What is quantum computational biology?

Thus far, this course has introduced different tools and areas of interest in computational biology. Whether its genome analysis or 3D modeling of biomolecules, computational biology attempts to understand life using computational tools. Quantum computational biology is no different; it is simply a special approach to applying computational tools to biology.

What is this special approach? Simply put, quantum computational biology makes use of a new and rapidly developing type of computer called a quantum computer. Researchers are looking to harness the power of quantum computers to simulate and analyze biological systems at unprecedented scales, potentially transforming our understanding of biology and medicine.

Quantum computers are fundamentally different from the computers we typically use in our daily lives. Our smartphones, laptops, and smartwatches all operate on bits, which represent information using what are essentially off and on switches (represented as 0’s or 1’s). These classical computers perform operations using this binary system, such as adding and multiplying, but also essentially every program that your computer runs. The system can be modeled using the physics you may have learned in your high school class (classical physics, which includes Newton’s laws of motion and classical electrodynamics). Quantum computers, on the other hand, takes advantage of quantum physics to outperform classical computers on certain tasks. Quantum physics can be extremely foreign and confusing for beginners, so a simple explanation of the field is provided below:

Classical computers, like the one pictured above, rely on classical physics and operate using a two-state (binary) system.

Introduction to Quantum Theory

Before explaining quantum physics, it should be made clear that you do not have to understand how quantum computers work to use them. While having a strong knowledge base might enhance your ability to program them, it is not strictly necessary. If you do not wish to learn quantum physics and are solely interested in the applications and practical skills related to computational biology, skip to the next section in this lesson, “Applications and Importance”.

Quantum physics as a fundamental theory

Quantum physics is a fundamental theory in physics—this means that scientists believe it is capable of explaining nearly all of the natural phenomenon we observe. What makes the theory so interesting and difficult to understand is that it introduces concepts that defy our everyday experiences. As an example, quantum physics says it is possible for a particle to be in two places at one—a concept called superposition. You might be thinking that quantum physics cannot be a fundamental theory that describes what we observe in daily life because we never encounter objects that exist in superposition. Indeed, quantum physics typically deals with the behavior of matter and energy at the smallest scales—atoms and subatomic particles—because the strange quantum behavior of particles tend to average out and become negligible when dealing with the vast number of particles in macroscopic objects. Thus, classical physics is typically a more convenient description for everyday experiences; we could in theory model everything using quantum physics, but that would unnecessarily complicate the problem when classical physics serves as a very good approximation.

History and significance

Quantum physics emerged in the early 20th century as scientists grappled with phenomena that classical physics couldn't explain, starting with Max Planck's work on black body radiation in 1900 and Albert Einstein's explanation of the photoelectric effect in 1905. The field developed rapidly through contributions from numerous scientists, including Niels Bohr, Werner Heisenberg, and Erwin Schrödinger, leading to the formulation of quantum physics in the 1920s, which revolutionized our understanding of matter and energy at the atomic and subatomic scales.

Key concepts

There are a few key concepts in quantum physics that are worth learning in order to understand how quantum computers work:

Superposition: Particles can exist in multiple states simultaneously. This allows quantum computers to process a vast number of possibilities at once.
Entanglement: Particles can be interconnected such that the state of one can instantly influence the state of another, no matter the distance. This interconnectedness enhances the computational power of quantum computers.
Interference: Quantum interference involves the combination of wave functions to amplify or cancel out probabilities. This principle helps quantum algorithms solve complex problems more efficiently.

You are likely very confused. This is normal! Quantum physics is weird and takes time to grasp. Let’s go into a bit more detail to understand each concept and how quantum computers exploit it.

Superposition

Consider the famous thought experiment, Schrödinger’s cat. Let’s say we put a cat in a box with a device that will with 50% probability release a poison into the box, killing the cat, and with 50% probability do nothing. According the quantum physics, before we open the box (and take a “measurement” of the state of the cat), the cat exists in superposition—it is both alive and dead at the same time. It is only after the measurement that we have the cat exists as alive or dead (we could find either outcome with 50% probability). It is not that we simply do not know whether the cat is alive or dead before we measure it; instead quantum physics tells us that the cat is both at the same time. This is a crucial distinction.

Schrödingers cat experiment illustrated. We begin with a living cat. Then, once the poison-releasing device is allowed to activate with 50% probability, the cat is in superposition. The measurement of the quantum state by opening the box makes the cat become either fully alive or fully dead.

Superposition is important to understand because quantum computers rely on it to gain their computational efficiency. Quantum bits, or qubits, are able to represent a very large number of states because they can 0, 1, or anything in between.

Classical bits are zero or one: two possible states, represented as points. Qubits are a superposition of zero and one: a continuum of possible states, represented as the surface of a sphere (the Bloch sphere). Image source: Karel Dumon

Now that we understand superposition and why it is a property useful in quantum computing, let’s move on to entanglement.

Entanglement

Entanglement allows qubits to be correlated in a way that is highly useful for computations. When two particles or qubits or entangled, they exist in superposition; however, once they are measured, the observed states is correlated in a deterministic manner. Here’s a simplest example of entanglement: when one of the two entangled particles is measured, superposition of both particles (which together form a single quantum system) is lost and both particles assume the same state. The connectivity allowed by the entanglement of qubits is taken advantage of by certain quantum algorithms to achieve an exponential speed-up over classical algorithms. It’s okay if this doesn’t make a lot of sense right now; the important takeaway is that entanglement is one weird quantum property that enables quantum computers to outperform classical computers on certain tasks.

Interference

You may have learned in physics class that waves can interfere with each other; in other words, when two waves collide, for example in water, they combine to form an overall wave that is in some way the sum of its parts. To make this idea more concrete, imagine you drop two pebbles into a body of water at the same time at two different locations. When the waves produced by one pebble collide with the waves produced by the other, we observe interference:

Sometimes, the peaks of both waves align, creating an even bigger wave. That's constructive interference.
Other times, a peak from one pebble’s wave meets a trough from the other pebble’s wave, and they cancel out. That's destructive interference.

This is interference in a nutshell - waves interacting and either amplifying or canceling each other out. Interference is not limited to waves in a pool. As it turns out, this exact type of interference works with photons of light, as well as particles. This is what physicists called wave-particle duality; waves can exhibit both wave-like and particle-like properties.

In the image to the left, light is being released from two point sources in phase, resulting in interference. Red shows the regions with the most amount of light and blue shows regions with the least amount of light. As you can see, the pattern shows that in some areas the waves from the two sources constructively interfere, creating peaks in light (red), and in some areas they destructively interfere forming troughs (blue).

How does this help quantum computers?

Amplifying correct answers: Quantum computers use interference to amplify the probability of getting the right answer. It's like turning up the volume on the solution we want.
Canceling out wrong answers: At the same time, interference can reduce the probability of incorrect answers, like turning down the volume on unwanted noise.

By cleverly using interference, quantum algorithms can solve certain problems much faster than classical computers. It's like finding a shortcut through a maze by letting multiple paths interfere with each other. The wrong paths destructively interfere making the probability that we measure that answer much lower relative to the right paths.

Now that we have a very rough understanding of quantum physics and how quantum computers work, let’s take a look at the applications of quantum computers to biology.

Applications and Importance

Quantum computing has the potential to revolutionize many fields, including computational biology. We are now at the point in the development of the technology where we are starting to see quantum computers overtake our most powerful classical supercomputers in certain very specific tasks. Quantum technology still has a long way to go before it can outclass these supercomputers in an appreciable variety of useful computations but now is the best time to get involved. The 2020s will be the quantum decade and by exploring the field now, we can fully reap the benefits. Here's how it can impact biological research:

Modeling Biological Systems: Quantum computers can simulate complex biological systems with high precision, providing insights into molecular interactions and cellular processes that are difficult to model with classical computers.
Protein Folding: Understanding how proteins fold into their functional shapes is crucial for drug design and disease understanding. Quantum computing can enhance our ability to predict protein structures and folding pathways.
Drug Discovery: Quantum algorithms can accelerate the process of discovering new drugs by simulating molecular interactions and optimizing drug candidates more efficiently.
Genomics/Proteomics Data Analysis: Quantum computing can handle vast amounts of genomic and proteomic data, uncovering patterns and relationships that are challenging to detect with classical methods.
Simulating Biomolecular Dynamics: By simulating the dynamic behavior of biomolecules, quantum computers can provide insights into how they function and interact, aiding in the development of new therapeutic strategies.
Modeling Gene Regulatory Networks: Quantum computing can improve our understanding of gene regulation by analyzing complex networks of gene interactions and regulatory mechanisms.

An excellent in-depth discussion of these applications, as well as more details on the math and hardware used in quantum computing is the research article “Quantum Computing in the Next-Generation Computational Biology Landscape: From Protein Folding to Molecular Dynamics” by Pal et al. (2024).

Resources for Getting Started

IBM Quantum Platform: Offers a suite of tools for quantum computing, including access to real quantum computers and educational resources. Access the online platform here and make an account to get started.

Educational Resources: Explore lessons, courses, and documentation available on the IBM Quantum website to deepen your understanding of quantum computing and its applications in biology.
IBM Quantum Composer: A beginner-friendly tool for building and running quantum circuits. This tool can be accessed through the quantum platform.
Qiskit: An open-source quantum computing framework in Python, useful for developing quantum algorithms. Familiarity with NumPy and matplotlib can enhance your ability to work with Qiskit.

PennyLane: A quantum computing software and website with numerous educational resources, including a quantum chemistry course that will provide students with both the knowledge background and skill foundation necessary to get started with quantum computing and applying it to biochemical problems.
Research Papers: While not often the most beginner-friendly introduction to a field, research papers are great at offering a detailed description of real, novel work being done in any area of research. After gaining a foundational knowledge in quantum computing, reading research papers related to quantum computational biology can provide students with specific examples of how quantum is being applied to computational biology. Here are some examples to get your research started:

Quantum Computing in the Next-Generation Computational Biology Landscape: From Protein Folding to Molecular Dynamics by Pal et al. (2024)
Quantum Computing at the Frontiers of Biological Sciences by Emani et al. (2021)
Kernel Alignment for Quantum Support Vector Machines Using Genetic Algorithms by Creevey et al. (2023)
An Independent Implementation of Quantum Machine Learning Algorithms in Qiskit for Genomic Data by Singh and Pokhrel (2024)

Online Courses: Educational platforms like Coursera and Udemy can provide valuable instruction on quantum computational biology. For example, the Udemy course, “Exploring Quantum Computational Biology” could be a good starting place.
Hands-on Projects: We can suggest some hands-on projects that combine elements of quantum computing and biology. These projects can help students build a practical skills for future work in quantum computational biology after they have mastered the basics of programming quantum computers and using algorithms.

Quantum circuit simulations of simple biological systems: Using tools like Qiskit or PennyLane, students can create quantum circuits that model basic biological processes. For example, they could simulate simple genetic algorithms or protein folding problems.
Quantum machine learning for biological data classification: Students can use quantum machine learning algorithms to classify biological data, such as gene expression patterns or protein structures. This project would combine quantum computing concepts with real biological datasets.
Quantum-inspired optimization for biological problems: Students can implement quantum-inspired algorithms to solve optimization problems in biology, such as finding optimal drug combinations or predicting protein structures.
Quantum random walk simulations of molecular diffusion: Students can create quantum circuits that simulate random walks, which can be used to model molecular diffusion in biological systems.

Quantum Computational Biology