I’m Emily, and my article of the week was inspired by my stumbling upon the FALCON quantum encryption algorithm. As a cybersecurity student, it is important to research information that is relevant to my field and to the future of computing. Doing the research for this article has helped me understand the implications of quantum computing not just in cybersecurity, but in many other aspects of computer science. I hope my work is able to help you understand it on a deeper level as well, no matter your major or profession.

Introduction: Why Should Computer Scientists Care About Quantum?

In recent years, quantum computing has transitioned from a niche area of theoretical physics to a rapidly emerging field with direct implications for computer science, cybersecurity, medical sciences, and beyond. While the technology is still in its developmental stages, its potential to disrupt classical paradigms of computation has captured global attention. For computer science students, understanding quantum computing is not only intellectually enriching but also increasingly relevant. As quantum hardware becomes more accessible and quantum programming languages like Qiskit and Cirq gain traction, those equipped with foundational knowledge will be best positioned to contribute to (or at least adapt to) this technological shift. This article offers a beginner-friendly overview of quantum computing, from its fundamental principles and applications to its current limitations and future prospects.

A Brief Timeline of Quantum Computing

To understand quantum computing’s “sudden” popularity, it's helpful to consider the field’s evolution. The conceptual groundwork was laid in the early 1980s, when physicist Richard Feynman proposed that quantum systems could not be efficiently simulated by classical computers, and therefore, a new computational model based on quantum mechanics was needed. This insight spurred early theoretical work, culminating in 1994 with Peter Shor's quantum algorithm for integer factorization, which demonstrated that a quantum computer could theoretically break widely-used encryption schemes like the RSA encryption algorithm exponentially faster than classical methods. In the decades that followed, academic and industrial labs began experimenting with physical implementations of qubits. Major milestones include IBM’s and Google’s advances in superconducting qubit systems, with Google’s 2019 “quantum supremacy” claim marking the first time a quantum computer outperformed a classical one on a contrived but complex task (random circuit sampling). Today, the field is progressing from foundational physics to practical engineering. Students can access real quantum hardware through cloud platforms, which is a remarkable shift from theoretical inaccessibility to hands-on learning.

Classical vs. Quantum: Understanding the Shift

Classical computers, the foundation of modern computing, operate using bits, which are binary units that exist in one of two states: 0 or 1. These bits are manipulated through logical gates built on transistor-based circuits, enabling deterministic computation. Quantum computers, by contrast, rely on qubits, which can exist in a linear combination of 0 and 1 simultaneously due to the principle of superposition. This property allows quantum systems to explore multiple computational paths in parallel, offering potential exponential speed-ups for certain problem classes. Additionally, qubits can become entangled, meaning their states are interdependent even when physically separated. These features allow quantum computers to solve particular problems, such as factoring large integers or simulating molecular structures in ways that classical machines fundamentally cannot. It is important to distinguish quantum computers from classical supercomputers: The latter are highly optimized versions of classical architectures, while the former represent an entirely different computational paradigm with unique capabilities and constraints.

The Principles Behind the Magic

Quantum computing is grounded in several key phenomena from quantum mechanics:

Superposition allows qubits to represent a continuum of states between 0 and 1, dramatically increasing the computational state space available to quantum algorithms.

Entanglement creates non-local correlations between qubits, such that operations on one qubit can instantaneously influence its entangled partner. This is a critical enabler of quantum speed-up and parallelism.
Interference, a concept borrowed from wave physics, is used to constructively amplify correct computational paths while canceling out incorrect ones.
Decoherence refers to the loss of quantum coherence due to environmental interactions, which collapses superpositions and destroys quantum information. This necessitates error correction strategies and physical isolation. While these concepts are rooted in quantum physics, their computational implications are increasingly accessible thanks to simplified models, educational tools, and visualizations tailored for computer scientists.

Meet the Qubit

A qubit (quantum bit) is the fundamental unit of quantum information. Unlike classical bits, which are confined to binary states, a qubit exists on the surface of the Bloch sphere, representing any linear combination of |0⟩ and |1⟩. Physically, qubits can be realized through various mechanisms, including superconducting circuits (as used by IBM and Google), trapped ions (IonQ), topological states, and photonic systems. Each implementation offers trade-offs in gate fidelity, decoherence time, scalability, and error rates. In computation, qubits are manipulated using Quantum gates, which are reversible unitary operations that modify the state of one or more qubits. These gates are combined into quantum circuits representing quantum algorithms. The choice and quality of qubits directly affect a quantum computer's capability to perform meaningful computations. As research progresses, hybrid systems and error-tolerant architectures are being explored to overcome current limitations and scale up to useful quantum machines.

Image of Bloch Sphere from https://en.wikipedia.org/wiki/Bloch_sphere

What Is Quantum Computing Useful For?

Quantum computing is not a general-purpose replacement for classical computing– it excels at specific types of problems that are intractable for classical machines.

In cryptography, quantum algorithms like Shor’s pose a threat to public-key systems such as RSA, motivating the development of post-quantum cryptographic standards.
In pharmaceuticals and materials science, quantum computers can simulate molecular systems at the quantum level, aiding in the discovery of new drugs and materials.
In machine learning, quantum algorithms may accelerate training, enhance optimization, and enable more expressive models through techniques like quantum kernel methods.
Similarly, complex optimization problems found in logistics, finance, and operations research may benefit from quantum approaches that explore large solution spaces more efficiently.

These applications are not yet fully realized but represent areas where quantum advantage could lead to transformative outcomes.

Quantum Utility vs. Quantum Advantage

Two important concepts in the trajectory of quantum computing are quantum utility and quantum advantage. Quantum utility refers to a quantum system’s ability to solve real-world problems in ways that are meaningful or beneficial, even if not necessarily faster than classical alternatives. This could include providing approximate solutions to hard optimization problems or simulating quantum chemistry more accurately. Quantum advantage, on the other hand, is achieved when a quantum computer outperforms the best-known classical solution for a given task– not just in theory, but in practice. While a few demonstrations of quantum advantage (such as Google's 2019 experiment) have been claimed, they often involve contrived problems rather than useful applications. The field is currently transitioning from proof-of-concept toward practical utility, and understanding this distinction is key to evaluating progress without falling prey to hype.

Not So Fast– What’s Holding Quantum Back?

Despite remarkable progress, several challenges prevent quantum computing from achieving mainstream adoption:

Error correction: due to decoherence and operational noise, qubits are highly error-prone, and maintaining fidelity over long computations requires thousands of physical qubits to encode a single reliable logical qubit (an abstract, error-resistant unit of information used in computation).
Scalability: building and controlling large numbers of qubits, each requiring precise isolation and calibration, presents formidable engineering obstacles. Many qubit systems must be kept near absolute zero using specialized cryogenic equipment, further increasing complexity and cost.
Software bottleneck: most existing algorithms do not yet offer a clear quantum advantage, and quantum programming paradigms remain underdeveloped compared to classical tools. As a result, quantum computing is not yet ready to displace conventional systems but complements them in specific domains.

Conclusion: Why Quantum Matters for the Future of Computing

Quantum computing represents a fundamental shift in how we conceptualize computation. While the technology is still maturing, its rapid development and strategic importance are undeniable. One of the most impactful recent developments is the U.S. NIST’s selection of post-quantum cryptographic algorithms, including FALCON, the algorithm that inspired this article. Meanwhile, companies like IBM, Google, and QuEra are scaling up quantum hardware and offering cloud-based access, with IBM planning a 100,000-qubit system by 2033. Globally, governments are investing in national quantum initiatives, emphasizing research, infrastructure, and workforce development.

These developments show that quantum computing is no longer theoretical, it is reshaping cybersecurity policy, tech priorities, and the future of computing. For computer science students, engaging with quantum concepts now provides a meaningful edge in many STEM related fields. Tools like Qiskit, IBM Quantum Experience, and QuEra’s open-access platforms make it easier than ever to start exploring this rapidly evolving frontier.

Additional References

Cirq documentation: https://quantumai.google/cirq
Qiskit documentation: https://www.ibm.com/quantum/qiskit
Documentation for FALCON, a quantum cryptographic algorithm submitted to NIST to be a standard for post quantum encryption: https://falcon-sign.info/
QuEra website: https://www.quera.com/
IBM Quantum website: https://quantum.ibm.com/
The Feynman Lectures on Physics: https://www.feynmanlectures.caltech.edu/III_01.html
Resources from IonQ: https://ionq.com/resources

Quantum Leaps and Bounds: A Beginner’s Guide to Understanding the Implications of Quantum Computing