Data encoding is a key step in almost all ap-plications of quantum computation, but one that is not very well understood in general. Extensively researched in quantum machine learning, it is pivotal to exploiting the speedups offered by quantum algorithms. Failure to efficiently represent data risks weakening or nullifying prospective quantum advantages. While naive methods, such as basis encoding, will almost always work, they are extremely inefficient in terms of qubit use, rendering them infeasible for near-term applications. We focus on data encoding for genomics, a deeply data-driven field with computationally intensive algorithms throughout, from the initial DNA sequencing to pangenome assembly and more. We first examine the compressibility of genomic data from an information-theoretic perspective and how compression affects the quantum state preparation complexity. Here, there are two opposing forces at work: compression reduces data size but at the cost of the beneficial structure within the data. We review some standard approaches to data encoding in the context of genomics data from considering both the preparation complexity in terms of gate counts, qubit use, and depth, as well as the usefulness of the resulting states. We also present some new data encoding methods designed specifically for genomics that have a low-depth preparation circuits while still maintaining the essential features the algorithms require.
Data encoding is a key step in almost all ap-plications of quantum computation, but one that is not very well understood in general. Extensively researched in quantum machine learning, it is pivotal to exploiting the speedups offered by quantum algorithms. Failure to efficiently represent data risks weakening or nullifying prospective quantum advantages. While naive methods, such as basis encoding, will almost always work, they are extremely inefficient in terms of qubit use, rendering them infeasible for near-term applications. We focus on data encoding for genomics, a deeply data-driven field with computationally intensive algorithms throughout, from the initial DNA sequencing to pangenome assembly and more. We first examine the compressibility of genomic data from an information-theoretic perspective and how compression affects the quantum state preparation complexity. Here, there are two opposing forces at work: compression reduces data size but at the cost of the beneficial structure within the data. We review some standard approaches to data encoding in the context of genomics data from considering both the preparation complexity in terms of gate counts, qubit use, and depth, as well as the usefulness of the resulting states. We also present some new data encoding methods designed specifically for genomics that have a low-depth preparation circuits while still maintaining the essential features the algorithms require.
Quantum state preparation is the process of producing a target quantum state that will be used as the input to a quantum circuit. Many quantum algorithms require an input state where amplitudes, phases, or basis probabilities encode problem data, and the cost of preparing this state can be significant in gate count and circuit depth. This review summarizes common goals, assumptions, and methods for quantum state preparation, with emphasis on preparing states from classical vectors, probability distributions, and feature data used in quantum machine learning. We organize approaches by the information they load and by the resources they require, including gate count, circuit depth, qubit overhead, and classical preprocessing. It also compares exact and approximate preparation procedures, and discusses how precision targets affect cost. The review highlights links between families of methods, typical sources of resource estimates, and criteria that help match a preparation method to a task and hardware constraints.
Encoding classical data into quantum systems is a foundational step in the execution of nearly all quantum algorithms, and a critical bottleneck in realizing practical quantum advantage. This review provides a comprehensive account of the concepts, algorithms, and practical considerations associated with quantum data encoding. We trace the development from its early conceptual foundations to recent advances, considering commonly used access models, such as quantum state preparation, unitary synthesis, QRAM and block encoding. We survey the circuit size, depth, space-time tradeoffs, as well as non-Clifford resources required for fault-tolerant implementation. We also discuss the roles of different access models in quantum algorithms. Special attention is given to structured data, such as sparse data, Boolean functions and data represented by tensor networks. This review bridges theory and applications, serving both as a pedagogical guide for newcomers and as a reference for active researchers. We also highlight the pivotal role of quantum data encoding in quantum computing and provide insights into future directions that will enable quantum advantage.
We assess the potential of quantum computing to accelerate computation of central tasks in genomics, focusing on often-neglected theoretical limitations. We discuss state-of-the-art challenges of quantum search, optimization, and machine learning algorithms. Examining database search with Grover's algorithm, we show that the expected speedup vanishes under realistic assumptions. For combinatorial optimization prevalent in genomics, we discuss the limitations of theoretical complexity in practice and suggest carefully identifying problems genuinely suited for quantum acceleration. Given the competition from excellent classical approximate solvers, quantum computing could offer a speedup in the near future only for a specific subset of hard enough tasks in assembly, gene selection, and inference. These tasks need to be characterized by core optimization problems that are particularly challenging for classical methods while requiring relatively limited variables. We emphasize rigorous empirical validation through runtime scaling analysis to avoid misleading claims of quantum advantage. Finally, we discuss the problem of trainability and data-loading in quantum machine learning. This work advocates for a balanced perspective on quantum computing in genomics, guiding future research toward targeted applications and robust validation.
Quantum image computing has attracted attention due to its vast storage and faster processing of image data than classical computers. Although classical computing power has grown substantially in the last decade, its abilities have plateaued, struggling to meet the needs of huge datasets. Quantum computers, on the other hand, take advantage of unique qualities like quantum parallelism, superposition, and entanglement to provide faster solutions to complicated problems. Managing huge image data in classical systems necessitates significant memory and hardware resources, which makes quantum computing a possible alternative. Several approaches have arisen for encoding and compressing classical images in the quantum processor. However, one of the most significant limitations is the complexity of quantum state preparation, which translates pixel coordinates to corresponding quantum circuits. Current approaches for representing large-scale images require a huge number of quantum resources including qubit, and qubit connection, which presents significant hurdles. Therefore, this article aims to overview the pixel intensity and state preparation circuit that needs fewer connections, i.e., quantum gates to represent quantum image data, and then explore and design effective compression and processing methods for medium and high-resolution images. This work specializes in emerging quantum models’ potential for enhancing image representation and compression capabilities. It also conducts a comprehensive study of quantum image representation and compression techniques, categorizing existing methods by grayscale and color image types and evaluating their methods, strengths, and limitations. It focuses on crucial difficulties, such as high qubit requirements and complex state preparation, that limit scalability for huge images. This paper also assesses each model’s compression efficacy, which will help future research focus on efficient circuit designs with lower complexity for medium to high-resolution images. It is an essential reference for advancing quantum image processing research since it provides a systematic framework for evaluating quantum image compression and representation algorithms.
The explosive growth of genomic information calls for computational methods to handle complex sequence analysis and genome assembly assays. Based on the principles of superposition and entanglement, quantum computing represents an another way of computation to achieve potentially faster and more efficient operations than classical systems. This article offers a comparative overview of 4 major studies dedicated to the use of QC in genomics, spanning theoretical algorithmical conceptualization, systematic literature mapping, analyses on practical challenges in QC-aided genomics and hybrid Q-C designs. The review indicates that there is potential for significant speed-up based on theoretical formulation, while current applications are limited by hardware resource constraint, data-loading latency and scalability. Hybrid models, in particular those that hybridize quantum annealing with classical optimization, seem to offer the most practicable route to near-term genomic computing. The analysis also shines a light on the increasingly pivotal positions that quantum-inspired approaches and hybrid orchestration systems are playing as crucial stepping stones towards full-quantum advantage. The results suggest that quantum computing is in its infancy as far as genomic analysis is concerned but the potential seems to be promising for future bioinformatics research and applications. Such opportunities are only accessible with further advancements in quantum hardware, data encoding and benchmark development.
DNA sequencing allows for the determination of the genetic code of an organism, and therefore is an indispensable tool that has applications in Medicine, Life Sciences, Evolutionary Biology, Food Sciences and Technology, and Agriculture. In this paper, we present several novel methods of performing classical-to-quantum data encoding inspired by various mathematical fields, and we demonstrate these ideas within Bioinformatics. In particular, we introduce algorithms that draw inspiration from diverse fields such as Electrical and Electronic Engineering, Information Theory, Differential Geometry, and Neural Network architectures. We provide a complete overview of the existing data encoding schemes and show how to use them in Genomics. The algorithms provided utilise lossless compression, wavelet-based encoding, and information entropy. Moreover, we propose a contemporary method for testing encoded DNA sequences using Quantum Boltzmann Machines. To evaluate the effectiveness of our algorithms, we discuss a potential dataset that serves as a sandbox environment for testing against real-world scenarios. Our research contributes to developing classical-to-quantum data encoding methods in the science of Bioinformatics by introducing innovative algorithms that utilise diverse fields and advanced techniques. Our findings offer insights into the potential of Quantum Computing in Bioinformatics and have implications for future research in this area.