Why Should We Trouble with Quantum Computing in ML?

When black cats prowl and pumpkins gleam, may luck be yours on Halloween. (Unknown)

, conferences, workshops, articles, and books on quantum computing have multiplied, opening recent ways to process information and to reconsider the limits of classical systems. The interplay between classical and quantum research has also driven hybrid algorithms that mix familiar techniques with quantum resources. This text introduces the essentials of quantum computing and tries to elaborate on further applications to data science.

With the 2025 Nobel Prize in Physics [1] recognizing advances in quantum tunneling, it is obvious that quantum technology can be much more present in the approaching years. This key idea, developed for the reason that Eighties, is that quantum tunneling enables devices that turn superposition, entanglement, and interference (consult with Figure 1 for definitions) into tools we will engineer, meaning we will run real algorithms on real chips, not only in simulations, and explore recent ways to learn from high-dimensional data more efficiently.

Before we dive into the fundamentals, it’s price asking why we’d like quantum in our workflows. The query is: 

what are the boundaries in today’s methods that force us to reframe our approach and consider alternatives beyond the tools we already use?

Limitations of Moore’s law:

Moore’s law, proposed in 1965, predicted that the variety of transistors on a chip, and thus computing power, would roughly double every two years. This expectation drove a long time of progress through regular transistor miniaturization: chips fit about twice as many transistors every two years, making computing cheaper and faster [2].

Nonetheless, as engineers push transistor sizes to the atomic scale, they encounter daunting physical limitations: fitting more, smaller devices into the identical area rapidly increases each heat generation and power density, making cooling and stability much harder to administer. At tiny scales, electrons leak or escape from their intended paths, causing power loss and making the chip behave unpredictably, which may result in errors or reduced performance. Furthermore, wires, memory, and input/output systems don’t scale as efficiently as transistors, leading to serious bottlenecks for overall system performance [2].

All these barriers make it clear that the exponential growth predicted by Moore’s law cannot proceed indefinitely; only counting on shrinkage alone isn’t any longer viable. As a substitute, progress now relies on higher algorithms, specialized hardware, and, where suitable, optimal algorithms that (when applicable) leverage quantum approaches for chosen, high-impact subproblems.

As data volumes proceed to grow and computational demands escalate, deep learning and other modern AI methods are reaching practical limits in time, energy, and memory efficiency. Quantum computing offers a distinct route, one which processes information through superposition, entanglement, and interference, allowing certain computations to scale more efficiently. The goal of quantum machine learning (QML) is to make use of qubits as a substitute of bits to represent and transform data, potentially handling high-dimensional or uncertain problems more effectively than classical systems. Although today’s hardware remains to be developing, the conceptual foundations of QML already point toward a future where each quantum and classical resources work together to beat computational bottlenecks.

Security Paradigm

Traditional encryption methods depend on complex mathematical problems that classical computers find hard to unravel. Nonetheless, quantum computers threaten to interrupt lots of these systems rapidly by exploiting quantum algorithms like Shor’s algorithm (one in every of the examples of quantum computational advantage) [3]. Many quantum-based security innovations are increasingly moving from theory into practical use in industries requiring the best data protection standards.

A concrete example of this risk is generally known as “harvest now, decrypt later”: where attackers capture and store encrypted data today, even when they can’t decrypt it yet. Once large-scale quantum computers turn out to be available, they may use quantum algorithms to retroactively decrypt this information, exposing sensitive data corresponding to health records, financial transactions, or classified communications [4].

To approach this challenge Google Chrome Browser Includes
Quantum-Resistance. Since version 116, Chrome has implemented a hybrid key agreement algorithm (X25519Kyber768) that mixes traditional elliptic-curve cryptography with Kyber, one in every of the algorithms standardized by NIST for quantum-resistant encryption. This approach protects data against each classical and future quantum attacks.

Mathematical complexity

Using Quantum principles might help to explore vast solution spaces more efficiently than traditional methods. This makes quantum approaches particularly promising for optimization, machine learning, and simulation problems with high computational complexity (Big-O or how effort scales with problem size). For instance, factoring large integers is computationally hard mainly as a consequence of mathematical complexity, not memory or brute force limits. Which means that for very large numbers, like those utilized in cryptographic systems, factorization of huge numbers is practically inconceivable on classical computers.

Understanding the basics

To know more about these topics, it’s crucial to understand the fundamental rules of quantum mechanics and the way they differ from the classical view that we use today.

In classical computing, data is represented as bits, which may have a worth of 0 or 1. These bits are combined and manipulated using logical operations or logic gates (AND, OR, NOT, XOR, XNOR) to perform calculations and solve problems. Nonetheless, the quantity of knowledge a classical computer can store and process is restricted by the variety of bits it has, which may represent only a finite variety of possible combos of 0s and 1s. Due to this fact, certain calculations like factoring large numbers are very difficult for conventional computers to perform.

Then again, in quantum computing, data is represented as quantum bits, or qubits, which may have a worth of 0 and 1 concurrently as a consequence of the principles of superposition, interference, and entanglement. These principles allow quantum systems to process information in parallel and solve some problems much faster. That is generally known as the ‘quantum cat state’ or Schrödinger’s cat state.

This concept could be explained with Schrödinger’s cat experiment (figure 1), during which a hypothetically radioactive atom is utilized in a closed mechanism that, if triggered, could end the lifetime of a cat trapped inside 🙀🙀🙀. The thought is that the atom is in a superposition of states that either prompts or doesn’t activate the mechanism, and at the identical time is entangled with the state of the cat, so until the atom’s state materializes, the cat’s state stays in a superposition of being each alive 😺 and dead ☠️ concurrently. The cat’s state in Schrödinger’s experiment is just not an actual state of matter but moderately a theoretical concept used to elucidate the strange behavior of quantum systems.

An identical idea could be illustrated with a quantum coin (a greater example that protects the cats 🐱). A standard coin all the time has one face up, either heads or tails, but a quantum coin can exist in a superposition of each possibilities directly until it’s observed. When someone checks, the superposition collapses right into a definite final result. The coin also can turn out to be entangled with the device or system that measures it, meaning that knowing one immediately determines the opposite (no matter initial classical conditions). Interference further modifies the possibilities: sometimes the waves add together, making one final result more likely, while in other cases they cancel out, making it less likely. Even the actions of starting, flipping, and landing can involve quantum phases and create superpositions or entanglement.

Constructing on these ideas, an n-qubit register lives in an area with possible states, meaning it will possibly represent complex patterns of quantum amplitudes. Nonetheless, this doesn’t mean that qubits store classical bits or that each one answers could be read directly. When the system is measured, the state collapses, and only limited classical information is obtained, roughly bits per run. The facility of quantum computation lies in designing algorithms that prepare and manipulate superpositions and phases in order that interference makes the proper outcomes more likely and the inaccurate ones less likely. Superposition and entanglement are the essential resources, but true quantum advantage relies on how these effects are used inside a particular algorithm or problem.

Different approaches

There are several sorts of approaches to quantum computing, which differ within the qubits they use, how they control them, the conditions they need, and the issues they’re good at. Figure 2 summarizes the foremost options, and as the sphere matures, more advanced techniques proceed to emerge.

In gate-model quantum computers and quantum annealers, simulation on classical computers becomes impractical as quantum systems grow large (corresponding to those with many qubits or complex problems like factorization of huge numbers) as a consequence of the exponential resource demands. Real quantum hardware is required to watch true quantum speedup at scale. Nonetheless, classical computers still play a vital role today by allowing researchers and practitioners to simulate small quantum circuits and experiment with quantum-inspired algorithms that mimic quantum behavior without requiring quantum hardware.

Once you do need real quantum devices, access is generally via cloud platforms (IBM Quantum, Rigetti, Azure Quantum, D-Wave). Libraries like Qiskit or PennyLane allow you to prototype on classical simulators and, with credentials, submit jobs to hardware. Simulation is crucial for development but doesn’t perfectly capture physical limits (noise, connectivity, queueing, device size).

Gate models:

On gate-model hardware, step one is frequently establishing a circuit that encodes the quantum state it is advisable solve the issue. So, the data we all know is encoded into quantum states using quantum bits or qubits, that are controlled by quantum gates. These gates are just like the logic operations in classical computing, but they work on qubits and reap the benefits of quantum properties like superposition, entanglement, and interference. There are a lot of ways to encode a quantum state right into a circuit, and depending on the way you do it, error rates could be very different. That’s why error correction techniques are used to repair mistakes and make calculations more accurate. In any case the operations and calculations are done, the outcomes have to be decoded back so we will understand them in the conventional classical world.

Within the case of QML or quantum ML, kernels and variational algorithms are used to encode and construct models. These techniques have approaches somewhat different from those utilized in classical machine learning.

• Variational algorithms (VQAs): define a parameterized circuit and use classical optimization to tune parameters against a loss (e.g., for classification). Examples include Quantum Neural Networks (QNNs), Variational Quantum Eigensolver (VQE), and Quantum Approximate Optimization Algorithm (QAOA).
• Quantum-kernel methods: construct quantum feature maps and measure similarities to feed classical classifiers or clusterers. Examples include Quantum SVM (QSVM), Quantum Kernel Estimation (QKE), and Quantum k-means.

QML algorithms, corresponding to kernel-based methods and variational algorithms, have shown promising leads to areas like optimization and image recognition and have the potential to revolutionize various industries, from healthcare to finance and cybersecurity. Nonetheless, many challenges remain, corresponding to the necessity for robust error correction techniques, the high cost of quantum hardware, and the shortage of quantum experts.

Quantum annealing

Many real-world problems are combinatorial, with possibilities growing factorially (e.g., 10!, 20!, etc.), making exhaustive search impractical. These problems often map naturally to graphs and could be formulated as Quadratic Unconstrained Binary Optimization (QUBO) or Ising models. Quantum annealers load these problem formulations and seek for low-energy (optimal or near-optimal) states, providing another heuristic for optimization tasks with graph structures. When put next fairly with strong classical baselines under the identical time constraints, quantum annealing can show competitive performance.

In QML, quantum annealing could be applied to optimize parameters in machine learning models, discover patterns, or perform clustering by finding minimum energy configurations representing solutions. Although quantum annealers are hardware-specific and specialized, their practical application to machine learning and optimization makes them a very important complementary approach to gate-model QML.

Quantum annealers often function heuristic solvers and are compared against classical strong baselines under similar time constraints. Access is usually via cloud services (like D-Wave), and their noise and hardware limitations distinguish them from gate-model quantum computers.

Quantum-inspired

These are classical algorithms that mimic ideas from quantum computing (e.g., annealing-style search, tensor methods). They run on CPUs/GPUs (no quantum hardware required ) and make strong baselines. You should utilize standard Python stacks or specialized packages to try them at scale.

Quantum-inspired algorithms provide a practical bridge by leveraging quantum principles inside classical computing, offering potential speedups for certain problem classes without having expensive quantum hardware. Nonetheless, they don’t provide the complete benefits of true quantum computation, and their performance gains depend heavily on the issue and implementation details.

Example:

Today’s quantum advantage remains to be embryonic and highly problem-dependent. The largest gains are expected on high-complexity problems with structure that quantum algorithms can exploit. The toy example presented is that this section is solely illustrative and highlights differences between approaches, but real advantage is more prone to appear on problems which are currently hard or intractable for classical computers.

On this example, we use a tabular and simulated dataset during which most points are normal and a small fraction are anomalies (Figure 3). On this demo, corresponds to the dense cluster across the origin, while anomalies form a couple of small clusters far-off.

The diagram of figure 4 illustrates a unified workflow for anomaly detection using three distinct approaches on the identical tabular dataset: (1) classical machine learning (One-Class SVM)[7], (2) gate-based quantum machine learning (quantum kernel methods)[8], and (3) quantum annealing-inspired optimization. First, the dataset is cleaned, scaled, and split into training, validation, and test sets. For the classical path, polynomial feature engineering is applied before training a One-Class SVM and evaluating predictions. The gate-based quantum ML option encodes features using a quantum map and estimates quantum kernels for training and inference, followed by decoding and evaluation. The annealing route formulates the duty as a QUBO, solves it with simulated annealing, decodes results, and evaluates performance. Each approach produces its own anomaly prediction outputs and evaluation metrics, providing complementary perspectives on the information and demonstrating how each classical and quantum-inspired tools could be integrated right into a single evaluation pipeline running on a classical computer.

On this tiny, imbalanced test set (22 normal, 4 anomalous points), the three approaches behaved in another way. The quantum-kernel OCSVM achieved the perfect balance: higher overall accuracy (~0.77) by catching most anomalies (recall 0.75) while keeping false alarms lower than the others. The classical OCSVM (RBF) and the annealer-style QUBO each reached recall 1.0 (they found all 4 anomalies) but over-flagged normals, so their accuracies fell (≈0.58 and 0.65).

The target here is demonstration, not performance: this instance shows tips on how to use the approaches, and the outcomes will not be the main target. It also illustrates that the feature map or representation can matter greater than the classifier.

Any claim of quantum advantage ultimately relies on scaling: problem size and structure, circuit depth and width, entanglement within the feature map, and the flexibility to run on real quantum hardware to use interference moderately than merely simulate it. We will not be claiming quantum advantage here; this is an easy problem that classical computers can solve, even when using quantum-inspired ideas.

When to Go Quantum

It is smart to start on simulators and only move to real quantum hardware if there are clear signals of profit. Simulators are fast, low-cost, and reproducible: you possibly can prototype quantum-style methods (e.g., quantum kernels, QUBOs) alongside strong classical baselines under the same time/cost budget. This allows you to tune feature maps, hyperparameters, and problem encodings, and see whether any approach shows higher accuracy, time-to-good-solution, robustness, or scaling trends.

You then use hardware when it’s justified: for instance, when the simulator suggests promising scaling, when the issue structure matches the device (e.g., good QUBO embeddings or shallow gate circuits), or when stakeholders need hardware evidence. On hardware you measure quality–time–cost with noise and connectivity constraints, apply error-mitigation, and compare fairly against tuned classical methods. In brief: simulate first, then go quantum to validate real-world performance; adopt quantum provided that the hardware results and curves truly warrant it.

As noted earlier, today’s quantum advantage remains to be embryonic and highly problem-dependent. The true challenge and opportunity is to show promising simulations into hardware-verified gains on problems that remain difficult for classical computing, showing clear improvements in quality, time, and price as problem size grows.

Quantum machine learning has the potential to transcend classical methods in model compression and scalability, especially for data-rich fields like cybersecurity. The challenge is handling enormous datasets, with hundreds of thousands of normal interactions and only a few attacks. Quantum models can compress complex patterns into compact quantum representations using superposition and entanglement, which allows for more efficient anomaly detection even in imbalanced data. Hybrid quantum-classical and federated quantum learning methods aim to enhance scalability and privacy, making real-time intrusion detection more feasible. Despite current hardware limitations, research indicates quantum compression could enable future models to administer larger, complex cybersecurity data streams more effectively, paving the way in which for powerful practical defenses.

References 

[1] Nobel Prize in Physics 2025. . Nobel Prize Outreach (2025). “Summary”. Accessed 19 Oct 2025. https://www.nobelprize.org/prizes/physics/2025/summary/

[2] DataCamp. (n.d.). Moore’s Law: What Is It, and Is It Dead? Retrieved October 2, 2025, from https://www.datacamp.com/tutorial/moores-law

[3] Classiq. (2022, July 19). . Classiq Insights. https://www.classiq.io/insights/shors-algorithm-explained

[4] Gartner. (2024, March 14). Retrieved October 10, 2025, from https://www.gartner.com/en/articles/post-quantum-cryptography

[5] The Quantum Insider. (2023, August 14). Retrieved October 10, 2025, from https://thequantuminsider.com/2023/08/14/google-advances-quantum-resistant-cryptography-efforts-in-chrome-browser/

[6] “Schrodinger’s Cat Coin (Antique Silver)” by BeakerHalfFull (accessed Oct 16, 2025). Taken from: Etsy: https://www.etsy.com/listing/1204776736/schrodingers-cat-coin-antique-silver

[7] Scikit-learn developers. “One-class SVM with non-linear kernel (RBF).” scikit-learn documentation, https://scikit-learn.org/stable/auto_examples/svm/plot_oneclass.html. Accessed 21 October 2025.

[8] Schuld, Maria. “Kernel-based training of quantum models with scikit-learn.” PennyLane Demos, https://pennylane.ai/qml/demos/tutorial_kernel_based_training. Published February 2, 2021. Last updated September 22, 2025. Accessed 21 October 2025.

[9] Augey, Axel. “Quantum AI: Ending Impotence!” , 12 June 2019, https://www.saagie.com/en/blog/quantum-ai-ending-impotence/.