Quantum bits are actual physical systems of a photon with a specific polarization or an ion trapped in a magnetic field. a qubit is described as the basic information unit of a quantum computer. A qubit is different from a classical computer bit which can be either 0 or 1 at any one time but a qubit can be 0 and 1 simultaneously. Observe Figure 2 where the behavior of the coin shows the classical and quantum physics stating the key difference between the deterministic and probabilistic systems which introduces the quantum superposition and entanglement. In classical the coin has two possible states Head or Tail when it is spinning it lands on either head or tail so it has a chance of 50% for both head and tail. In quantum, the coin blends in both head and tail calculating the probability of the states and giving the state that has a high probability value. This is called superposition which can make quantum computers solve many problems altogether hence making them so efficient for specific tasks.

Qubits are basics units of quantum computer systems and are physically implemented through certain entities such as ions subjected to magnetic fields. A qubit can be in any state other than 0 and 1 as Schrodinger’s cat is alive and dead state at the same time (Black et al., 2002). A qubit exhibits one of the following collapse states when observed? Interactions with a single qubit can affect the entire state, as groups of qubits can correlate. Qubits, which are denoted by the complex coefficients α and β, exist in a superposition of the states 0 and 1, whereas bits are the fundamental units of traditional computing.

In Figure 3 watching a qubit changing its state, these coefficients pertain to physical measurements. In the domain of quantum information, the qubit, represented by the Dirac notation |0⟩ and consisting of two states, serves as the comparable entity. |1⟩, where |•⟩ denotes a quantum state. The main difference between quantum and classical information is that, as Equation 1 illustrates, a qubit can exist in any superposition of the states |0⟩ and |1⟩.

The frequencies of the distinct states in quantum computing are denoted by the complex coefficients α and β. As a fundamental principle of quantum physics, these amplitudes are highly susceptible to the impact of physical measurement. A qubit’s state will be altered during the measurement process, by the principles of quantum mechanics (Dejpasand and Sasani Ghamsari, 2023), if it is in a superposition of potential measurement outcomes. As a qubit collapses into its measured state, its amplitudes lose all information. Complex language patterns and relationships in biological data can be represented by qubits, which are capable of existing in superposition states. Simultaneously including numerous linguistic aspects or representations, QNLP models provide a more comprehensive analysis of biological texts, analogous to how a qubit can exist in a superposition of states.

In addition, entanglement is a quantum phenomenon that qubits are capable of manifesting, in which the states of two qubits become coupled irrespective of their separation. It is easier to imagine two qubits as two magic dice. In classical the flipping of two dice results is independent of each other. As shown in Figure 4, in quantum they are “entangled,” then flipping one die immediately reveals the outcome of the other no matter where the two are located across the room, across the country, across the universe! Their results are perfectly correlated, even though the outcomes are random and unpredictable. This magical connection does not work the way we observe other connections that are usual to us but is a natural component of quantum mechanics.

The experimental data show that any quantum calculation that does not require entanglement can be carried out at a slightly slower pace on a classical computer. When computing is associated with systems, which involve many qubits that are susceptible to quantum entanglement, one fully understands the meaning of computing. Entanglement is defined as any process that takes place on a single qubit influences the total state of the whole set of qubits.

Figure 5 represents system consists of two qubits, with each qubit capable of existing in a superposition of the states |0⟩, |1⟩, the combined system can also exist in any superposition of the states |00⟩, |01⟩, |10⟩, |11⟩, and so forth (or any of the 2^N binary strings from |0…0.0⟩ to |1…0.1⟩ in the case of an N-qubit system). The so-called Bell states, which are significant in the context of quantum entanglement (Wong, 2019), are among these superpositions. This is illustrated by Equation 2.

The underlying assumption is based on the potential of the quantum computer to work at data-intensive large volumes. In order to understand this, let us consider a quantum system of N qubits at our disposal. If the state of the system is not entangled, the number of amplitudes in a state of this system is equal to 2^N, where amplitudes of states of each qubit in the system are summed. When the system becomes entangled, however, these amplitudes all become independent and the qubit register as a whole transform into a 2 N-dimensional vector (Tao, 2024). By modeling and analyzing interrelated links between biological things or concepts represented in textual data, the concept of quantum entanglement is utilized. Similar to how quantum entanglement enables the correlation of qubits, QNLP represents and comprehends the intricate interrelationships among proteins, illnesses, genes, and other biological components by employing this idea.

Quantum interference is employed in the computation of quantum computers with the help of Equation 3, which is helpful. However, acknowledging the fact that quantum computers are inherently noisy and do not always provide accurate results and decisions, they always utilize probability to provide the best guess or most likely occurrence anticipated. Besides locations, quantum computers use the energy level of qubits or spin to do computations (Simmons, 2024). This can be expressed using the Born rule, which states that the probability of measuring a particular outcome x given a quantum state represented by a density matrix ρ is given by:

The weight of each state in the superposition is determined by the complex probability amplitudes, denoted as c₁ and c₂, which are represented by Equation 4. The interference of various states may result in interference effects in the final state ∣ψ_total⟩, which can manifest as observable phenomena like interference patterns in experiments involving interference, such as the double-slit experiment.

The interference element (c1c2*) in the superposition formula has the potential to induce either constructive or destructive interference, contingent upon the relative phases of 𝑐1 and 𝑐2. The interference behavior described here is an essential component of quantum physics and has far-reaching implications for quantum communication, computation, and other technologies.

The processing and analysis of textual data may be improved with the application of quantum interference (Torlai and Melko, 2020). Utilizing interference effects, QNLP models are capable of conducting more complex and context-aware analyses by modeling language elements and relationships as quantum states (Pseiner et al., 2024). Language modeling, sentiment analysis, and information extraction from biological texts are a few examples of the tasks in which this may result in enhanced precision.

Quantum information can be manipulated by performing quantum gates wherein physical operations are utilized by using laser pulse for the ion qubits and optical elements for the photonic qubits. Unlike their more often than not conceptual definitions, quantum gates have to be described as unitary matrices by strict quantum mechanical demands. When a quantum gate is performed on at least two entangled qubits, then a 2 N × 2 N matrix is multiplied by a 2 N entity. The fact that quantum computers can register and manipulate roughly 2 N quantities of information using a number of operations equal to N forms the basis of a possible exponential quantum edge over classical computers.

For quantum gates to be used in the normalizing of quantum states, they have to be unit and linear, or act on superposition (Klimov et al., 2024). But, unlike classical computing that involves only one nontrivial gate, namely the NOT gate per bit, quantum computing can perform an infinite number of one-qubit quantum gates. It has been established that any quantum gate can be approximated from a basis set of gates which only includes the single qubit gate and the two qubit controlled NOT gate. The exponential number of gates may hinder the application of good approximations should the need arise in the future. Language properties and relationships can be transformed by quantum gates and therefore, more complex tasks can be performed (Van Vu et al., 2024).

The development of quantum algorithms that are well suited to AI tasks such as data analysis, optimization, and machine learning has elicited a lot of attention from academic and industrial pioneers (Preskill, 2018). These algorithms exploit the specific features of quantum computers and, thus, perform calculations, which makes artificial intelligence operations more complex and faster compared to their counterparts based on classical computers (Patel et al., 2023). The properties of classical and quantum computing that distinguish their respective methods of operation are compared in Table 1.

Having discussed the fundamental principles of quantum computing, the next section focuses on how these principles are adapted and implemented in QNLP methodologies, particularly in bioinformatics applications.

While we have the classical embeddings like Word2Vec or Glove, which map words in the high-dimensional vector space where quantum embedding maps words or phrases as quantum states as observed in Figure 6. This could lead to improved levels of comparison between text analysis and semantic similarity assessments of texts. In few of the research, the advantages of quantum embedding in NLP tasks were highlighted. One of the works suggested a quantum embedding model based on quantum circuits. By mapping words or phrases into quantum states through quantum gates, our paradigm allows more accurate and effective control and description of linguistic features compared to traditional methods (Nam and Nguyen., 2024). A unique approach inspired by quantum mechanics is presented in this study, which utilizes embeddings to facilitate biomedical text-mining tasks including entity detection and relation extraction. By exploiting quantum computing principles, this approach transforms high-dimensional quantum states into biological concepts and relationships (Samanta et al., 2016; Baiardi et al., 2023).

One approach is to use amplitude encoding, where each word is encoded as a quantum state is represented in Equation 5:

where, |i⟩ represents the basis states, and α_i are the complex amplitudes corresponding to the word w. An innovative methodology utilizes the principles of quantum computing to encode relationships and concepts in biomedicine into high-dimensional quantum states.

Quantum Natural Language Processing (QNLP) is a recent and fascinating application of quantum computing that seeks to represent the meaning of sentences as vectors encoded into quantum computers (Abbaszade et al., 2021). It achieves this by extending the distributional meaning of words to encompass the compositional meaning of sentences, a concept known as the DisCoCat model (Martinez and Leroy-Meline, 2022). This model employs an algorithm based on tensor products to compose the vectors representing the meanings of words through the syntactic structure of the sentence. One striking aspect of this approach is that while the algorithm is inefficient on classical computers, it exhibits promising scalability when executed using quantum circuits.

One of the fundamental ideas underlying the convergence of quantum theory and natural language processing is the establishment of a direct link between linguistic features, such as syntactic structures and semantic meanings, and quantum states (Surov et al., 2021). As illustrated in Figure 5 (Yeung and Kartsaklis, 2021), the DisCoCat framework serves as a network-like language for accomplishing this relationship through the use of string diagrams as given in Figure 7. This approach is an integral part of the longstanding tradition of computational linguistics, which has sought efficient methods for describing language structures and meanings in machine-accessible formats (Tsujii, 2021). The distributional approach relies on statistical analysis of word contexts based on the distributional hypothesis. In contrast, the symbolic approach focuses on individual word meanings and the compositionality of sentences. The symbolic approach, rooted in theoretical linguistics, posits that the meaning of a sentence depends on the meanings of its constituent words and the grammar used to arrange them (Ganguly et al., 2022). One of paper shows how DisCoCat allows QNLP to classify biomedical abstracts by building sentence embeddings that retain structural and content-based characteristics. By mapping the syntactic dependencies of sentences to quantum states, the framework improves the identification of functional/contextual similarities in biology articles (Steedman and Baldridge, 2011). However, this approach has seen limited success in natural language processing applications, where the distributional paradigm, based on statistical analysis, currently dominates (Liu et al., 2024).

Quantum language models (QLM) is a kind of quantum-inspired neural network model that defines language units, such as words and phrases, as quantum states in Hilbert space and create text using quantum algorithms, which may result in exponential speedups compared to classical models. Complex patterns in language data can be efficiently learned by the Quantum Boltzmann Machine (QBM), allowing for more precise language production and modeling (Wiebe et al., 2019). The present study introduces a novel quantum circuit-based QLM architecture and showcases its efficacy in various domains, including text classification, sentiment analysis, and language modeling (Shuyue et al., 2023). The existing body of research primarily represents word sequences as a classical mixture of word states, which fails to adequately utilize the capabilities of a probabilistic quantum description (Campbell et al., 2024). As of yet, a comprehensive quantum model that explicitly captures the non-classical correlations inside word sequences has not been created (Yu et al., 2020). A neural network architecture has been suggested, using an innovative Entanglement Embedding (EE) component, to convert word sequences into entangled pure states of many-body quantum systems. The word sequences exhibit robust quantum entanglement, a fundamental principle of quantum information and a sign of parallelized correlations among the words (Chen et al., 2023; Figure 8).

Equation 6 represents the quantum circuit to estimate the probability distribution over words, given a context as follows:

Where, |ψ(c)⟩ is the quantum state representing the context, U_w is a unitary operator corresponding to the word w, and ⟨ψ(c)|U_w|ψ(c)⟩ is the probability amplitude of observing w given c.

In the field of bioinformatics, QLMs predict the probability of a word in a context so that functions such as sequence alignment have been accomplished (Liang et al., 2023). Linguistic features are processed in quantum circuits which enhances the accuracy of sentiment to be derived from research abstracts or clinical data (D'Aloisio et al., 2024). Potential advantages of this quantum approach to estimating the probability distribution across words over classical methods include a more effective capacity to capture complex dependencies and context information (Jayanth et al., 2023).

These techniques not only capture complex linguistic relationships but also lay the foundation for addressing bioinformatics where semantic precision is critical. In the following section, we explore how Quantum techniques are used in the QNLP tasks.

Machine learning (ML) is a branch of Artificial intelligence that enables systems to learn patterns from data and make predictions. NLP focuses on equipping computers to comprehend, interpret, and generate human language and ML uses that data to generate predictions, decisions, and classifications. This ML integration in NLP is observed in Figure 9.

Quantum Machine Learning (QML) combines quantum computing with machine learning to enhance data processing by leveraging quantum properties like superposition and entanglement, offering exponential speedups and richer data representations (Das Sarma et al., 2019). Within QML, Quantum Natural Language Processing (QNLP) specializes in applying quantum principles to linguistic tasks, encoding text as quantum states and enabling efficient processing of language structures. Like sentiment analysis, semantic parsing, and relation extraction to scale efficiently while uncovering deeper patterns in language using quantum-enhanced embeddings and kernels. As Bioinformatics involves analyzing and interpreting large volumes of biological data, such as genomic sequences, protein structures, and gene expression data. QNLP benefits from QML’s advanced computation, many problems in this field can be formulated as machine learning tasks, such as classification, clustering, and pattern recognition (Repetto et al., 2024; Ghoabdi and Afsaneh, 2023).

Quantum machine learning (QML) has emerged as a promising approach to tackle these challenges, leveraging the principles of quantum mechanics to potentially enhance the computational capabilities of classical machine learning algorithms. The following concepts are provided for further enhancement of QML techniques.

Quantum simulation involves using quantum computers to solve and model quantum systems which cannot even be approximated by classical computers due to the exponential resource requirement. It opens the chance to model complicated dependencies in the structures of language which is in concordance with the outlook of QNLP. Conducting new studies in this field has been made to explore many approaches to quantum simulation that can offer explanations of numerous quantum phenomena in various fields. A quantum circuit model which encodes the quantum system into a sequence of quantum gates acting on an initial state. As proposed in a recent study (Durán et al., 2023), the dynamics of a quantum system can be simulated using Equation 9:

Where |Ψ(0)⟩ is the initial state, U(t) is the time-evolution operator implemented as a quantum circuit, and |Ψ(t)⟩ is the final simulated state after time t. The study demonstrated an efficient simulation of quantum problems using this approach. Another study (Miranda et al., 2022) explored quantum annealing for simulating quantum systems, encoding the system into an energy landscape described by a quantum Hamiltonian. The quantum annealing process finds the ground state of the Hamiltonian, corresponding to the simulated system state.

Optimising new quantum algorithms and approximations of quantum circuits for large scale problems. In a recent work by Edward et al. (2024), the authors presented a quantum simulation strategy to simulate biomolecular events including protein folding changes using near-term quantum computers. They explained the basic ideas of the simulation of protein model and showed that quantum computers could be used for investigations of biomolecular systems. For this, they created a quantum algorithm that would be able to efficiently simulate quantum dynamics of the biomolecular system, using quantum characteristics such as superposition and entanglement. Magann et al. (2021) studied the application of quantum simulation in protein–ligand interactions because these interactions play a vital role in the discovery of drugs and engineering of proteins. They proposed a quantum algorithm for the quantum dynamics of a protein-ligand system and pondered over the benefits of applying quantum computing over the classical approach to this sphere. Cao et al. (2018) discussed the use of quantum simulation to study the Protein-DNA binding, which is critical in gene control and drug development. They designed a quantum algorithm for seeking an optimal binding of a protein to a DNA sequence. They debated the significance of such findings for future studies of gene regulation and the development of treatments that modify protein-DNA interactions.

The major issues in bioinformatics can be addressed using mathematical programming, which involves identifying the solution that provides the minimum or maximum value to a specified objective function. Such problems occur when the search space is large; therefore, it is computationally expensive when solved using conventional algorithms (Reali et al., 2017). The objective function is in turn mapped to a Quantum Hamiltonian which describes a wave operator for a quantum system. The goal is to find an optimal solution of the optimization problem which is the ground state energy value of the Hamiltonian.

Let us consider a general optimization problem:

Minimize f(x) Subject to x ∈ S where f(x) is the objective function, and S is the set of feasible solutions. In quantum optimization, this problem can be mapped to a quantum Hamiltonian, H, such that the ground state of H corresponds to the optimal solution(s) of the problem. Equation 10 expresses the quantum Hamiltonian mathematically by:

Where: 𝐻o represents the initial Hamiltonian, 𝐻𝑝 is the problem Hamiltonian encoding the objective function and constraints, and 𝛾 is a parameter controlling the weight of 𝐻𝑝 in the overall Hamiltonian.

The goal is to find the ground state of the Hamiltonian H, which can be achieved using quantum algorithms such as quantum annealing or the quantum approximate optimization algorithm (QAOA; Perdomo-Ortiz et al., 2012). Quantum annealing is a process where the quantum system is initialized in the ground state of H_0 and then slowly evolves towards the ground state of H by gradually increasing the value of γ. This process exploits quantum phenomena like superposition and tunneling to explore the vast solution space more efficiently than classical algorithms.

The QAOA is an iterative algorithm that alternates between applying a mixer operator and a phase operator to an initial quantum state. The mixer operator introduces quantum entanglement, while the phase operator encodes the objective function and constraints. The parameters of these operators are optimized to find an approximate solution to the optimization problem.

Mathematically, Equation 11 represents the QAOA as follows:

Where ∣ψ(γ,β) is the quantum state prepared by the circuit, parameterized by vectors 𝛾 and 𝛽,𝐻𝐵 and HC are the mixing and cost Hamiltonians respectively, representing the classical and quantum parts of the optimization problem,𝛽𝑝,𝛾𝑝 are the parameters controlling the evolution of the quantum state,∣𝜓0⟩ is an initial state, The circuit prepares a state that is expected to encode the optimal solution to the optimization problem and the parameters 𝛾 and 𝛽 are chosen to optimize the objective function.

These techniques optimize parameters in quantum linguistic embeddings, enabling tasks like accurate biomedical question-answering and semantic search. Quantum optimization has shown promise in multiple sequence alignment (MSA), which is a fundamental problem in bioinformatics. The objective of MSA is to discern areas of similarity across numerous biological sequences, hence offering valuable insights into functional conservation and evolutionary connections. Utilizing the quantum features of superposition and tunneling, a recent work (Benedetti et al., 2017) developed a quantum annealing methodology for MSA that more effectively explores the huge solution space than classical methods. Protein structure prediction is yet another bioinformatics use of quantum optimization. The optimization issue of deducing the three-dimensional structure of a protein from its amino acid sequence is extremely difficult and involves a large number of local minima. Scholars have investigated the application of quantum annealing and other techniques for quantum optimization to resolve this issue (Patti et al., 2022). Additionally, quantum optimization has been implemented to address the issue of inferring gene regulatory networks. The aforementioned networks symbolize the intricate interplay between genes and their regulatory factors. Deducing these networks from experimental data necessitates the resolution of extensive optimization challenges. A recent study (Mokhtari et al., 2024) introduced a method for deducing gene regulatory networks using quantum annealing, capitalizing on the quantum computer’s enhanced efficiency in traversing the extensive solution space. In addition, additional bioinformatics issues, including drug discovery (Onodera et al., 2023), phylogenetic tree reconstruction (Bach et al., 2024), and genomic sequence assembly (Boev et al., 2021), have demonstrated the potential of quantum optimization.

While these theoretical advances demonstrate the potential of QNLP, their true impact becomes evident in addressing practical challenges in bioinformatics. The following sections explore how these QNLP techniques are applied to bioinformatics problems, such as drug discovery, protein structure prediction, and genomic sequence analysis.

The process of drug discovery means virtual screening and identification of lead compounds as the major stages. These procedures estimate the affinities and selectivity of potential drugs to the target biomolecules. In this context, studies have been conducted to enhance the accuracy and efficiency of the procedures of QNLP, alongside machine learning and quantum computing models. In this approach, QNLP tools are used to search biomedical literature for information about potential drug candidates and their structures and targets (Gorgulla et al., 2022). This information is then employed and incorporated to commence quantum simulations of these drug candidates and the target biomolecules at the quantum level. QNLP can extract and incorporate useful knowledge from large volumes of text data more effectively than conventional approaches; quantum simulations allow for realistic modelling of the interaction of molecules and their properties.

Previous studies have revealed that QNLP methods are very useful in predicting and analyzing the drug-target interactions that are so important in the drug discovery phase. Some studies have investigated how quantum-based machine learning algorithms, using big databases of known drug–target associations and molecular conformations, can be used to predict new interactions and the underlying processes (Ginex et al., 2024). This approach shows the possibility of using QNLP in conjunction with other quantum computing methods for a comprehensive analysis of the interactions between drugs and targets for their use in drug discovery and development.

These techniques as a group demonstrate the usefulness of QNLP in understanding the drug discovery process. As we shall see, by utilizing properties of quantum computing that are intrinsically superior to classical computing, such as quantum parallelism and entanglement, one can hope for QNLP techniques to surpass classical models in terms of their ability to identify novel interactions and intricate patterns and structures within the data.

Quantum machine learning models and quantum simulations have been shown in several recent works to be capable of predicting binding affinities and interactions between potential drugs and a target biomolecule, often a protein or nucleic acid. Mensa et al. presented a quantum machine learning model for virtual screening that utilized quantum features of the problem to incorporate quantum-mechanical effects inherent in molecular interactions (Mensa et al., 2022). Their approach demonstrated enhanced precision in the prediction of binding affinities from the traditional computational techniques. Arguing the same idea, Mohammed et al. (2017) have established a hybrid quantum mechanical/molecular mechanics model of virtual screening and identification of lead compounds. To this end, their model used both quantum simulations and machine learning algorithms to predict the binding affinities and interactions of the potential drugs with the target biomolecules. The authors showed that due to the application of quantum effects, the precision of predictions made with the help of the proposed model exceeded classical approaches, which indicates the applicability of quantum computing in this field. Thus, it has been suggested that for virtual screening and identification of lead compounds, it is possible to use hybrid quantum–classical methods based on the advantages of QNLP and quantum simulations. Ting and Caflisch (2010) described a pipeline that combines QNLP for mining chemical/biological information from text sources with quantum calculations for estimating the interactions of potential drugs with target biomolecules.

In a study by Sathan and Baichoo (2024) the authors proposed a quantum machine learning model for drug-target interaction prediction, demonstrating its potential to outperform classical models. Their approach involved training a quantum neural network on a large dataset of known drug-target interactions and molecular structures, leveraging the unique capabilities of quantum computing to capture the complex patterns and relationships within the data. Similarly, Veleiro et al. (2023) Combined Transformer and Graph Neural Networks, these architectures, models capture both global and local structural information of drugs and sequence features of targets, improving precision and recall in DTI predictions (Khurana et al., 2023).

A notable study by Mohammed et al. (2017) further highlighted the potential of quantum machine learning models in this domain. The authors trained a hybrid quantum-classical neural network on a large dataset of drug-target interactions and molecular structures, demonstrating its ability to outperform state-of-the-art classical models in predicting novel interactions. Their work also explored the interpretability of the quantum model, providing insights into the underlying mechanisms of drug-target interactions. Xiong et al. (2023) proposed a framework called “Q-Drug” that aims to bring drug design into the quantum space using deep learning techniques. This framework incorporates QNLP for literature mining, quantum simulations for modeling molecular interactions, and quantum graph neural networks for predicting drug-target interactions. The integration of these components aims to provide a comprehensive and efficient computational framework for drug discovery and design. Similarly, a recent study by Sathan and Baichoo (2024) proposed a quantum graph neural network for drug-target interaction prediction, which utilizes QNLP for extracting relevant information from biomedical literature and integrates it with protein structure data and quantum simulations of molecular interactions. The Table 3 below provides details of quantum approaches applied to various drug discovery tasks, highlighting their datasets, performance metrics, and specific QNLP tasks.

The Table 3 presents different QNLP approaches, including quantum language models, quantum machine learning models (e.g., quantum neural networks, quantum transformers, quantum graph neural networks), quantum kernel methods, quantum generative adversarial networks, and quantum attention models. Various computational models in these tasks showcase their comparative analysis over classical NLP and QNLP methods. The research emphasized the capability of QNLP methods to efficiently investigate the extensive chemical space and detect prospective medication candidates. Nevertheless, these practical implementations also unveiled many obstacles. Prominent challenges that need to be addressed include the encoding and representation of data, the optimization of algorithms, and the restricted scalability of existing quantum technology (Selig et al., 2021). Furthermore, the implementation of QNLP methodologies in conjunction with pre-existing drug discovery pipelines and the analysis of quantum-derived outcomes presented pragmatic obstacles.

It is a fundamental metric in structural bioinformatics and computational chemistry for evaluating the accuracy of predicted protein structures. RMSD measures the average distance between the atoms of a predicted protein structure and the corresponding atoms in the experimentally determined native structure, providing a quantitative assessment of the structural similarity between the two structures (Olechnovič et al., 2019). The formula for calculating RMSD between two sets of coordinates, each containing n atoms, is given in Equation 12:

Here,piand rirepresent the coordinates of the 𝑖^th atom in the predicted and native structures, respectively. The term ‖pi−ri‖ denotes the Euclidean distance between the corresponding atoms in the two structures. In structural bioinformatics, RMSD is a key metric for assessing the quality of protein structure prediction algorithms and molecular docking simulations (Neveu et al., 2018). Low RMSD values indicate a high degree of similarity between the predicted and native structures, suggesting that the model accurately captures the protein’s folding pattern (Jumper et al., 2021). Researchers often use RMSD in conjunction with other metrics, such as Ramachandran plots and GDT (Global Distance Test), to provide a comprehensive evaluation of protein structure predictions and refine computational models for drug discovery and molecular biology applications.

It is another important metric used in structural bioinformatics to assess the quality of predicted protein structures. GDT measures the similarity between a predicted protein structure and the experimentally determined native structure by considering the distance between equivalent residues in the two structures (Poleksic, 2015). The GDT score is calculated as the percentage of residues in the predicted structure that are within a specified distance threshold of the corresponding residues in the native structure. The GDT score is typically calculated at different distance thresholds like 1 Å, 2 Å, 4 Å, and 8 Å to provide a comprehensive assessment of the structural similarity.

Mathematically, in Equation 13 the GDT score at a given distance threshold is calculated as follows:

D is the total number of residues (atoms) in the protein. GDTi is the fraction of residues for atom 𝑖 that fall within the distance thresholds.

The GDT score is expressed as a percentage, with higher scores indicating a greater degree of structural similarity between the predicted and native structures. A GDT score of 100% indicates perfect structural similarity, meaning that all residues in the predicted structure are within the specified distance threshold of the corresponding residues in the native structure. GDT provides a quantitative measure of the structural similarity between predicted and native structures. It complements other metrics such as RMSD and Ramachandran plots, offering researchers a comprehensive assessment of the accuracy of computational models in protein structure prediction.

Equations 14–16 is important to assess the overall performance of a binary classification model. It is particularly useful when the class distribution is imbalanced (Nunn et al., 2021).

For calculating F1-score:

Where:

The F1 score ranges from 0 to 1, where a higher score indicates better model performance. It provides a balance between precision and recall, making it a useful metric for evaluating models, especially when there is an imbalance between the two classes. It is important to assess the overall performance of a binary classification model.

It is a metric that evaluates how efficiently a quantum algorithm utilizes quantum resources such as qubits, gates, and circuit depth. It is often used to compare the efficiency of different quantum algorithms in terms of resource consumption. The exact formula for calculating quantum resource utilization can vary depending on the specific context and the factors considered (Hansen et al., 2023). However, a general formula could be constructed as follows:

Where, the total number of physical qubits required for quantum computation, the depth of the quantum circuit represents the number of sequential gates that need to be applied, and the number of logical qubits required to represent the problem being implemented. Equation 17 provides a measure of how efficiently quantum resources (such as qubits and gates) are being utilized to solve a particular problem or implement an algorithm. A lower QRU indicates more efficient resource utilization, while a higher QRU indicates that more resources are required for the computation (Lubinski et al., 2023). Table 8 provide the overview of evaluation metrics and its significance.

QNLP is a captivating domain within the field of bioinformatics, presenting prospective benefits in terms of computational economy, precision, and performance when compared to traditional approaches. Although benchmarking studies have shown competitive outcomes for tasks such as genomic sequence analysis (Shiny Duela et al., 2023) and protein structure prediction (Chow, 2024), the current limitations in error correction overhead and quantum hardware scalability prevent the practical implementation of the theoretical speedups offered by QNLP (Saggi et al., 2024). To address these obstacles, hybrid quantum-classical methodologies have surfaced, which involve the strategic delegation of computationally demanding duties to quantum processors while making use of classical resources to preprocess and post-process data (Zhou et al., 2024).

Nevertheless, QNLP techniques encounter intrinsic drawbacks, such as the complexity of encoding data, challenges in optimizing algorithms, and the relative youth of quantum hardware in comparison to the firmly established classical NLP environment. Adoption of QNLP in bioinformatics will ultimately depend on continuing algorithm development, progress in quantum computing, and a prudent comparison of the capabilities of classical and hybrid techniques to application-specific performance, accuracy, and scalability needs. However, addressing these obstacles through collaborative research and technological advancements will not only overcome existing limitations but also set the stage for groundbreaking developments in bioinformatics. As we transition into discussing future directions, we focus on the transformative possibilities that lie ahead for QNLPFuture Research Directions and Roadmap.

A highly auspicious domain for QNLP to be implemented is customized medicine and precision treatments. QNLP techniques could facilitate a more precise and effective examination of individual genomic data by harnessing the computational capabilities of quantum computing. This, in turn, could pave the way for the creation of customized treatments and personalized pharmacological regimens (Chow, 2024). Equipped with extensive multi-omics and clinical data, quantum machine learning models can discern intricate patterns and correlations, hence enabling the identification of previously undiscovered biomarkers and therapeutic targets.

De novo protein design and protein engineering are indispensable for the creation of novel biomaterials, enzymes, and medicines. When combined with quantum simulations and molecular modeling, QNLP methods have the potential to greatly revolutionize this field by enabling more accurate predictions of protein folding kinetics, structures, and functions (Shiny Duela et al., 2023). The application of quantum algorithms for the fast conformational sampling of proteins might help to develop new enzymes, designer proteins, and engineered biomaterials by defining sequences and structures that provide specific functions.

Using multiple omics data, including genomes, transcriptome, proteome, and metabolome, is crucial to develop systems-level models that capture complex cellular functions. Technique of QNLP can have a tremendous effect on this field as they can support the integration of data, patterns’ recognition, and knowledge search across different and diverse data (Saggi et al., 2024). It could be hypothesized that the existing relationships and interactions between multi-omics data sets could be learned by quantum machine learning and algorithms, providing new and previously unknown information about gene regulation networks, metabolic pathways, and disease etiology.

The combination of QNLP methods with molecular quantum simulations and quantum chemistry calculations may open a new era in drug discovery. Thanks to quantum computer computing capacities, the scientists were able to create detailed models of what happens with proteins and ligands, biochemical reactions, and other complex biomolecular interactions with superior accuracy and efficiency (Zhou et al., 2024). The use of these simulations may result in profound understanding of the molecular interactions of drugs, which in turn may help in the rational design of novel therapeutic agents and optimizing their selectivity, pharmacokinetic profile and affinity.

QNLP methods can be beneficial for the development of new sustainable bioeconomies and the conservation of biological diversity. By applying QNLP, it is possible to analyze large biological data sets, which include ecological data, environmental monitoring data, and genomics data to improve the efficiency of sustainable practices in agriculture, forestry and ecosystem management (Pal et al., 2024). If, for instance, unstructured data from environmental reports and scientific research was subjected to QNLP methods, then there would be knowledge and information that is relevant to the development of sustainable bioeconomies and conservation of biotic diversity. QNLP is expected to have further potential in bioinformatics and related fields and the development of quantum computing technology will extend basic biological research and development. To achieve these prospective uses, however, will require collective endeavours to overcome the challenges that arise from the scale of quantum technology, the algorithms, the data encoding and the application.

There is a potential for QNLP techniques to contribute to sustainable bioeconomies and support of initiatives related to the conservation of biological diversity. QNLP could help promote the development of sustainable practices in different industries including agriculture and forestry (Kirubakaran and Midhunchakkaravarthy, 2024) because QNLP can process big biological data which include genomic data, ecological data and environmental data. From genomic data of crop species QNLP algorithms can identify the right crop cultivars for resistance and climatic adaptability by feature extraction (Prasad et al., 2024). For the enhancement of the conservation approaches and ecosystem management, it could also allow the integration of the data on biological diversity from different sources such as field surveys, eDNA, and remote sensing (Di Sipio et al., 2021). In addition, the knowledge acquisition and insight generation techniques applicable to sustainable economies and biodiverse conservation (Sood and Chauhan, 2024) could be used with QNLP techniques to extract information from such unstructured text data as environmental reports and research articles. It is also possible that some of the sustainable practices in industries such as agriculture, forestry, fishery, and biotechnology might be supported by QNLP through the improvement of efficient knowledge search and decision-making. The opportunity to change the approach to the management of natural systems, as well as the development of bio-economies, and the protection of the biological wealth of the planet for future generations, is in the use of QNLP for environmental sustainability and the conservation of biological diversity.

The potential applications of QNLP provides its capacity to improve in various domains, from personalized medicine to sustainable bio-economies and biodiversity conservation. These advancements underscore the transformative potential of QNLP in addressing some of the most challenges in bioinformatics and beyond. However, it helps in realizing these opportunities will require progress in quantum hardware, algorithm development, and data integration techniques. The following section delves into the roadmap for overcoming these challenges, outlining actionable strategies and collaborative efforts needed to fully harness the power of QNLP in future research and applications.

The direction for the development of energy-saving quantum hardware technologies must be granted high importance. These include the analysis of new forms of qubit, such as quantum dots. Semiconductor nanostructures used in these qubits may allow them to operate at higher temperatures, thus rendering large-scale cooling unnecessary (Nayak et al., 2008). However, topological qubits, due to their grounding in the principles of topological matter, these qubits have an intrinsic immunity to external noise and dephasing. As a consequence, they might require less amount of energy to correct the errors and other expenses associated with the process. To achieve efficient quantum hardware, material scientists, specialists in energy efficiency, and engineers in quantum hardware will need to work together. To obtain reliable QNLP applications, it will be necessary to design quantum systems that are immune to errors. It is strongly believed that the performance of QNLP algorithms can be enhanced by the advancements in fault-tolerant architectures and quantum error-correcting codes. Further study of the more extensive quantum processors with more qubit numbers and longer coherence times is necessary. This would make it possible to apply more complicated QNLP models for tasks such as genome-wide association analysis and protein-ligand interaction analysis. Introducing louder architectures for quantum devices might reduce the impact of environmental decoherence and expand the usability of QNLP in actual problems of bioinformatics.

In tandem with developments in hardware, the development and optimization of quantum algorithms for sustainable bioinformatics applications should be another top priority. This implies the examination of quantum algorithms that have been developed to tackle specific problems, and these are some of the problems that may be solved by quantum computing; sustainable agriculture, protection of biodiversity, and development of environmentally friendly drugs (Andersson et al., 2022). It is anticipated that QNLP together with future generations of quantum computers will revolutionize predictive bioinformatics. They may make the realistic quantum computer modelling of different biomolecular processes, for instance protein folding and gene regulation, feasible on FTQC. With better qubit coherence and coherence times and with the deployment of scalable architectures in the future quantum systems, the solution to the computational challenges of the emulation of sophisticated biological structures will be realized and quantum advancements in biological fields will be made. For example, the incorporation of hybrid quantum-classical systems to preprocess the data classically before going to quantum circuits for sequence alignment task could optimise it. Subsequent studies should focus on improving data encoding, quantum embeddings, and quantum-classical combinations in order to minimize the computational load and energy costs associated with them. (Nammouchi et al., 2023).

Prescribing and following the best practicable procedures for eco-friendly computational research and sustainable software engineering is vital to reducing the carbon impact of QNLP in bioinformatics. Applying ideas like the “GREENER” approach which aims at developing principles and recommendations for the sustainable software development, maximization of resource utilization efficiency such as memory, storage space, data transfer, as well as the incorporation of energy-efficient computing solutions are parts of this process (Lannelongue et al., 2023).

To provide impartial and consistent assessments of the ecological consequences of QNLP algorithms and approaches, it is imperative to establish standardized benchmarking frameworks and processes for environmental impact evaluation (Strubell et al., 2020). Measuring the energy usage of quantum and classical computational resources Assessing the carbon footprint associated with the deployment and operation of QNLP solutions Evaluating the efficient use of hardware resources, such as qubits, memory, and storage should be incorporated into these frameworks to enable comparisons with traditional methodologies and to direct the optimization of sustainable QNLP solutions (Liu et al., 2021).

Establishing strong partnerships among quantum computing researchers, bioinformaticians, sustainability scientists, and industry stakeholders is imperative to effectively apply QNLP findings practically and sustainably. The establishment of interdisciplinary research institutes, collaborative initiatives, and platforms for knowledge exchange can expedite the development of sustainable QNLP solutions for bioinformatics and encourage the cross-pollination of ideas (Awschalom et al., 2021).

By executing this strategic blueprint and attending to these critical domains, the bioinformatics community can effectively utilize the paradigm-shifting capabilities of QNLP in a manner that is consistent with worldwide sustainability objectives and reduces the ecological repercussions of computational procedures. Creating libraries and tools for QNLP applications tailored to bioinformatics needs could accelerate the development and testing of novel algorithms on emerging quantum devices. Nevertheless, it is imperative to recognize that the achievement of sustainable QNLP in bioinformatics necessitates significant financial expenditures, enduring dedication, and interdisciplinary cooperation among many stakeholders (Quantum Technology and Application Consortium – QUTAC et al., 2021).

While the limitations of current quantum hardware present significant challenges, the future directions outlined suggest a promising trajectory for QNLP in bioinformatics. The discussion and conclusion section synthesizes the insights and their broader implications for the field.