Quantum Computing Challenges: Vision and Survey

Understanding Quantum Computing Challenges in 2026

Quantum computing challenges represent some of the most fascinating and consequential problems in modern technology. As researchers worldwide race to build practical quantum machines, they face a complex web of obstacles spanning hardware engineering, algorithm design, error correction, and software development. A comprehensive survey by Gill et al. (2025), published in the Elsevier book Quantum Computing: Principles and Paradigms, provides one of the most thorough examinations of these challenges and the vision driving the quantum computing industry forward.

The quantum computing landscape has transformed dramatically since Richard Feynman’s pioneering 1982 talk, where he first imagined a machine that could simulate quantum physics using quantum mechanical principles. Feynman’s core insight—that “a computer based on quantum mechanical fundamentals might be necessary to mimic natural occurrences, as Nature is fundamentally quantum mechanical”—set the stage for decades of research. Yet most scientists did not consider industrial quantum computers feasible until the late 1990s, and early hardware efforts moved at what researchers describe as “a snail’s pace” due to the extraordinary difficulty of shielding and coherently controlling quantum mechanical properties at atomic scales.

Today, quantum computing challenges span multiple domains simultaneously. Hardware teams struggle with qubit coherence times and error rates. Software developers work with tools that remain at “assembly-level” maturity compared to classical programming languages. Cryptographers face the dual challenge of protecting existing systems from quantum threats while developing quantum-native security protocols. This article synthesizes the key findings from the Gill et al. survey, offering a comprehensive exploration of where quantum computing stands today and where it is headed, designed for technology professionals, researchers, and anyone seeking to understand the transformative potential—and current limitations—of this revolutionary technology. For more insights on how emerging technologies are reshaping industries, explore our interactive library of technology reports.

Quantum Fundamentals: Qubits, Superposition, and Entanglement

To understand quantum computing challenges, one must first grasp the fundamental concepts that make quantum computation possible. At the heart of every quantum computer lies the qubit—the quantum analog of the classical bit. While classical digital computing relies on bits limited to two discrete values (‘0’ or ‘1’), a quantum bit can exist in a superposition of both states simultaneously. Mathematically, a qubit’s state is represented as a|0⟩ + b|1⟩, where a and b are complex amplitudes satisfying the normalization condition |a|² + |b|² = 1.

This seemingly simple difference unlocks extraordinary computational power. Quantum computers access a computational field known as Hilbert space, a mathematical domain of enormous dimensionality. With n qubits in superposition, a quantum system can represent 2ⁿ potential values at any given moment. As the survey emphasizes, “even a very limited number of qubits, N, can be used to solve problems that are intractable with classical computers, thanks to the rapidly expanding computational domain as an exponential function (2ᴺ).” To put this in perspective, just 300 qubits in full superposition could represent more states than there are atoms in the observable universe.

Entanglement represents another cornerstone of quantum computation. Unlike classical bits, which operate independently, qubits can be placed in entangled states where they persist in a correlated global configuration even when physically separated. All qubits in an entangled state can have their characteristics changed when only one is measured or probed. This property is invaluable for dense coding, quantum simulation of linked networks, and quantum communication protocols that form the basis of quantum cryptography.

However, measurement introduces a fundamental constraint. The final stage of quantum computation collapses the stochastic quantum state into a deterministic classical result. Quantum algorithms must be carefully designed so that the correct outcome has the highest probability of being measured, but the inherently stochastic nature of quantum mechanics means that no single measurement guarantees the correct result. Classical post-processing techniques—including majority voting, statistical estimation, or simply repeating the computation multiple times—are essential components of any practical quantum computing workflow.

Perhaps the most significant quantum computing challenge at the fundamental level is decoherence. This phenomenon occurs when qubits interact with their surrounding environment and lose their coherent quantum features. The survey identifies decoherence as “one of the biggest obstacles to developing large-scale quantum devices,” noting that qubits have very short coherence periods that are highly dependent on the specific qubit technology being used. Due to decoherence, quantum systems lose data extremely frequently, making error correction not just desirable but absolutely essential for practical quantum computation.

Quantum Computing Paradigms and Architecture Models

The quantum computing challenges researchers face vary significantly depending on which computational paradigm they employ. The survey identifies three primary approaches to quantum computation, each with distinct strengths, limitations, and application domains.

The quantum circuit framework (also called gate-based quantum computing) is the most widely pursued paradigm and represents the most feasible option for general-purpose quantum computation. Similar to how classical computers use logic gates to manipulate bits, gate-based quantum computers use quantum gates to manipulate qubits, exploiting entanglement and superposition to perform computations. This universal computational model supports the widest range of applications and allows quantum computers to be reprogrammed for different tasks. Both Shor’s algorithm for integer factorization and Grover’s algorithm for database search can be implemented within this framework. However, decoherence is the primary challenge for gate-based systems, making error correction the most critical practice.

Adiabatic quantum computing, typically implemented as quantum annealing, takes a fundamentally different approach. Rather than executing a sequence of gate operations, it relies on the natural tendency of quantum systems to find low-energy states. The system begins in a simple initial state and gradually evolves toward the ground state of a complex problem Hamiltonian, using quantum tunneling to traverse energy barriers. D-Wave Systems has commercialized this approach with processors containing thousands of qubits. Quantum annealing is somewhat less sensitive to certain types of computational errors compared to the gate model, making it a promising alternative for large optimization problems. However, it requires maintaining a coherent quantum state throughout the entire annealing process and is less versatile than gate-based approaches.

The third paradigm, measurement-based (one-way) quantum computing, represents a conceptually distinct approach where computation proceeds entirely through the sequential measurement of qubits in a highly entangled initial state called a cluster state. Each measurement effectively implements a quantum gate, and the choice of measurement basis determines the computation performed. While theoretically equivalent in power to the gate model, measurement-based quantum computing presents unique engineering challenges in preparing and maintaining the required large-scale entangled states.

Key Quantum Algorithms Driving Innovation

Quantum algorithms represent the software foundation upon which the promise of quantum computing rests. The development of quantum algorithms has a rich history, beginning with Daniel Simon’s presentation of the first quantum method demonstrated to outperform classical algorithms. Since then, several families of algorithms have emerged that address different categories of quantum computing challenges.

Quantum Fourier Transform-based algorithms form one of the most important families. The Deutsch-Jozsa algorithm addresses problems requiring exponential classical queries by examining algorithmic balance and robustness in a single quantum evaluation. The Bernstein-Vazirani algorithm provides efficient solutions to black-box problems. Simon’s algorithm demonstrates exponential quantum speedup for specific structured problems. Most famously, Shor’s algorithm solves integer factorization and discrete logarithm problems efficiently, with profound implications for cryptographic security—it could theoretically crack RSA encryption, which underpins much of modern internet security.

Amplitude amplification-based algorithms form another crucial family. Grover’s algorithm, proposed in 1996, addresses the fundamental problem of searching unstructured databases for marked entries. While it provides a quadratic rather than exponential speedup, it may significantly accelerate searches across huge datasets compared to any classical approach. Quantum counting extends this framework to more generalized search problems.

Perhaps the most practically relevant for the current NISQ era are hybrid quantum-classical algorithms. The Variational Quantum Eigensolver (VQE) combines the advantages of both quantum and classical computing, performing exceptionally well on current NISQ devices for quantum mechanical problems and quantum AI tasks. The Quantum Approximate Optimization Algorithm (QAOA) applies a similar hybrid approach to graph theory and combinatorial optimization problems. These algorithms are particularly important because they can tolerate the noise levels present in today’s quantum hardware.

The survey also examines quantum AI algorithms, including quantum neural networks, quantum support vector machines, and quantum principal component analysis. However, it notes an important caveat: “it is still not completely known if quantum neural networks will provide better computing efficiency than traditional machine learning techniques.” This uncertainty represents one of the most significant open questions in the field and a key quantum computing challenge for AI researchers. For a deeper dive into how AI intersects with emerging technologies, check out our AI and machine learning research collection.

Quantum Computing Challenges in the NISQ Era

The current period in quantum computing is defined by what researchers call the NISQ (Noisy Intermediate-Scale Quantum) era. NISQ devices attempt to deal with the imperfections and losses driven by decoherence while still delivering useful computational results. Understanding the NISQ era is essential for grasping both the promise and the quantum computing challenges that define the field today.

Current NISQ devices operate with relatively sparsely connected qubits, making it difficult to map deep quantum circuits that require multiple two-qubit gates with strong couplings. Reducing decoherence probability and creating effective error correction procedures are among the most important current research goals. The survey emphasizes that these devices, while imperfect, are already capable of demonstrating quantum computational advantages in carefully selected problem domains.

The most celebrated NISQ-era milestone came on October 23, 2019, when Google Quantum AI and NASA announced a demonstration of quantum computation that would take an impractically long time on any traditional computer. Using their 53-qubit Sycamore processor, Google claimed to have performed a specific calculation in 200 seconds that would take the world’s most powerful classical supercomputer approximately 10,000 years. However, the survey notes an important qualifier: IBM scientists subsequently demonstrated that the identical computation could be executed “far more efficiently on a conventional supercomputer” with different classical algorithms and sufficient storage.

This controversy highlights a crucial distinction the survey draws between quantum supremacy and quantum advantage. Quantum supremacy is a theoretical concept implying the ability to solve a challenging problem on any conventional processor—essentially a benchmark achievement. Quantum advantage, by contrast, is described as “a more realistic concept” dealing with solving practical, real-world issues that cannot be effectively addressed on traditional computers. The survey notes that “there is currently a worldwide race to be the first to implement quantum computing to tackle a practical problem that a conventional computer cannot solve in a reasonable time.”

The gap between quantum supremacy and quantum advantage represents one of the most important quantum computing challenges. While supremacy has been demonstrated with artificial benchmarks, finding real-world problems where quantum computers deliver genuine practical benefits remains “unsolved mainly due to the decoherence of quantum bits.” This gap drives much of the current research in error correction, algorithm design, and hardware improvement.

Quantum Computing Challenges in Hardware and Scalability

The hardware landscape for quantum computing is characterized by intense competition among multiple technological approaches, each presenting unique quantum computing challenges and advantages. The survey identifies two primary physical build methods—analog and digital—with various material systems being researched for quantum bits and gates.

Superconducting quantum computers represent the most commercially advanced approach. Companies including IBM, Google, and Rigetti build quantum machines using superconducting quantum circuits, where qubits are housed in dilution refrigerators and maintained at temperatures near absolute zero (approximately 15 millikelvin). IBM’s quantum journey illustrates the rapid pace of advancement: in 2016, they unveiled a 5-qubit system called IBM Quantum Experience, making it publicly available as a cloud processor. By 2017, they had added quantum assembly language support, a user-friendly interface, and simulation capabilities, releasing the Qiskit framework for enhanced quantum programming. IBM subsequently created a 16-qubit system and has since provided cloud access to machines with up to 65 qubits, with a 433-qubit quantum computer recently revealed.

Despite this progress, the survey notes that the “current generation of quantum computers is cumbersome and underpowered,” suffering from poor qubit fidelity, especially in two-qubit operations. Error rates below 1% still lead to “deleterious cumulative error rates” when executing complex real-world circuits. The error correction overhead is staggering: a large number of physical qubits are needed to execute a single quantum algorithm successfully, creating a massive control burden requiring tight, constant communication between classical control systems and the quantum device.

Beyond superconducting systems, the broader hardware ecosystem includes trapped ion computers (pursued by IonQ and Honeywell), photonic quantum computers (developed by Xanadu), and quantum annealers (commercialized by D-Wave). Each platform offers distinct tradeoffs. Rigetti Computing offers their Forest framework as a cloud-based quantum computing utility with processors exceeding 36 qubits. In Europe, QuTech provides the Quantum Inspire platform for cloud-based quantum algorithm execution.

The industry landscape includes established corporations such as IBM, Google, Intel, Microsoft, and Honeywell, alongside growing SMEs like D-Wave and startups including Rigetti, Xanadu, Infleqtion, Origin Quantum, and IonQ. Chinese companies ZTE and QUDOOR are also active players. The survey notes that building practical quantum infrastructure requires far more than just qubits—it demands complex classical management and wiring, sophisticated cooling systems, user interfaces, networks, data storage, and electromagnetic shielding, with more than 100 laboratories globally collaborating on these challenges.

Quantum Cryptography and Security Challenges

Quantum computing challenges extend far beyond computation itself—they pose fundamental threats to the security infrastructure that underpins global communications. The survey provides a detailed analysis of how quantum computers could “effortlessly break the security of traditional cryptosystems” that rely on factorization and discrete logarithm problems. An adversary with a sufficiently powerful quantum computer could break RSA security in polynomial time using Shor’s algorithm. While this threat is “not yet practical,” it poses potential dangers to the integrity of communication networks worldwide.

The survey examines two complementary approaches to quantum-safe security: Quantum Key Distribution (QKD) and Post-Quantum Cryptography (PQC).

QKD represents a fundamentally different approach to secure communication. Unlike classical cryptography, which relies on computationally hard mathematical problems, QKD’s security is guaranteed by the fundamental laws of quantum physics. The no-cloning theorem—a central tenet of QKD—states that “a flawless replica of arbitrary quantum states cannot be created without corrupting the probed quantum states.” This means any eavesdropping effort introduces detectable noise, providing an inherent intrusion detection mechanism.

The survey identifies two primary QKD implementations. Discrete Variable QKD (DV-QKD) encodes information in qubits and uses single photon detectors, with foundational protocols including BB84 (1984), B92 (1992), and BM92 (1992). Continuous Variable QKD (CV-QKD) encodes information in the phase and amplitude of bright coherent states, using homodyne detection in a setup similar to conventional optical communications. Both implementations include a classical communication phase where parties apply error correction and privacy amplification protocols to reduce any eavesdropper’s knowledge to a negligible amount.

Advanced QKD approaches include Device-Independent QKD, which uses Bell inequality violations to verify entanglement and establish “unconditionally secure secret keys” without needing to specify the physical implementation of equipment. However, this approach faces practical limitations as “current technologies cannot still provide in full” the required loophole-free Bell inequality violations and near-perfect quantum detection. Measurement Device-Independent QKD and Twin-field QKD represent additional advances addressing specific limitations in measurement device trustworthiness and distance constraints.

Post-Quantum Cryptography takes a different approach, developing classical algorithms based on mathematical problems believed to be hard even for quantum computers. The survey identifies five families of PQC protocols: code-based, hash-based, lattice-based, multivariate, and supersingular curve-elliptic isogeny schemes. NIST’s post-quantum cryptography standardization process is currently identifying specific algorithms and protocols to be considered secure under quantum threat.

One particularly concerning threat is the “harvest now, decrypt later” attack strategy, where adversaries store encrypted data today with the intention of decrypting it once quantum computers become powerful enough. The survey warns that “in the case of extremely sensitive data, this may represent a threat to security that cannot be neglected, where data needs to remain confidentially protected for very long periods of time.” QKD is identified as the “ultimate counter-measure” against even this sophisticated attack approach.

Quantum Software Frameworks and Development Tools

Among the most practical quantum computing challenges facing developers today is the immaturity of quantum software tools. The survey characterizes quantum software as “an emerging yet relatively less developed field compared to quantum modelling and quantum technology.” Current quantum programming applications are described as “still rather low-level, like assembly-level languages,” lacking the sophisticated, user-friendly tools equivalent to classical programming languages like C++ and Java.

Despite these limitations, several major software frameworks have emerged. Qiskit (Quantum Information Science Kit), developed by IBM, is an open-source framework with a Python API that has become one of the most widely used quantum development platforms. Cirq, developed by Google, offers another open-source Python-based option. PyQuil, from Rigetti Computing, provides cloud-based quantum programming capabilities. PennyLane, developed by Xanadu, specializes in hybrid quantum-classical computations. For quantum annealing, D-Wave offers both Ocean Software and Leap, while Fujitsu’s Digital Annealer provides a quantum-inspired approach running on traditional computing platforms.

A common pattern across these frameworks is that developers release them with open-source licenses and Python APIs, making them relatively straightforward to learn for developers already familiar with Python. However, circuits specified in high-level languages need to be “translated” to fit the actual quantum hardware topology—a compilation step that introduces its own set of quantum computing challenges related to qubit connectivity, gate decomposition, and circuit optimization.

The survey identifies several key research areas for quantum software development: frameworks, semantics, and compilation of programming languages; workflows including controlled and adjoint operations, clean and borrowed qubits; and quantum simulators. Error-correcting firmware sits at the very bottom of the quantum computing stack, with the goal of “effectively integrating quantum algorithms with defective equipment.” This firmware layer aids in lowering error rates due to flawed hardware and reduces the complexity and resource consumption of quantum error correction. For an exploration of how software frameworks are evolving across technology sectors, visit our technology innovation report collection.

The software management requirements for quantum systems are demanding: outstanding performance, sophisticated quantum management techniques, top-quality effects at the system level, regulation for both global and local optimal outcomes through simulation, and adequate physical scheduling. These requirements reflect the unique challenges of managing quantum hardware that operates under extreme physical conditions and with inherent noise characteristics fundamentally different from classical computing systems.

Future Vision: Overcoming Quantum Computing Challenges

The survey paints a picture of a field at an inflection point, where decades of foundational research are beginning to yield practical results, yet enormous quantum computing challenges remain before the technology reaches its full potential. The applications span an extraordinary range of domains, from logistics and banking to drug design and sustainable energy, from quantum chemical engineering to climate prediction and medical diagnostics.

In the financial sector, quantum computing promises rapid risk estimation, improved portfolio optimization for volatile markets, and enhanced trading strategies. Healthcare applications include rapid radiation treatment planning, more accurate diagnostic tools, and accelerated drug discovery through quantum simulation of molecular interactions. The automotive industry sees potential in optimization of manufacturing processes and materials science, while telecommunications could benefit from quantum-secured communication networks and improved network optimization.

The path forward requires simultaneous advances on multiple fronts. Hardware teams must continue improving qubit coherence times, reducing error rates, and scaling systems beyond the current NISQ limitations. The goal of fault-tolerant quantum computing—where quantum error correction enables arbitrarily long computations—requires not just better individual qubits but entirely new architectures for managing the enormous overhead of error correction codes. Algorithm designers must develop new quantum algorithms that provide genuine advantage for practical problems, moving beyond theoretical speedups to real-world performance gains.

Software development must mature rapidly, providing developers with higher-level abstractions and tools that make quantum programming accessible to domain experts who are not quantum physicists. The survey’s comparison of current quantum software to “assembly-level languages” suggests a long road ahead, but also enormous opportunity for companies and researchers who can bridge this gap. On the cryptography front, the parallel tracks of QKD deployment and PQC standardization must both accelerate to protect critical infrastructure before large-scale quantum computers arrive.

Perhaps the most important insight from the survey is that quantum computing is not a single technology but an ecosystem. Success requires coordinated progress across hardware, software, algorithms, error correction, and applications—with more than 100 laboratories worldwide collaborating on these interconnected quantum computing challenges. The vision is clear: a future where quantum and classical computers work together, each handling the problems best suited to their respective strengths, unlocking capabilities that neither could achieve alone.