Description:FORM 2 THE PATENTS ACT, 1970 (39 of 1970) & THE PATENT RULES, 2003 COMPLETE SPECIFICATION (See Section 10 and Rule 13) Title of invention: METHOD AND SYSTEM FOR REDUCING TIME COMPLEXITY OF DENSITY FUNCTIONAL THEORY CALCULATIONS WITH QUBITIZED DIAGONALIZATION Applicant Tata Consultancy Services Limited A company Incorporated in India under the Companies Act, 1956 Having address: Nirmal Building, 9th floor, Nariman point, Mumbai 400021, Maharashtra, India Preamble to the description: The following specification particularly describes the invention and the manner in which it is to be performed. TECHNICAL FIELD The disclosure herein generally relates to physical/chemical systems, and, more particularly, to a method and system for cubic-scaling wall of density functional theory calculation with Qubitized node diagonalization. BACKGROUND Current software packages calculate the energetics and other physical properties of physical/chemical systems using a quantum mechanical (QM) electronic self-consistent field approach called the Kohn-Sham Density functional theory (KS-DFT). However, a computational time complexity bottleneck of the DFT approach has remained a cubic function of the number of electronic orbitals. This is also known as the cubic scaling wall bottleneck in DFT. Most industrially relevant chemical systems in Material sciences industry are bulk materials. One supercell made from a collection of neighboring unit cells involves around thousands of atoms which corresponds to several thousands of electronic orbitals even in the least correlated basis. Currently these chemical systems are treated approximately within the QM/Molecular Mechanical (MM) approach, where only a subsystem with strong quantum correlations is simulated using DFT and the rest is treated only via the MM technique. Identifying these subsystems requires doing prior Molecular mechanical and/or Force field analysis and adds to additional overhead cost. The gap in applying this DFT technology to the complete system arises from the cubic scaling wall bottleneck i.e. for a 50000 electronic orbital system (corresponding to a supercell of the bulk material) computing even one KS-DFT step would require 0.001 secs on supercomputer FUGAKU having Rmax of 442 PETA Flops. For all practical purposes the DFT code should converge within 100 self-consistency steps therefore to simulate one large supercell on FUGAKU would require 0.1 seconds. For real world simulations one would require screening across several lakhs of molecular configurations in a supercell and that would require more than one day. The output electronic density computed from DFT needs to be in turn passed into Force-Field or Molecular mechanical modelling suites that then recomputes the geometric positioning of atoms and the DFT energies needs to be recomputed. Altogether that implies that for slightly larger system such calculations can enter several days of calculations even on the largest of the supercomputers. Similarly, the chemical systems in Pharma industry that correspond to Protein drug or protein-protein systems involve more than thousands of atoms which corresponds to a several thousands of electronic orbitals. These chemical systems are treated approximately within the QM/Molecular Mechanical (MM) approach, only a subsystem is treated with strong quantum correlations and is simulated using DFT and the rest is treated only via the MM technique. Drug molecules in the pharma industry have more than 500 Molecular weight, screening across different drug molecules enters across several days of efforts. SUMMARY Embodiments of the present disclosure present technological improvements as solutions to one or more of the above-mentioned technical problems recognized by the inventors in conventional systems. For example, in one embodiment, a processor implemented method is provided. The method involves obtaining an electron density value as input. Further, a computational complexity for the obtained input is computed, by performing, via the one or more hardware processors, a Kohn-Sham Hamilton simulation of the input. Further, one or more eigen states of the input are determined, via the one or more hardware processors, by iteratively performing, for each of a plurality of density values: obtaining a) a parameterized similarity transformed matrix, b) a matrix equation for the parameterized similarity transformed matrix, and c) a quadratic polynomial equation system for a plurality of parameters; constructing a Jacobian and Hessian from residual of a current set of parameters from among the plurality of parameters; computing a simplified form of a unitary transformation, if normalized value of product of the Jacobian and Hessian is below a threshold; computing a unitary transformation matrix from the simplified form of the unitary transformation; and calculating a plurality of eigen values from a plurality of column vectors of the unitary transformation matrix, wherein the plurality of eigen values represent the one or more eigen states. Further, the one or more eigen states of the input are mapped, via the one or more hardware processors, to a recursive sequence of nonlinear least squares problem solved by executing a Quantum linear system algorithm at every step, wherein the mapping causes reduction in the computational complexity of the input. In an embodiment of the method, computing each of the plurality of density values comprises: obtaining a diagonalization equation; performing an eigen decomposition of an overlap matrix in the diagonalization equation; performing a unitary transformation based iterative diagonalization of a Fock Matrix in the diagonalization equation, based on values from the eigen decomposition of the overlap matrix, to obtain a diagonalized Fock matrix; and obtaining the density value by performing the eigen decomposition of the diagonalized Fock matrix. In another embodiment of the method, performing the eigen decomposition comprises iteratively reducing dimension of the overlap matrix, wherein at each iteration of a plurality of iteration, a matrix with a reduced dimension as compared to matrix in a previous iteration is generated, and wherein the plurality of eigen values are calculated for each of the matrices with the reduced dimension. In yet another embodiment, a system is provided. The system includes one or more hardware processors, a communication interface, and a memory storing a plurality of instructions. The plurality of instructions cause the one or more hardware processors to obtaining an electron density value as input. Further, a computational complexity for the obtained input is computed, by performing, via the one or more hardware processors, a Kohn-Sham Hamilton simulation of the input. Further, one or more eigen states of the input are determined, via the one or more hardware processors, by iteratively performing, for each of a plurality of density values: obtaining a) a parameterized similarity transformed matrix, b) a matrix equation for the parameterized similarity transformed matrix, and c) a quadratic polynomial equation system for a plurality of parameters; constructing a Jacobian and Hessian from residual of a current set of parameters from among the plurality of parameters; computing a simplified form of a unitary transformation, if normalized value of product of the Jacobian and Hessian is below a threshold; computing a unitary transformation matrix from the simplified form of the unitary transformation; and calculating a plurality of eigen values from a plurality of column vectors of the unitary transformation matrix, wherein the plurality of eigen values represent the one or more eigen states. Further, the one or more eigen states of the input are mapped, via the one or more hardware processors, to a recursive sequence of nonlinear least squares problem solved by executing a Quantum linear system algorithm at every step, wherein the mapping causes reduction in the computational complexity of the input. In yet an embodiment of the system, computing each of the plurality of density values comprises: obtaining a diagonalization equation; performing an eigen decomposition of an overlap matrix in the diagonalization equation; performing a unitary transformation based iterative diagonalization of a Fock Matrix in the diagonalization equation, based on values from the eigen decomposition of the overlap matrix, to obtain a diagonalized Fock matrix; and obtaining the density value by performing the eigen decomposition of the diagonalized Fock matrix. In yet another embodiment of the system, performing the eigen decomposition comprises iteratively reducing dimension of the overlap matrix, wherein at each iteration of a plurality of iteration, a matrix with a reduced dimension as compared to matrix in a previous iteration is generated, and wherein the plurality of eigen values are calculated for each of the matrices with the reduced dimension. In yet another aspect, a non-transitory computer readable medium is provided. The non-transitory computer readable medium includes a plurality of instructions, which causes one or more hardware processors to initially obtaining an electron density value as input. Further, a computational complexity for the obtained input is computed, by performing, via the one or more hardware processors, a Kohn-Sham Hamilton simulation of the input. Further, one or more eigen states of the input are determined, via the one or more hardware processors, by iteratively performing, for each of a plurality of density values: obtaining a) a parameterized similarity transformed matrix, b) a matrix equation for the parameterized similarity transformed matrix, and c) a quadratic polynomial equation system for a plurality of parameters; constructing a Jacobian and Hessian from residual of a current set of parameters from among the plurality of parameters; computing a simplified form of a unitary transformation, if normalized value of product of the Jacobian and Hessian is below a threshold; computing a unitary transformation matrix from the simplified form of the unitary transformation; and calculating a plurality of eigen values from a plurality of column vectors of the unitary transformation matrix, wherein the plurality of eigen values represent the one or more eigen states. Further, the one or more eigen states of the input are mapped, via the one or more hardware processors, to a recursive sequence of nonlinear least squares problem solved by executing a Quantum linear system algorithm at every step, wherein the mapping causes reduction in the computational complexity of the input. In yet an embodiment of the non-transitory computer readable medium, computing each of the plurality of density values comprises: obtaining a diagonalization equation; performing an eigen decomposition of an overlap matrix in the diagonalization equation; performing a unitary transformation based iterative diagonalization of a Fock Matrix in the diagonalization equation, based on values from the eigen decomposition of the overlap matrix, to obtain a diagonalized Fock matrix; and obtaining the density value by performing the eigen decomposition of the diagonalized Fock matrix. In yet another embodiment of the non-transitory computer readable medium, performing the eigen decomposition comprises iteratively reducing dimension of the overlap matrix, wherein at each iteration of a plurality of iteration, a matrix with a reduced dimension as compared to matrix in a previous iteration is generated, and wherein the plurality of eigen values are calculated for each of the matrices with the reduced dimension. It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the invention, as claimed. BRIEF DESCRIPTION OF THE DRAWINGS The accompanying drawings, which are incorporated in and constitute a part of this disclosure, illustrate exemplary embodiments and, together with the description, serve to explain the disclosed principles: FIG. 1 illustrates an exemplary system for reducing computational complexity of DFT based energy calculation, according to some embodiments of the present disclosure. FIG. 2 is flow diagram depicting steps involved in the process of reducing computational complexity of DFT based energy calculation, according to some embodiments of the present disclosure. FIG. 3 is a flow diagram depicting steps involved in the process of computing each of the plurality of density values for reducing computational complexity of DFT based energy calculation, by the system of FIG. 1, in accordance with some embodiments of the present disclosure. FIGS. 4A and 4B depict experimental results associated with the reduction of computational complexity of DFT based energy calculation by the system of FIG. 1, in accordance with some embodiments of the present disclosure. DETAILED DESCRIPTION OF EMBODIMENTS Exemplary embodiments are described with reference to the accompanying drawings. In the figures, the left-most digit(s) of a reference number identifies the figure in which the reference number first appears. Wherever convenient, the same reference numbers are used throughout the drawings to refer to the same or like parts. While examples and features of disclosed principles are described herein, modifications, adaptations, and other implementations are possible without departing from the scope of the disclosed embodiments. For all practical purposes the DFT code should converge within 100 self-consistency steps therefore to simulate one large supercell on FUGAKU would require 0.1 seconds. For real world simulations one would require screening across several lakhs of molecular configurations in a supercell and that would require more than one day. The output electronic density computed from DFT needs to be in turn passed into Force-Field or Molecular mechanical modelling suites that then recomputes the geometric positioning of atoms and the DFT energies needs to be recomputed. Altogether that implies that for slightly larger system such calculations can enter several days of calculations even on the largest of the supercomputers. Similarly, the chemical systems in the Pharma industry that correspond to Protein drug or protein-protein systems involve thousands of atoms which corresponds to a several thousands of electronic orbitals. These chemical systems are treated approximately within the QM/Molecular Mechanical (MM) approach, only a subsystem is treated with strong quantum correlations and is simulated using DFT and the rest is treated only via the MM technique. Drug molecules in the pharma industry have more than 500 Molecular weight, screening across different drug molecules enters across several days of efforts. In order to address these challenges, a method and system provided in the embodiments disclosed herein initially obtains an electron density value as input. Further, a computational complexity for the obtained input is computed, by performing a Kohn-Sham Hamilton simulation of the input. Further, one or more eigen states of the input are determined by iteratively performing, for each of a plurality of density values: obtaining a) a parameterized similarity transformed matrix, b) a matrix equation for the parameterized similarity transformed matrix, and c) a quadratic polynomial equation system for a plurality of parameters; constructing a Jacobian and Hessian from residual of a current set of parameters from among the plurality of parameters; computing a simplified form of a unitary transformation, if normalized value of product of the Jacobian and Hessian is below a threshold; computing a unitary transformation matrix from the simplified form of the unitary transformation; and calculating a plurality of eigen values from a plurality of column vectors of the unitary transformation matrix, wherein the plurality of eigen values represent the one or more eigen states. Further, the one or more eigen states of the input are mapped to a recursive sequence of nonlinear least squares problem solved by executing a Quantum linear system algorithm at every step, wherein the mapping causes reduction in the computational complexity of the input. Referring now to the drawings, and more particularly to FIG. 1 through FIG. 4, where similar reference characters denote corresponding features consistently throughout the figures, there are shown preferred embodiments and these embodiments are described in the context of the following exemplary system and/or method. FIG. 1 illustrates an exemplary system for reducing computational complexity of DFT based energy calculation, according to some embodiments of the present disclosure. The system 100 includes or is otherwise in communication with hardware processors 102, at least one memory such as a memory 104, an I/O interface 112. The hardware processors 102, memory 104, and the Input /Output (I/O) interface 112 may be coupled by a system bus such as a system bus 108 or a similar mechanism. In an embodiment, the hardware processors 102 can be one or more hardware processors. The I/O interface 112 may include a variety of software and hardware interfaces, for example, a web interface, a graphical user interface, and the like. The I/O interface 112 may include a variety of software and hardware interfaces, for example, interfaces for peripheral device(s), such as a keyboard, a mouse, an external memory, a printer and the like. Further, the I/O interface 112 may enable the system 100 to communicate with other devices, such as web servers, and external databases. The I/O interface 112 can facilitate multiple communications within a wide variety of networks and protocol types, including wired networks, for example, local area network (LAN), cable, etc., and wireless networks, such as Wireless LAN (WLAN), cellular, or satellite. For the purpose, the I/O interface 112 may include one or more ports for connecting several computing systems with one another or to another server computer. The I/O interface 112 may include one or more ports for connecting several devices to one another or to another server. The one or more hardware processors 102 may be implemented as one or more microprocessors, microcomputers, microcontrollers, digital signal processors, central processing units, node machines, logic circuitries, and/or any devices that manipulate signals based on operational instructions. Among other capabilities, the one or more hardware processors 102 is configured to fetch and execute computer-readable instructions stored in the memory 104. The memory 104 may include any computer-readable medium known in the art including, for example, volatile memory, such as static random-access memory (SRAM) and dynamic random-access memory (DRAM), and/or non-volatile memory, such as read only memory (ROM), erasable programmable ROM, flash memories, hard disks, optical disks, and magnetic tapes. In an embodiment, the memory 104 includes a plurality of modules 106. The plurality of modules 106 include programs or coded instructions that supplement applications or functions performed by the system 100 for executing different steps involved in the process of reducing computational complexity of DFT based energy computation, being performed by the system 100. The plurality of modules 106, amongst other things, can include routines, programs, objects, components, and data structures, which performs particular tasks or implement particular abstract data types. The plurality of modules 106 may also be used as, signal processor(s), node machine(s), logic circuitries, and/or any other device or component that manipulates signals based on operational instructions. Further, the plurality of modules 106 can be used by hardware, by computer-readable instructions executed by the one or more hardware processors 102, or by a combination thereof. The plurality of modules 106 can include various sub-modules (not shown). The plurality of modules 106 may include computer-readable instructions that supplement applications or functions performed by the system 100 for reducing the computational complexity of DFT based energy computation. The data repository (or repository) 110 may include a plurality of abstracted piece of code for refinement and data that is processed, received, or generated as a result of the execution of the plurality of modules in the module(s) 106. Although the data repository 110 is shown internal to the system 100, it will be noted that, in alternate embodiments, the data repository 110 can also be implemented external to the system 100, where the data repository 110 may be stored within a database (repository 110) communicatively coupled to the system 100. The data contained within such external database may be periodically updated. For example, new data may be added into the database (not shown in FIG. 1) and/or existing data may be modified and/or non-useful data may be deleted from the database. In one example, the data may be stored in an external system, such as a Lightweight Directory Access Protocol (LDAP) directory and a Relational Database Management System (RDBMS). In an embodiment, the system 100 maybe a quantum computing system having capability to perform functions/calculations as detailed in the embodiments disclosed herein, for reducing the computational complexity of DFT based energy computation. Functions of the components of the system 100 are now explained with reference to the steps in flow diagrams in FIGS. 2 and 3. FIG. 2 is a flow diagram depicting steps involved in the process of reducing computational complexity of DFT based energy calculation, according to some embodiments of the present disclosure. In an embodiment, the system 100 comprises one or more data storage devices or the memory 104 operatively coupled to the processor(s) 102 and is configured to store instructions for execution of steps of the method 200 by the processor(s) or one or more hardware processors 102. The steps of the method 200 of the present disclosure will now be explained with reference to the components or blocks of the system 100 as depicted in FIG. 1 and the steps of flow diagram as depicted in FIG. 2. Although process steps, method steps, techniques or the like may be described in a sequential order, such processes, methods, and techniques may be configured to work in alternate orders. In other words, any sequence or order of steps that may be described does not necessarily indicate a requirement that the steps to be performed in that order. The steps of processes described herein may be performed in any order practical. Further, some steps may be performed simultaneously. At step 202 of method 200 in FIG. 2, the system 100 obtains an electron density value as input. The electron density value corresponds to a crystal being analyzed for energy estimations. In an embodiment, the input is obtained via one or more suitable channels provided by the I/O interface 112. Further, at step 204 of the method 200, the system 100 computes a computational complexity for the obtained input, by performing, via the one or more hardware processors 102, a Kohn-Sham Hamiltonian simulation of the input. Solving the Kohn-Sham Hamiltonian corresponds to solving a generalized eigenvalue equation for a N*N matrix, i.e. a Fock matrix in this context. Solving the eigenvalue equation has a known complexity of O(N^3). The method 200 uses a quantum computational method to recursively diagonalize the Fock matrix, wherein at every step one eigenvalue and one eigenvector are found and a block diagonal matrix having dimension (N-1)x(N-1) is created.. This step is again repeated, to get the next eigenvalue and eigenvector. At every step, obtaining the eigenvalue and eigenvector involves a O(N) computational complexity. Depending on the number of eigenvalues and eigenvectors to be computed, O(kN) complexity is obtained, wherein k is the number of eigenvalues and eigenvector. This way the system 100 uses the quantum computational approach to achieve a speed-up = (O(kN)/O(N3))=O(k/N2), which corresponds to polynomial speed-up of the Density functional theory calculations. Further, at step 206 of the method 200, one or more eigen states of the input are determined, via the one or more hardware processors 102, by iteratively performing steps 206a through 206e, for each of a plurality of density values. At every step of the Density functional theory, an outer-loop the input is an electronic density value that enables the computation of the Kohn-Sham Hamiltonian, from which the eigenvectors and eigenvalues are computed by solving a generalized eigenvalue equation. At every subsequent iteration, the eigenvectors from previous iteration are used to calculate the new electronic density, using the method 300. After every iteration, a conditional statement is checked that the difference between two subsequent electronic density values is below a threshold or not to fulfill a convergence criterion. The difference is above the threshold, then the system 100 determines that convergence has not been achieved, and then calculates next value of the electronic density. At step 206a, the system 100 obtains a) a parameterized similarity transformed matrix, b) a matrix equation obtained from the parameterized similarity transformed matrix, and c) a quadratic polynomial equation system for a plurality of parameters associated with rotations angles encoded within the parameterized similarity transformation. The similarity transformed matrix is obtained by applying a similarity transformation on the matrix M. At this stage the system 100 considers a most general form of a parametrized similarity transformation that can be used to obtain one eigenvalue and one eigenvector. The matrix equation is obtained by based on a preset condition/requirement that under the similarity transformation in the transformed matrix the Nth diagonal entry of the matrix is to be decoupled from the rest N-1 entries in the column. That is in the transformed matrix the first N-1 entries in the last column apart from the last Nth entry is zero. This in turn leads to a square system of quadratic polynomials (i.e. N-1 equations) in terms of the N-1 parameters of the parametrized similarity transformation. Further, at step 206b, the system 100 constructs a Jacobian and Hessian from residual of a current set of parameters from among the plurality of parameters. At this step, though structure of the parametrized similarity transformation is fixated, the parameters are to be determined such that they enable computation of one eigenvector one eigenvalue. For this purpose, one or more solution parameters are selected such that they fulfill a system of polynomial quadratic equations. That is N-1 parameters and N-1 quadratic equations. The set of parameters are determined from the system of polynomial equations, and in solving the system of polynomial equations for obtaining the set of parameters, a 2nd order gradient descent based optimization method is used by using a set of initial values for the parameters. These get updated from one optimization step to the next. At each step, the present step parameters before the update are taken as the current parameters. For solving the N*N system the number of current parameters is N-1, which get updated at every optimization step, wherein after every optimization step, the final solution parameters are obtained. Solving the N*N system corresponds to solving a nonlinear least squares problem. Further, at step 206c, the system 100 computes a simplified form of a unitary transformation, if normalized value of product of the Jacobian and Hessian is below a threshold. Further, at step 206d, the system 100 computes a unitary transformation matrix from the simplified form of the unitary transformation. Further, at step 206e, the system 100 calculates a plurality of eigen values from a plurality of column vectors of the unitary transformation matrix, wherein the plurality of eigen values represent the one or more eigenstates. In these steps, if the system of polynomial equations is fulfilled by the solution parameters within a specified degree of tolerance, then the system 100 considers the similarity transformed matrix as obtained with an accuracy set by the tolerance. From the similarity transformation matrix then the unitary transformation matrix is obtained. Further, from the unitary transformation matrix, the eigen values and the eigen vectors are obtained. Equations supporting the aforementioned steps are given below. F= ?_(ij=1)^N¦?F_ij |+ i??j¦| ? ---- (1) O_p=1- (?_(j=1)^(p-1)¦?|+ j?+ ? ?_(j=p+1)^N¦?|+ j? ?) ?p|t_j ¦ --- (2) (1-|+ p??p¦|) O_p^(-1) FO_p (|+ p??p¦|)=0 --- (3) ?_(i=1,i ?p)^N¦(F_ip-?_(j=1,j?p)^N¦?F_ij t_j+ t_i F_pp ?- ?_(j=1,j?p)^N¦?t_i F_pj t_j)|+ i??p¦|=0 ? ) --- (4) f_i (t)= F_ip- ?_(j=1,j?p)^N¦?F_ij t_j+ t_i F_pp ?- ?_(j=1,j?p)^N¦?t_i F_pj t_j ?=0 --- (5) Here (1) is the mathematical representation of matrix F, |i> is called a ket and refers to the row index and