quantum operations. Measurements can be performed, for example, by coupling the superconducting qubit to a resonator, where the resonator frequency shifts depending on the qubit's state. The qubit state can then be detected via this frequency shift by examining the resonator with a microwave signal. Measurements can also be performed by measuring a microwave signal emitted or reflected by the resonator.

[0120] Advantageously, the use of superconducting Josephson junctions for qubit implementation allows for improved scalability, as standard microfabrication techniques can be employed, resulting in a greater number of qubits in a QPU 602. The qubits also enable fast gate operations on the nanosecond scale.

[0121] In another embodiment, qubits comprise trapped ions, particularly trapped ions in an ultra-high vacuum, wherein the ions are trapped in space by applying oscillating electric fields, for example in a quadrupole ion trap (also known as a Paul trap). In this embodiment, the states |0〉 and |1〉 of a qubit can be the ground state and an excited state, or two different excited states of an ion, such as a ytterbium ion or a calcium ion. The states of the qubit can be manipulated with laser light whose frequency is tuned to the energy of the transition between the states |0〉 and |1〉 to perform quantum operations. For example, a laser emitting a light frequency tuned to the energy difference between the states |0〉 and |1〉 can realize a single qubit rotation gate, wherein the length of the laser pulse corresponds to the rotation angle.The laser light can be applied to one or more qubits simultaneously to perform single- or multi-qubit quantum operations. Measurements can be performed as optical readout with fluorescence detection. For example, a higher fluorescence signal can correspond to state |0) and a lower fluorescence signal to state |1〉.

[0122] The use of trapped ions for qubit implementation offers the advantage of longer coherence times compared to other implementations. Another benefit is that trapped ions enable full coupling of qubits in a QPU 602, thereby reducing the number of operations required to implement a quantum algorithm and resulting in improved algorithm performance.

[0123] In another embodiment, the qubits consist of neutral atoms, in particular neutral atoms trapped in optical lattices or optical tweezers. In this embodiment, the states |0〉 and |1〉 of a qubit can be the ground state and an excited state, or two different excited states of a neutral atom, e.g., two hyperfine states of a rubidium atom. These atoms can be excited to Rydberg states using laser pulses. The states of the qubit can be manipulated with laser light emitting a frequency tuned to the energy of the transition between the states |0〉 and |1〉 to perform quantum operations. Multi-qubit gates can also be implemented with Rydberg blocking, which prevents other neutral atoms from being excited to the same Rydberg state.For example, a laser emitting a light frequency tuned to the energy difference between states |0〉 and |1〉 can implement single-qubit rotation gates, where the laser pulse length corresponds to the rotation angle. The laser light can be applied to one or more qubits simultaneously to perform single- or multi-qubit quantum operations. The measurements can be performed as an optical readout with fluorescence detection. Thus, for example, a higher fluorescence signal can correspond to state |0〉 and a lower fluorescence signal to state |1〉.

[0124] The use of neutral atoms for the implementation of qubits has the advantage of enabling long coherence times, all-to-all connections, manipulation of qubits with standard techniques such as optical tweezers, and operation at or near room temperature.

[0125] In another embodiment, the qubits comprise nitrogen vacancy centers (NV centers) in a solid-state system, particularly in diamond, with a nitrogen atom replacing a carbon atom in a diamond lattice and a vacancy adjacent to the nitrogen atom in the diamond lattice. In this embodiment, the states |0〉 and |1〉 are spin states of a free electron located at the vacancy of the NV center. The states of the qubit can be manipulated using microwave and optical techniques to perform quantum operations. The measurements can be performed as an optical readout with fluorescence detection. For example, a higher fluorescence signal can correspond to state |0〉 and a lower fluorescence signal to state |1〉.

[0126] Advantageously, the use of NV centers in diamond for the implementation of qubits enables optical addressability, which includes optical initialization, manipulation and readout of qubits, operation at room temperature and improved shielding of the qubit from ambient noise by embedding the qubit in the solid lattice of diamond.

[0127] In another embodiment, the qubits comprise electronic states of semiconductor quantum dots, e.g., quantum dots in silicon. The quantum dots can enclose electrons or electron holes, so that charge or spin states of these electrons or electron holes can be used as qubits. Using a spin state, for example, the states |0〉 and |1〉 of a qubit can correspond to an electron spin that is in an "up" or "down" state with respect to a reference axis. Using a charge state, the states |0〉 and |1〉 of a qubit can correspond, for example, to the presence or absence of an electron in a particular quantum dot or to the presence of the electron in one of two coupled quantum dots. The states of the qubit can, for example,They can be manipulated by oscillating magnetic and / or electric fields, by the coupling of spins in neighboring quantum dots, and / or by electrostatic interactions between charges in neighboring quantum dots. Measurements can be performed, for example, by spin-charge conversion techniques and / or charge measurement.

[0128] Advantageously, the use of quantum dots for the implementation of qubits enables scalability through the use of standard manufacturing techniques for semiconductors, especially silicon, and facilitates compatibility with other silicon-based technologies.

[0129] In another embodiment, qubits consist of photons. For example, the states |0〉 and |1〉 of a qubit can be defined as photons with horizontally or vertically oriented polarization relative to a reference axis. Alternatively, the states |0〉 and |1〉 can be defined as photons following different paths in space. As a further alternative, the states |0〉 and |1〉 can be defined as photons arriving at a destination, such as a sensor, at an early or late time. The states of the qubit can be manipulated using optical elements to perform quantum operations, with the optical elements including wave plates, beam splitters, and / or phase shifters. Linear or nonlinear optical processes can be used in quantum computing with photons as qubits. Measurements can be performed, for example, by…This can be achieved by detecting the polarization of a photon using a polarizing beam splitter and / or wave plates. Alternatively, measurements can be performed using interferometry techniques to determine the path of the qubits, e.g., Mach-Zehnder interferometry. Another alternative is the time-resolved detection of photons arriving at an early or late time, e.g., using single-photon detectors with sufficiently high temporal resolution.

[0130] The use of photons for the implementation of qubits has the advantage that they can be operated at room temperature, that quantum calculations can be performed at high speed, that the decoherence in the transmission of information over long distances is lower, and that they are compatible with conventional optical technologies, especially with techniques for the fabrication of photonic circuits.

[0131] In another embodiment, the qubits comprise quasiparticles, particularly anyons, in two-dimensional materials. These materials include, for example, topological superconductors, semiconductor nanowires, and / or highly correlated materials. The qubits can be realized, for example, with a system of Majorana fermions, where pairs of Majorana fermions can fuse to form either a vacuum state, representing |0〉, or a fermion state, representing |0〉. The states of the qubit can be manipulated by exchanging and / or braiding anyons to perform quantum operations. Measurements can be performed, for example, by fusion measurements, observing whether a pair of fermions fuses to form the vacuum state or the fermion state. Fusion measurements can be performed using interferometric techniques or by employing additional qubits.

[0132] The advantage is that using anyons for the implementation of qubits enables intrinsic fault tolerance and fault resistance through topological protection of the qubit states.

[0133] It is understood that the present invention can be implemented using a quantum processing unit (QPU) 602 using an alternative quantum system for implementing qubits that differs from the implementations listed here, for example, if such an alternative quantum system offers advantages such as improved noise resistance, longer coherence times, lower gate error rates, lower measurement error rates, a larger number of qubits, and / or advantages in the design of the hardware of the quantum processing unit 602, e.g., with regard to the required cooling of at least parts of the quantum system and the associated hardware, e.g., sensors, or the generation of a vacuum.

[0134] It is further understood that the quantum system used in the Quantum Processing Unit 602 can include, instead of or in addition to qubits, the use of additional basis states beyond |0〉 and |1〉; that is, the quantum system can include “qudits,” each qudit comprising a number of d basis states with d ≥ 2. For example, a qudit with d = 3, also called a qutrit, can assume superpositions of three basis states, exemplarily denoted as |0〉, |1〉, and |2〉, thereby advantageously increasing the dimension of the Hilbert space of the quantum system.

[0135] Although the invention is illustrated and described in detail in the drawings and the foregoing description, these illustrations and descriptions are to be regarded as illustrative or exemplary and not limiting; the invention is not limited to the disclosed examples. REFERENCE MARK LIST 100 biometric data points 100' biometric training data 102 PIN 104 people 106 Bin-Packing Problem 108 coded biometric data points 108' coded biometric training data 110 One-way function 112 Hash 114 Database 116 first level of the database 118 second level of the database 120 Access control device 200 process steps for constructing a one-way function 201 Receiving training data 203 Solving the optimization problem 204 Optimization Problem (Bin-Packing) 205 support points 207 Applying the (quantum) data fitting method 208 (Quantum) Data Fitting Method 209 Evaluating the one-way function 300 procedural steps for adding entries to the database 305 Receiving biometric data and PIN 307 Converting Data 309 One-way hashing 311 Comparison with entries in the database 400 procedural steps for processing access requests 401 Receiving an access request 403 Biometric data and PIN queries 405 Receiving biometric data 407 Converting Data 409 One-way hashing 411 Comparison with entries in the database 413 Sending the reply 502 Computer System 503 processors 505 storage unit 507 Bus 509 Network adapters 511 Storage system 513 External Devices 519 I / O interface 600 Quantum Information Processing System (QIPS) 602 Quantum Processing Unit (QPU) 604 Quantum Cloud Service Providers (QCSPs) QUOTES INCLUDED IN THE DESCRIPTION

[0000] This list of documents cited by the applicant was automatically generated and is included solely for the reader's convenience. The list is not part of the German patent or utility model application. The DPMA accepts no liability for any errors or omissions. Cited non-patent literature

[0000] QAL-BP: an augmented Lagrangian quantum approach for bin packing” by Cellini et al., published in Scientific Reports on March 1, 2024

[0004] Article “Hybrid Approach for Solving Real-World Bin Packing Problem Instances Using Quantum Annealers” by Romero, published on arXiv on 25.05.2023

[0005] Article “Quantum Data-Fitting” by Wiebe published on arXiv on 03.07.2012

[0006]

Claims

[1] A method for granting or denying secure anonymized access to a restricted location, database and / or computer system to a person (104), the method comprising: - Constructing (200) a one-way function (110) and evaluating (209) the one-way function (110) on biometric data (100, 100'), resulting in coded biometric data (108, 108'); - Adding (300) entries to a database (114), wherein the entries comprise hashes (112) of encoded biometric data (108, 108') and PINs (102); - Processing (400) an access request from person (104); and - Based on the outcome of the processing (400), granting or denying secure anonymized access to the restricted location, database and / or computer system for the individual (104). [2] Method according to claim 1, wherein the construction (200) of the one-way function comprises: - Receiving (201) biometric training data (100'); - Solving (203) an optimization problem (204) using the received biometric training data (100, 100') as input, resulting in support points (205) as output; - Applying (207) a data fitting procedure (208) to construct the one-way function (110) using the support points (205). [3] Method according to any of the preceding claims, wherein solving (203) the optimization problem (204) comprises solving a bin-packing optimization problem. [4] Method according to any of the preceding claims, wherein the data fitting (207) is a quantum data fitting (207) using the biometric data x (100, 100'), wherein the biometric data in particular comprise biometric training data (100'), and a set of learnable parameters λ j with j ∈ {1, ..., M} includes, where the one-way function (110) has the form f(x,λ):=∑j=1Mfj(x)λj assumes, and where f j (x) indicates the support points (205). [5] Method according to any of the preceding claims, comprising adding (300) entries to the database: - Receiving (305) biometric data (100, 100') and a PIN (102) assigned to a person (104); - Encoding (307) the received biometric data (100, 100') by evaluating (209) the one-way function (110), resulting in encoded biometric data (108, 108'); - Hashing (309) of the encoded biometric data (108, 108') and the PIN (102), resulting in a hash (112); and - Storing (311) the hash in the database (114), resulting in an added entry in the database. [6] Method according to any of the preceding claims, wherein processing (400) the access request comprises: - Received (401) the access request from the person (104); - Querying biometric data (100) and a PIN (104) of the person (104) to identify the person; - Receiving (405) the biometric data (100) and PIN (102) from the person (104); - Encoding (407) the received biometric data (100) by evaluating (209) the one-way function (110), resulting in encoded biometric data (108); - Hashing (409) of the encoded biometric data (108) and the PIN (102), resulting in a hash (112); - Comparing (411) the hash (112) with entries in the database (114); and - Based on the result of the comparison (411), sending (413) a response to the person (104), the response indicating a grant or denial of secure anonymized access to the restricted location, database and / or computer system to the person based on the result of the comparison (411). [7] Method according to any of the preceding claims, wherein the biometric data (100, 100') of the person (104) comprises at least one of the following elements: an image of the person's face, an image of the person's fingerprint, an image of the person's iris and / or an audio recording of the person's voice. [8] The method according to one of the preceding claims, wherein the database (114) is a two-level database comprising a first level and a second level, wherein the first level stores encoded biometric data (108) and the second level stores hashes (112), wherein the hashes result from a hashing of the encoded biometric data and a PIN (102). [9] Method according to any of the preceding claims, wherein the second level of the database (114) further comprises entries that specify access rights of persons (104). [10] Method according to any of the preceding claims, wherein any of the steps of constructing (200) the one-way function (210), in particular solving (203) the optimization problem (204) and / or fitting the data (207), is performed on a computer system (502), in particular a classical computer system (502), a quantum information processing system (600) or a combination of a classical computer system and a quantum information processing system, wherein the quantum information processing system is communicatively coupled to the computer system. [11] Method according to any of the preceding claims, wherein the quantum information processing system (600) comprises a quantum processing unit (602), wherein the quantum processing unit is a gate-based quantum computer or a quantum annealer. [12] Method according to any of the preceding claims, wherein the quantum information processing system (600) further comprises the use of a quantum cloud service provider (604), wherein the quantum cloud service provider is communicatively coupled to both the quantum processing unit (602) and the computer system (502). [13] Computer system (502) for granting or denying secure anonymized access to a person (104), wherein the computer system is communicatively coupled to an access control device (120) and a database (114), wherein the computer system is configured to perform the method according to any of the preceding claims. [14] Computer system (502) according to claim 13, wherein the computer system is communicatively coupled to a quantum information processing system (600), wherein the quantum information processing system (600) comprises a quantum processing unit (602), wherein the quantum processing unit is a gate-based quantum computer or a quantum annealer. [15] Computer program product for granting or denying secure anonymized access to a restricted location, database and / or computer system (502) for a person (104), wherein the computer program product comprises one or more computer-readable storage media on which program instructions are embodied, wherein the program instructions are executable by one or more processors (503) of the computer system (502), and wherein the program instructions cause the one or more processors to perform operations according to the method of any of the preceding claims. [16] Computer program product according to claim 15, further comprising instructions to be executed by the quantum information processing system (600), wherein the instructions include instructions for one or more quantum gates and / or quantum operations to be applied on one or more qubits on the quantum processing unit (602), instructions for a transpilation and / or optimization of a quantum circuit to be performed on the quantum processing unit, and / or instructions for a number of measurements to be performed on the quantum processing unit.

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