A Step Ahead: Quantum Computing and Data

Introduction

This article is the first in a series exploring how quantum computing could change the way we process and work with data. Here, we focus on the foundations: What makes a quantum bit (qubit) different from a classical bit, how qubits can hold many possibilities at once, how they can be strongly linked through entanglement, and how quantum circuits process these qubits in a way that resembles data pipelines or machine learning models.

We introduce core quantum computing concepts for data and machine learning practitioners. We explain how qubits differ from classical bits by using superposition to represent weighted combinations of 0 and 1, and how entanglement encodes strong joint correlations that lack classical analogues. Quantum circuits are presented as data pipelines: Quantum gates act like layers that transform embedded input data into output probability distributions, from which predictions or scores are derived.

The article also outlines parameterized quantum circuits (variational circuits) trained in hybrid quantum–classical loops, drawing parallels to neural networks with tunable weights. Throughout, it emphasizes both the potential of quantum computing to represent complex data patterns and the practical limitations of today’s noisy, intermediate-scale hardware. This foundational perspective sets the stage for subsequent articles on concrete quantum-enhanced machine learning workflows.

The goal is not to turn you into a quantum physicist, but to give you enough intuition to see why these ideas might matter for data and AI. In the following articles, we will build on this foundation and look more closely at specific quantum approaches for data processing and how they might extend or enhance classical machine learning workflows. For overviews see [Biamonte][Schuld & Petruccione].

The Quantum State

In today’s computers, information is stored in bits. Each bit can be either 0 or 1. This is simple and powerful for representing and storing a data value.

A quantum bit, or qubit, is different. A qubit can be in a mix of 0 and 1 at the same time. This is called superposition [belNielsen & Chuang].

For people who work with data, you can think of a qubit as being described by two complex numbers (called amplitudes), one attached to 0 and one attached to 1. The squared magnitudes of these amplitudes behave like probabilities measured as 0 or 1. So, in a simplified, data-oriented view, a qubit can encode “it’s mostly like 0, but also somewhat like 1,” similar to a probability distribution over the two values.

In summary:

A classical bit holds one outcome (0 or 1).
A qubit holds a vector of possibilities with associated weights (amplitudes).
When you measure, you only see one outcome, but those weights affect how likely each outcome is.

You never directly “see” a qubit in its mixed state. To get information out, you have to measure it. When you measure a qubit, it stops being “in-between” 0 and 1 and turns into a definite value: either 0 or 1. Which one you get is random, but not arbitrary. It follows the probabilities defined by the qubit’s state before the measurement. As a result, a single run of quantum program doesn’t usually give you a single, perfectly stable answer. Instead, you run the same quantum circuit many times (often thousands of “shots”), collect many samples, and then analyze the empirical distribution of outcomes. This repeated sampling is how you uncover the information encoded in the superposition.

Qubits also have another special property called entanglement. When qubits are entangled, they become strongly linked in a way that doesn’t have a straightforward everyday analogue. You can think of it with the following rough analogy:

Imagine you have two special coins that are “paired” together.

Each coin, on its own, looks completely random: If you flip it, it’s 50% heads and 50% tails.

But these two special coins are prepared so that whenever you flip both, you always get perfectly related results. For example, they might always come out opposite: If one is heads, the other is guaranteed to be tails.

Before you flip them, neither coin has a fixed value; each is “undecided.” But the moment you flip and look at one coin, you immediately know what the other coin will show, even if it’s far away. The outcomes are perfectly correlated.

Entangled qubits behave in a similar way, but with more flexibility and power than simple “same” or “opposite” patterns. Their values are not fixed ahead of time like hidden labels; instead, their shared, joint state is set up so that when you finally measure them, their results are strongly linked.

So, when qubits are entangled:

You can’t fully describe each qubit on its own; you have to treat them together as a single combined system.

Measuring one qubit instantly tells you something about the others, no matter how far apart they are.

These correlations are stronger than anything you can get with ordinary classical data

For data practitioners, you can think of entanglement as a way to encode very strong, structured relationships between features that only show up when you look at them together. Entanglement lets you bind outcomes across qubits so that information about one feature immediately constrains others, enabling the circuit to represent joint patterns that classical factorized models struggle to capture. This ability to tightly connect qubits is one of the key reasons quantum computers can represent and process certain kinds of complex data patterns more efficiently than classical computers in some specifically structured problems (e.g., certain optimization, simulation, or linear-algebra tasks). Paired with superposition which lets the system explore many potential outcomes in parallel (though this “parallelism” only becomes useful when algorithms use interference to amplify the right outcomes), entanglement provides a mechanism to both search a large space of possibilities and encode the complex dependencies among those possibilities. Quantum algorithms are designed to exploit these two properties together for representational and computational advantages in specific problem domains [Montanaro] [Arute].

Quantum Circuits as Data Pipelines

A quantum circuit is how we “process” qubits, in the same way that a data pipeline or a model processes input data.

A quantum circuit is built from a sequence of operations called quantum gates. Each gate is a basic instruction that changes the state of one or more qubits. If you’re familiar with linear algebra, each gate can be described as a unitary matrix (a table of numbers) that acts on a vector (a list of numbers) representing the qubit’s state. If that math language isn’t familiar, you can simply think:

A qubit’s state is some internal configuration that captures “how much 0” and “how much 1” it has using complex-valued weights whose square magnitudes correspond to probabilities (and, for multiple qubits, how they might be entangled).

A gate is a defined rule for transforming that configuration into a new one.¹

For data practitioners, the easiest way to think about quantum circuits is as data pipelines:

The input to the pipeline is the initial state of your qubits (which might encode your data).

The pipeline consists of quantum gates applied in a specific order, like layers in a model or steps in an ETL process.

The output is what you get after you measure the qubits at the end of the circuit, which gives you samples from some probability distribution over possible outcomes.²

Just as machine learning models pass input features through layers of transformations (linear layers, activation functions, etc.), quantum circuits pass qubits through gates. Each gate modifies the quantum representation, and the full circuit defines a workflow that maps input states to output probability distributions.³

Some quantum gates can be parameterized, meaning they depend on adjustable values. A common example is a “rotation” gate: It rotates the state of a qubit by some angle. That angle is a parameter you can tune. These parameters play a similar role to weights in a neural network:

The structure of the circuit (which gates are used and in what order) is like the architecture of a model.

The parameters of the gates are like the trainable weights or biases.

By adjusting these parameters, the circuit can be trained to perform specific tasks such as classification, optimization, or generative modeling. You typically do this with a hybrid process:

Run the quantum circuit many times on a quantum computer to get measurement results. Because each run is probabilistic, you need repeated “shots” to estimate the underlying probabilities or expectation values accurately.
Use a classical (ordinary) computer to analyze those results, compute a loss or objective function, and decide how to update the parameters.
Update the parameters, modify the circuit slightly, and run it again.

This loop represents a quantum circuit for generating samples, paired with a classical computer for optimization and is the basis of Parameterized Quantum Circuits (PQCs) [Benedetti] [Cerezo]. PQCs combine quantum transformations (superposition, entanglement, and quantum gates) with classical optimization (gradient descent or other methods) to create hybrid models that can, in principle, capture rich patterns in data using quantum resources. In practice, today’s hardware is noisy and limited in size (the so called “NISQ Era”) [Preskill], so PQCs are actively researched to find problem settings where they can offer an advantage over classical methods despite these constraints.

In our next segment, we’ll describe how PQCs could extend classical machine learning pipelines into the quantum domain.

Conclusion

Quantum computing introduces two key ideas that are especially relevant for data and machine learning: superposition, which lets qubits represent weighted combinations of many classical states at once, and entanglement, which encodes strong, structured correlations that have no classical counterpart. Quantum circuits use sequences of gates to shape these quantum states, much like data pipelines or neural networks use a series of transformations to shape feature representations.

By embedding classical data into quantum states and training parameterized quantum circuits with classical optimizers, we obtain hybrid models that may express certain probability distributions or decision boundaries more efficiently than purely classical models in specific problem families. However, today’s devices are small and noisy, and any near-term advantage is expected to be problem-dependent and modest.

As the series continues, we will look at concrete ways to build quantum-enhanced data workflows: How to design feature maps, how to integrate PQCs into existing ML stacks, and how to reason about when a quantum approach is likely to be beneficial versus when classical methods remain the better choice.

Footnotes

¹ Because gates are unitary, they preserve the total probability and are reversible until you perform a measurement. Before you can process data, you usually need to encode it into qubits. This is sometimes called a “feature map” or data embedding: you start from a simple reference state (like all qubits in 0) and apply a first stage of gates whose parameters are functions of your classical input features. This prepares an initial quantum state that represents your data [Havlíček] [Schuld & Killoran].

² Often, you summarize these outcomes by estimating expectation values (averages) of certain measurements, which play a role similar to model logits, scores, or predictions.

³ Different circuit designs (depth, connectivity between qubits, types of gates) determine what kinds of probability distributions or decision boundaries the circuit can express, much like model capacity in classical ML [Cerezo].

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The author’s affiliation with The MITRE Corporation is provided for identification purposes only, and is not intended to convey or imply MITRE’s concurrence with, or support for, the positions, opinions, or viewpoints expressed by the author.’

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About the Author

Ali Obaidi is a Principal Data Engineer at MITRE Corporation, serving as a subject matter expert in various data management fields, including data architecture, data strategy and AI governance, data integration and sharing, data security and privacy, and data ethics. He is a member of the Data Management team within MITRE Labs, a researcher, Principal Investigator, and an adjunct professor at George Washington University teaching data management and information systems classes.