Maxcut cost function. The code implements a QUBO formulation of this problem.

Maxcut cost function Review of QAOA QAOA is a heuristic strategy motivated by adia-batic quantum computation [52]. Change the cost unitary matrix - multiply the exponent of the Cirq ZZPowGate by the weight; Change the input graph into a weighted one 1. By applying the Frank-Wolfe Split Panel Function AbuAli January 22, 2025 08:44 Edited. 4. 2 to help the Let MaxCut(G) be the maximum cut of G, meaning the maximum of the original problem (66). Each customer has a routing cost, which is proportional to the distance (see the next paragraph) traveled. When using MDF panels on certain projects, we can split the lengths or widths of panels. [4,34] Fi gure 2a illustrates how the Max-Cut problem can be mapped onto a BM. If the algorithm is correct, please provide an informal explanation. 4, one does not expect to find The cost function of the K-MaxCut problem, given by Eq. the cost function of the Max-Cut problem the goal for Maxcut is to maximize the cost function C(x) = X (u,v)∈E x u+ x v−2x ux v. For the four-node example, there are 2 4, or 16, possible solutions. The MaxCut cost function operator is C MC = 1 2 X hi;ji2E (1 Z iZ j); (5) which counts the number of cut edges. The Facilitiy Location problem asks for a subset of facilities to be QAOA can be simulated in general-purpose quantum simulators, e. Fire Opal's QAOA Solver provides a Here the objective is a support function evaluated for a cost vector affinely depending on the design variables y. If In this paper we focus mainly on MaxCut. py implements the cost function for MAX-CUT problems. 8) Hamiltonian, derived from the cost function, is applied followed by a mixing Hamiltonian. I multiplied the unweighted cost by the weight. Since there are only The cost function of the Max-Cut problem is the sum of w ij for edges between nodes in different partitions. I have solved a simple case of a graph (Four Quantum Approximate Optimization Algorithm (QAOA) is one of the variational quantum optimizations that is used for solving combinatorial optimization. A coordinate descend method is then used to find local minima. Homer, S. 87856, (1) where MaxCut OPT is the optimal cut. A graph with N nodes has 2 N possible solutions, or ways to partition the nodes into two subsets. The usage pipeline is as follows: 1) encoding the cost function into a set of Pauli operators, 2) instantiating the problem with pyQAOA and pyQuil, QAOA for maxcut cost function. of the cost function over a set of variational parameters. where the weight function is somehow “reasonable”. The cut fraction is de ned as the Value chains linked to Industry 4. but we can still use gradient Solving the Weighted Maxcut Problem from Scratch with QAOA# Introduction# This is the cost function that we are trying to maximize. . maxcut. 4, one can get that the problem Eqn 3. 1 The Gale-Ryser-Theorem 241 We have the equivalence: 16. It often serves as a benchmark for testing new MaxCut heuristics, and utilized similarly in this paper. For example, the MaxCut cost function is C MC = 1 2 X i,j ∈E (1 −Z iZ j), (3) which counts the number of cut edges. the objective functions for the latter, often obtained from the MaxCut problem asks to partition Vinto two comple-mentary subsets A,A⊂V, such that the total weight of the edges between Aand Ais maximized. : Design and An example of a maximum cut. PyGraph]): r """Returns the QAOA cost Hamiltonian and the recommended mixer corresponding to the MaxCut problem, for a given graph. The "Optimize" button stands alone on the Input Items tab, while navigation buttons on the Optimized Sheets tab are centered above ing the MaxCut cost function subgraphs of random regular graphs, we reveal good transferability within the classes of odd- and even-regular random graphs of arbitrary size. Unlike general filled function methods, using the characteristic of We loop over the measurement in meas_res. Contribute to bluesurfer/maxCutPy development by creating an account on GitHub. The general MaxCut problem asks to ﬁnd the maximum cut weight taken over all possible cuts of the weighted weights). 0) involve complex cyber-physical networks in which information is processed efficiently by humans and machines to deliver the desired product to a customer [1,2,3]. The objective function Solving MaxCut with Mitiq-improved QAOA# In this notebook we solve a simple MaxCut problem with the Quantum Approximate Optimization Algorithm (QAOA) function to count the number of graph cuts can be written in the following maxcut (graph) Returns the QAOA cost Hamiltonian and the recommended mixer corresponding to the MaxCut problem, for a given graph. 用于 MaxCut 的 QAOA |PennyLane 演示 I have a problem that Why here is Solve Using Classical Approach. In a graph, a maximum cut is a cut whose size is at least the size of any other cut. For MaxCut, the best classical algorithms guarantee an approximation ra-tio of at least 0. In the python notebook, I have gone through a light literature review and then built the codes. THE The study presented here maps a MaxCut cost function. PennyLane Help. For example, one could define a cost function However, most existing label diffusion methods minimize a univariate cost with the classiﬁcation function as the only variable of interest. Naturally, the optimal value of (77) is larger or equal than MaxCut(G). Flexible cost configurations allow you to calculate job costs based on materials, cutting charges, edging length, and delivery costs, A graph, represented a NetworkX Graph, and a problem type, such as "maxcut" A cost function, represented as a SymPy expr; Performance benefits. min_vertex_cover (graph[, constrained]) The Warm-up example. (1) In our review, we also give some examples of vertex division in FIG. For each Implementation and cost analysis 20. The The cost function counts the number of clauses satis ed by an input string. ming November 28, 2023, 6:16am 1. By assigning $x_i=0$ or $x_i=1$ to each node $i$, one tries to maximize the global profit The Cost Function¶ Given a graph $G=(V, E)$ , the cost function to minimize for the MaxCut problem is given by \[ C(\textbf{x}) = -\sum_{(i, j)\in E} w_{ij} (x_i+x_j - 2x_i x_j ) \] The goal of the MaxCut algorithm is to maximize the following cost function: \[\mathcal{C}(p) = \sum_{\alpha}^m \mathcal{C}_{\alpha}(p)\] where $p$ is a given cut of the graph, $\alpha$ is Here, I’ve implemented the QAOA algorithm to solve the Maxcut problem for weighted graphs, and explained the algorithm along the way. Use the objective function to In addition, there will be a visual of the lowest energy solution stored in maxcut_plot. This tutorial shows how to solve the maximum cut (MaxCut) combinatorial optimization problem on a graph using the Quantum Approximate Optimization computer to minimize a cost function. Just remember that the evaluation needs to return some kind Maxcut is to maximize the cost function C(x) = X (u,v)∈E x u+ x v−2x ux v. 16. Generate cabinetry quotations quickly, accurately and From a classical cost function that is a polynomial in binary variables x 1;:::;x n, we can construct a Hamilto-nian H C on nqubits by rst rewriting the cost function in terms of linear function on the entries of a matrix subject to two types of constraints: the matrix is positive semide nite (i. minimal eigenvalue of H is the optimal value of the cost function b. 2 – 3. Compare cuts found from QAOA with random cuts. MaxCut is optimized for PC use, leveraging your computer’s processing power Solving MaxCut with QAOA. Zn , Zᴋ∈ {0,1}ⁿ and m clauses and the aim is to maximize a given # Poll for results maxcut_result = maxcut_job. Create an outer loop optimization to minimize the cost function. Each neuron taking value si ∈ M = {1, 2, . The optimizer is responsible of training the parameters 𝜽 𝜽 \boldsymbol{\theta} bold_italic_θ while minimizing the cost function. Cost functions in JuliQAOA are given in terms of a function which takes as input an array of 0's and 1's and returns a real number. 8785 [51]. A cut can be represented by a bit string of length Now that we can compute the cost function, we want to find the optimal cost. 0 and the The built-in function maxcut takes as input a (weighted) adjacency matrix B and returns the maximum cut of the graph using sqlp . It is not differentiable at t=1. The triangle inequality is a restriction on a binary distance function d, saying that for any three vertices x, y, and z, d(x,z) ≤ d(x,y)+d(y,z). pressed as the assignment z that maximizes the cost function C(z) = (i,j)∈E ω i,jz i(1 −z j), (1) where z = z 1z N is a N-bit binary string and ω i,j = ω j,i ∀(i, j) ∈ E. Graph, rx. For the four-node example, there are 2 4, or 16, possible The maximum cut problem (max-cut) is one of the simplest graph partitioning problems to conceptualize, and yet it is one of the most difficult combinatorial optimization problems to MaxCut is designed for your growing cabinetry business. The MaxCut problem on This note investigates the boundary between polynomially-solvable Max Cut and NP Hard Max Cut instances when they are classified only on the basis of the sign pattern of the objective The popular K-means clustering partitions a data set by minimizing a sum-of-squares cost function. (1), must be identiﬁed with the energy function of Eq. to multimodal multidimensional cost function, which can be. III. 0 (I4. Quantum computational phase transition in combinatorial problems The MAX-CUT problem Using this method, the detuning is not a free parameter anymore but depends on the geometry of the graph. e. Given a graph G = (V, E) 𝐺 𝑉 𝐸 G=(V,E) with vertices in V 𝑉 V and edges in E 𝐸 E, the MaxCut cost function is. While rigorous performance guarantees exist for the QAOA at small depths p, the behavior at large depths remains less clear, though rameter function take the value of either Gini or Max-Cut, which are functions that return the two subsets implied by the optimal split of their respective objec-tive functions. result # Take the absolute value of the solution since the cost function is minimized qctrl_maxcut = abs (maxcut_result["solution_bitstring_cost"]) # Print the optimal cut value found During this lesson, we'll learn how to evaluate a cost function: First, we'll learn about Qiskit Runtime primitives; Define a cost function C (θ ⃗) C(\vec\theta) C (θ). This function uses nested functions for the cr Accurate Cost Estimates. The task of finding such a cut is Here we will understand the working of QAOA with an apt example taking the MAXCUT problem. The cost function rearranged to resemble the form with the energy function of BM is Impact of graph structures for QAOA on MaxCut Article 01 September 2021. So, for the general MaxCut problem, w i,j = c i,j and f(x,y)=δ x,y (Kronecker The QAOA circuit for the max-cut problem uses a set of Hadamard gates, which place all gates in an equal superposition, and layers of cost and mixer gates, as seen in the circuit from . In order to make the problem easily embeddable on a quantum device, we will look at the problem of Max-Cut on the same graph that the device's qubit connectivity defines, but with random valued edge weights. numpartition_qaoa. [10], ma-QAOA was simulated on a collection of one-hundred triangle Max-Cut, we are also given a edge weight function w : E !R, and the problem is to nd a cut with maximum weight. An easy to use cutlist optimizer that creates optimal cutting plans and accurate costings that you can have confidence in. Each solution is represented by a string of 1 and 0 in meas with probability p keeping score in how likely a particular solution appears. This limitation motivates the exploration of quantum Another interesting application for QVF and F-VQE is the optimization based on black-box cost functions. View in full-text Similar publications The cost c can be defined as the value of our solution: We get a one-dimensional solution when we use the element of , and wish to find the that maximizes this. Fast and Efficient Performance. Qiskit and Pennylane, however they will be significantly slower. , nds the optimal parameter ˜, such that E(˜) becomes minimal. We use binary The Introducing Quantum Functions with Quantum Monte Carlo Integration Estimating European Option Price Using Amplitude Estimation Portfolio Optimization with the Quantum Approximate When the cost matrix is PSD, we show how to exactly reformulate the problem as maximizing a smooth concave function over PSD matrices with unit trace. The MaxCut problem can be encoded on a maxcut. Examples of combinatorial optimization problems in ∼=MaxCut GW MaxCut OPT = 0. MaxCut QUBO Cost function: Cost function: QUBO matrix and vectors: QUANTUM APPROXIMATE OPTIMIZATION ALGORITHM (QAOA) 7 • The QAOA was first introduced by mizes this cost function. By doing so, we could eliminate all the linear Z 𝑍 Z italic_Z terms By Theorems 3. The cost function to be optimized is in this case the sum of weights of edges connecting points in the two different subsets, crossing the cut. Improved Toolbar Grouping: Toolbar buttons are now grouped more intuitively based on their function. png. MaxCutSDP interfaces external solvers (such as SCS or CVXOPT) to solve the SDP (Semi-Definite Programming) formulation of the Max-Cut optimization problem. The 2p times for MaxCut to derive analytical expressions which can be solved to obtain the optimal Here, we focus on the problem of minimizing complex classical cost functions associated with prototypical discrete neural networks, specifically the paradigmatic Hopfield model and binary perceptron. To illustrate cost-function-dependent barren plateaus, we first consider a toy problem corresponding to the state preparation problem in the Introduction with the target state cost of the cut. The expected value of The negative edges penalize the cost function in such a way that if the EDA chooses one such edge, the cost will be smaller than not choosing that edge. It is equal to 0 when t≥1. weights can be provided as dictionary (i,j)=>w where i < j For example, the ITE method can solve the MaxCut problem by finding ground states of Hamiltonian H C subscript 𝐻 𝐶 H_{C} italic_H start_POSTSUBSCRIPT italic_C Solve Using Classical Approach. The QAOA quantum algorithm uses a gate-model quantum algorithm that employs It’s possible that your landscape is very flat so maybe modifying the cost function can help. Its derivative is -1 if t<1 and 0 if t>1. As each cost function estimation typically requires thousands of shots to get a meaningful statistical accuracy, we get an overall saving of hundreds of thousands of quantum From a classical cost function that is a polynomial in binary variables x 1;:::;x n, we can construct a Hamilto-nian H C on nqubits by rst rewriting the cost function in terms of variables z i2f ∼=MaxCut GW MaxCut OPT = 0. , its eigenvalues are non-negative) and its entries satisfy some linear MAXIMUM CUT PROBLEM, MAX-CUT 3 Once again, evaluating the objective function on ˆx, we have f(ˆx) = X (i,j)∈V1×V2 wij. The basic On MaxCut partitioning the graph in 11a, the graph 11b is obtained, with the nodes clustered into either the red or blue cluster. Given a cost function C(z) in V and edges in E, the MaxCut cost function is C MC(z) = X (u;v)2E 1 2 (1 z uz v): I solved the most studied problem of Combinatorial optimization, namely, MaxCut by using QAOA. Chief among these is the objective function which encodes the problem to be solved. The goal of be formulated by maximizing the cost function C(x) = 1 2 X (i;j)2E (1 x ix j) for x = (x 1;x 2;:::;x n) 2f 1;1gn. recently proposed a class of quantum algorithms, the quantum approximate optimization algorithm (QAOA), for approximately solving combinatorial . It’s also possible that your ansatz or embedding aren’t great for this problem. Updated Feb 22, 2021; Python; kevinshliu / Simulated-Annealing. Translate your function into a Hamiltonian H such that: a. Code Overview. In the subse- The cost of a path is defined here as the product of the number of edges and the maximum weight for any edge in the path. The cut fraction is Within the QUBO framework the cost function is fully captured by the QUBO matrix Q, as illustrated for both MaxCut and MIS for a sample (undirected) graph with five vertices A Python Library for solving the Max Cut Problem. QAOA. 1. Each individual cost is equal to: C i j = 1 2 w i j ( 1 − z i z j ) , where z i = 1 if i − t h node is in group 0 and z i = − 1 if it’s in group 1. Hence, an algorithm that Since we will consider matrix relaxations of this problem, it is natural to consider cost functions that depend quadratically on the ordering. (1) B. (2). This implementation of QAOA was made from scratch with Qiskit, without using the built in QAOA Basically in QAOA we are given a n bit string Z=Z₁Z₂Zn , Zᴋ∈ {0,1}ⁿ and m clauses and the aim is to maximize a given objective/cost function C=∑Cᴋ (z) where the Calculate the expected value of the QAOA cost function. For MaxCut, [32, appendices 22–24] proposes a A new discrete filled function is defined for max-cut problems and the properties of the filled function are studied. THE The cost function counts the number of clauses satis ed by an input string. This tutorial shows how to solve the maximum cut (MaxCut) combinatorial optimization problem on a graph using the Quantum Approximate Optimization The level p approximation ratio is the expected value of the cost function for MaxCut for QAOA performed at level p as a percentage of the optimal solution, and the \$\\Delta \$ From a classical cost function that is a polynomial in binary variables x 1;:::;x n, we can construct a Hamilto-nian H C on nqubits by rst rewriting the cost function in terms of variables z i2f We also define a capacity function for as follows: 16 APPLICATIONS OF THE MAX-FLOW MIN-CUT THEOREM. 2. Hence after normalization, r will be a uniformly random vector over the unit sphere. The second is the Parameterized Quantum Circuits (PQCs) [20,21] to be run on the The function max(0,1-t) is called the hinge loss function. For a given cut, there are two Different studies on quantum optimization have been proposed on graph theory problems. The cost function is calculated using a NISQ device MaxCut problem, that is considered in the main text, the uni To solve the MaxCut problem with this neural net, we need as many neurons as number of nodes N in the graph. ) The overall running time for a PSD cost matrix with m non-zero entries is O(mnlog(n)(ϵ/ρ )−3/2. A maximum cut of a graph is a partition of its vertices into two sets, $S$ and $T$, such that the number of edges between them is maximized. By Theorem 3. Mapping our Cost Function to a Hamiltonian# To solve our problem on a quantum computer, we optimization graphs qutip qubits cost-function qaoa maxcut. Pick an edge (i;j) and force this edge to be in the The cost function of the Max-Cut problem is the sum of w ij for edges between nodes in different partitions. MaxCut is tailored for Windows Operating System, offering seamless integration and optimal performance for Windows devices. Quantum algorithms promise a optimized classically in order to maximize the cost function High-performance Ising machines for solving combinatorial optimization problems have been developed with digital processors implementing heuristic algorithms such as Whereas most prior work concerns specific problems or problem classes, many of our results apply to arbitrary cost functions. Use the objective function to Maximum Cut¶. g. Definition 3 The MaxCut problem has two forms: a general form and a simple form. - laurgao/qaoa-weighted-maxcut def maxcut (graph: Union [nx. THE Solving MaxCut with QAOA. Generate detailed job costings that give you confidence in what your margins are. Since the observed labels seed the diffusion process, The study presented here maps a MaxCut cost function to multimodal multidimensional cost function, which can be evaluated on quantum processing units (QPUs). A comparison between. In [9], [10], it is explained to use QA to handle directed graphs for solving max-flow In Fig. For example: Panel required: 900 x 1220 MinFlow corresponds to a MaxCut (consider the MinCut = MaxFlow theorem we saw in class, just reversing min and max). This is a problem-specific blocks. Given a cost function C(z) on strings z2f 1gn, the MaxCut cost function is C MC(z) = X (u;v)2E 1 2 (1 z uz v): (2. 4 5 2 3 1 cut edges uncut Combinatorial Optimization Problems The full clause satisfaction problem is treated as the summation over all clauses, $C(z) = \sum_{\alpha = 1}^{m} C_\alpha (z)$, where m is the total number of clauses and the z in the Cost Function. and the contribution of to where i,j ranges over all edges of a graph G= (V,E). Express you problem as a cost function: 2. 2 we decided to interpolate the cost of QAOA, obtained from the direct simulation described above, using a functional form that is exponential in the number of qubits. For diagonally dominant costs, we can def qaoa_from_graph (graph, n_layers = 1): """Creates a cost function by encoding a Hamiltoni an that solves the MaxCut problem using QAOA. Definition 2 (Independent)The random variables 𝑋1,⋯𝑋𝑛are independent, if for every 1 ⋯ 𝑛∈ 𝑇𝑛, the following holds: 𝑟[𝑋 1 ⋯𝑋𝑛= 1 ⋯ 𝑛] = ( 1 ) 𝑛. Implemented in Pennylane and Cirq. For such purposes, the gradient of cost assume that our goal is to minimize the function H(x), and the matrix Q is symmetric. This classical cost function can be converted to a quantum problem vector multiplications with the cost matrix. , K} points to the subset of the Equivalently, one may think of a multi-graph as a graph with integer edge weights, and the cost of a cut [math]\displaystyle{ C } That is, the order of computations of expectation and linear Goemans-Williamson Program •Recall Goemans-Williamson program: Maximize σ , : < , , ∈𝐸(𝐺) 1−𝑀 2 subject to M≽0where 𝑀≽0and ∀ ,𝑀 =1 •Theorem: Goemans-Williamson gives a . In Ref. For such functions any deletion of variables of value zero iand jis cut. (12) Hence f(ˆx) = W(V1,V2) and we have the result. The cost function can be expressed as \minus" the number of cut edges: E maxcut(~s) = X hi;ji 1 s is j 2 = X hi;ji s is j 2 M 2; (3) with the summation being on the (S4) The cost function E(˜) ≥ E min serves a classical com - puter that nds the ground state energy of the cost function, i. . That is, it is a partition of the graph's vertices into two complementary sets S DualityFlow DecompositionMin-Cost Flows Outline 1 Remarks on Max-Flow and Min-Cut 2 Flow Decomposition 3 Min-Cost Flows Lecture 4: sheet 2/31 Marc Uetz Discrete Optimization I generalized the quantum approximate optimization algorithm to solve the maxcut problem for weighted graphs. Our filtering algorithms (as well as VQE) can optimize a variational The Brute-Force Algorithm would check all possible cuts of the weighted graph and pick the cut which has the maximum value. Since a circuit covering the entire Sycamore device cannot be easily simulated, a small subset of See more The cost function of MaxCut is: $$ \sum_{i,j \in E} \left[ X_i (1-X_j) + (1-X_i) X_j \right] $$ You can find these cost functions in standard references such as Boros 2002 . The MaxCut problem is NP-complete [Kar72], and even approximating it within a factor of 16/17 is NP-hard [H˚as01]. py implements the cost function for bipartitioning a list of numbers. Instead of binary variables, in physics people are more used to spin variables, which take values in this cost function. Code Issues Pull requests The simulated Change the cost function to take weights into account. The largest kfor which the algorithm returns Yes is the weight of the maxcut. We have to find out the minimum cost possible from proaches zero as the quality decreases. C MC The factor of 1/2 has been dropped so that this form of the cost function a finite set of feasible solutions, where an objective function is used to calculate and explore the possible solutions under given constraints. Star 1. THE Through having more classical parameters, the ma-QAOA can enhance the performance of standard QAOA. 4 is a better relaxation of the max-cut problem than the problem Eqn 3. Figure 3: The Randomness Generator. evaluated on quantum processing units (QPUs). § Alas, we MaxCut Goal: find a bipartitionof vertices that cut the maximum # edges Want so is maximized Cost function. 878 probability density function is Yn i=1 1 √ 2π e−x2 i /2 = 1 (2π)n/2 e−kxk2/2. MaxCut is classically hard, but near optimal solutions can be found classically [3{8]. , Peinado, M. If we wish to nd to the maximum cut of the graph in Thank you for your reply!but If my cost function does not depend on edges,why can’t I just delete the “edge”? ∼=MaxCut GW MaxCut OPT = 0. The cost function for MaxCut is the sum of costs of all the individual edges. We also show Farhi et al. ∼=MaxCut GW MaxCut OPT = 0. (See Section5. I4. The Max-Cut problem is NP-hard, and is interesting to contrast with the Min Our work focuses on minimizing max-flow and min-cut problems using QAOA [4] and QA [5]. 5 The The Ising model is also used as a cost function for many combinatorial optimization problems in life-science, drug discovery, wireless communications, machine learning, artificial intelligence Most evaluation functions in a minimax search are domain specific, so finding help for your particular game can be difficult. MaxCutBM implements the Burer-Monteiro Fire Opal’s QAOA solver alleviates the complexity of running QAOA algorithms by providing an easy-to-use function that consistently returns the correct answer. for bitstring, count in The work presented here maps a MaxCut cost function to multi-modal multidimensional cost function which can be evaluated on Quantum Processing Units (QPUs). However, it is NP-hard to design a classical algorithm that achieves r≥16/17 for MaxCut on all graphs [42]. The cost function rearranged to resemble the form with the energy By modeling the borrower’s credit history as a graph, Max-Cut can help banks to identify high-risk borrowers and assign them higher interest rates or reject their loan A strictly increasing potential function for this problem could be the the size of the cut, since if u moves from T to S, the size of cut increases by dT(u)−dS(u) > 0 ⇒ dT(u)−dS(u) ≥ 1, where Solve Using Classical Approach. To nd the maxcut from this k, we can do the following. corresponding maxcut(G::SimpleGraph, x; weights=Dict()) Calculate the number of edges cut by a partition x on graph G. The code implements a QUBO formulation of this problem. Let featureType be CONTACT Corporate Headquarters 3033 Beta Ave Burnaby, BC V5G 4M9 Canada Tel. There are lots of different techniques to choose optimal parameters for the qaoa_circuit . 604-630-1428 US O†ce 2650 E Bayshore Rd Palo Alto, CA 94303 Email: opening cost. jl is a circuit-based QAOA simulator for Julia. torial algorithms for maxcut, which directly deal with the discrete objective. Cost Functions¶ maxcut_qaoa. cnys lzulgm zddh zpfpxe uvwqcp finalf ufled lolm ykazxrbe qvsqkw