Quantum computers have the potential to revolutionize many fields, and one of the most exciting applications is quantum simulation. This article explores what quantum simulation is, why it holds such promise, and how it can be practically applied using tools like IBM’s Qiskit.
Quantum simulation is a powerful tool for discovering new materials with unique physical properties. This field is part of condensed matter physics, which studies the properties of solids and liquids. Despite its significant contributions to modern technology, such as the development of semiconductors, condensed matter physics is not widely known.
The ability to understand and manipulate materials has been a cornerstone of technological advancement. Materials are essentially configurations of atoms, and there are likely many undiscovered configurations with novel properties. For instance, superconductivity is a fascinating phenomenon where certain materials can conduct electricity without resistance at very low temperatures. Discovering a material that can superconduct at room temperature could lead to more efficient and compact technologies.
Currently, the search for new materials is somewhat trial-and-error, akin to alchemy. We lack a comprehensive theory for high-temperature superconductivity, so researchers experiment with various combinations of materials. Quantum simulators could accelerate this process by quickly evaluating the properties of countless material combinations.
Consider the example of fertilizer production, which is energy-intensive and has a high carbon footprint. Improving the catalyst used in this process could significantly reduce energy consumption. Understanding the quantum mechanics of such chemical reactions is challenging with classical computers, but quantum simulators offer a promising solution.
Quantum computers are being developed by several companies worldwide, with qubits as their fundamental units of computation. The goal is to increase both the number and quality of qubits to reduce error rates. As of now, Google and IBM have quantum computers with 72 and 65 qubits, respectively.
While classical computers can simulate quantum systems, the complexity increases exponentially with more quantum particles. Quantum simulators, however, can theoretically solve these problems much faster. The future likely involves a hybrid approach, combining quantum and classical techniques, similar to how CPUs and GPUs are used together today.
Quantum simulators are particularly useful for chemistry simulations, where even small errors can lead to significant inaccuracies. For example, simulating a hydrogen molecule requires 56 qubits, which Google achieved in 2016. IBM and IonQ have also made strides in simulating more complex molecules.
Using IBM’s quantum computer, I conducted a quantum simulation to find the natural configuration of a lithium hydride molecule. The results matched the expected lowest energy state, demonstrating the potential of quantum simulations.
What’s exciting is that these tools are accessible to the public through platforms like Qiskit, which offers tutorials and resources for learning quantum computing. This hands-on experience is invaluable for understanding quantum physics concepts like superposition and entanglement.
Looking forward, IBM plans to develop a quantum computer with over a thousand qubits by 2023, and both Google and IBM aim for systems with over a million qubits by 2030. While increasing qubit count is crucial, improving their quality is equally important for scalability. Challenges remain, such as implementing quantum error correction and managing the complexity of large-scale systems.
Despite skepticism, the progress in quantum computing over recent years is promising. Investing in the development of large-scale quantum computers could lead to solutions for real-world problems, making the endeavor worthwhile.
In conclusion, quantum simulation offers a glimpse into the future of technology and materials science. As we continue to explore and develop these capabilities, the potential for groundbreaking discoveries is immense.
Participate in a hands-on workshop using IBM’s Qiskit platform. You’ll learn to set up and run basic quantum simulations, exploring concepts like superposition and entanglement. This activity will help you understand how quantum simulators can be used to model complex systems.
Analyze case studies of successful quantum simulations in material science. Discuss in groups how these simulations have contributed to the discovery of new materials and what challenges were overcome. This will deepen your understanding of the practical applications of quantum simulation.
Engage in a debate about the future of quantum computing. Consider the potential benefits and challenges, such as qubit scalability and error correction. This activity will encourage you to critically evaluate the current state and future prospects of quantum technology.
Work in teams to design a quantum simulation project that addresses a real-world problem, such as optimizing chemical reactions or discovering new superconductors. Present your project plan and expected outcomes to the class. This will help you apply theoretical knowledge to practical scenarios.
Attend a series of guest lectures from experts in quantum computing and condensed matter physics. These lectures will provide insights into the latest research and developments in the field, enhancing your understanding of quantum simulation’s role in scientific advancement.
Here’s a sanitized version of the provided YouTube transcript:
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I’ve talked before about how my favorite application of quantum computers is quantum simulations. I thought I’d make a video to explain what it is, why I’m so excited about it, and later on, I’ll be running my very own quantum simulation on an IBM quantum computer. I’ll be using a software development kit called Qiskit, which is sponsoring this video. What’s really cool is that this is available to anyone for free to run software on a quantum computer. More on that later.
First of all, why am I so excited about quantum simulation? Quantum simulation is a promising route to discover the technologies of the future by helping us investigate or discover new materials with novel physical properties. The area of physics involved in studying the physical properties of solids and liquids is called condensed matter physics, which is what I did my PhD in. Interestingly, not many people have heard of condensed matter physics, despite it being a huge area of research that has significantly contributed to the modern world.
A big success story of condensed matter physics was the ability to describe and understand band structures, which explains how semiconductors conduct electricity. This understanding led to many technologies, including computers and the internet, contributing to what we call the digital age. A significant part of human history is our ability to find and harness materials and use their physical properties to invent new technology.
So, what is a material? A material is a specific configuration of atoms. There could be configurations of atoms out there with novel physical properties, such as their strength, how they interact with light, or their magnetic and electronic properties. We want to search for novel materials because we probably haven’t discovered them all yet.
Let me give you a concrete example: superconductivity. Superconductivity is a unique feature of certain materials. For example, if you take a wire of aluminum and cool it down to a very low temperature (just 1.75 Kelvin), it will start to superconduct, meaning electrical current can flow through it with zero resistance. You could set up an eddy current in an aluminum wire that would keep looping forever, as long as you keep it cold.
We already use this technology for superconducting magnets in MRI scanners and particle accelerators, but keeping them cold is a challenge. For a long time, people have been searching for a material that superconducts at room temperature, which would allow us to build computers that wouldn’t generate heat, making them more compact and energy-efficient.
Earlier this year, researchers discovered the first room-temperature superconductor material called carbonaceous sulfur hydride, which superconducts at 15 degrees Celsius (59 degrees Fahrenheit) but only under extreme pressure. The point is, there could be a material out there that superconducts at room temperature and pressure; we just haven’t discovered it yet.
Currently, searching for that material is a bit like alchemy, as we don’t have a theory of high-temperature superconductivity. We try different combinations of materials and see what their properties are. With a quantum simulator, we could potentially search through all those combinations much quicker and measure their physical properties.
The big picture is that we have the entire periodic table at our disposal, and different combinations of elements will produce materials with different physical properties. However, the number of combinations is vast—essentially infinite. Another aspect is what happens when we bring those materials together, which is essentially chemistry, leading us to another infinite set of combinations.
For example, making fertilizer for agriculture is currently a very energy-intensive process with a high carbon footprint. If we could improve the catalyst used to convert molecular nitrogen into a form that plants can use, we could reduce the energy expenditure of this process. Bacteria do this much more efficiently than our current processes, and we’d love to understand the quantum physics of this chemical reaction. However, our current best techniques on conventional computers struggle with this, making it brilliant to simulate on a quantum computer.
Researchers at Microsoft calculated that you could simulate this if you had 200 perfect qubits, which practically means you’d need about 200,000 physical qubits, depending on their quality. This gives us an idea of how many qubits you’d need to solve valuable problems.
Now, where do we stand in the world today? A big part of this is the development of quantum computers. Several companies are developing quantum computers worldwide. Qubits are the fundamental unit of computation in a quantum computer, and everyone is trying to increase the number of qubits in their systems while also improving their quality to reduce error rates.
As of this video, Google has a quantum computer with 72 qubits, and IBM has one with 65 qubits. There are also other techniques for quantum simulation, like trapped ion systems or ultracold atoms trapped in optical lattices.
You might wonder what’s stopping us from simulating quantum systems on a conventional computer. While we can do that, it becomes exponentially more difficult with more quantum particles involved. This means a quantum simulator will theoretically be exponentially faster at solving the laws of quantum physics than a classical computer. However, we are quite good at using conventional computers to approximate solutions through statistical methods.
In the future, the sensible approach for quantum simulators will be to use them alongside classical techniques in a hybrid approach, similar to how we currently use CPUs and GPUs in our computers. The real potential for quantum simulators is to solve problems where approximate techniques fail, such as in chemistry simulations where even a small error can lead to significant inaccuracies.
For chemistry simulations, you need one qubit for each electron orbital. To perfectly simulate hydrogen, you’d need 56 qubits, which Google achieved in 2016. However, that’s an upper limit; you can use encodings to decrease that number. In 2017, IBM simulated lithium hydride and beryllium hydride, and in 2019, IonQ managed to simulate water using a trapped ion system.
Now, let’s run an actual quantum simulation. I’m currently connected to an IBM quantum computer, and in this simulation, I’m trying to find the natural configuration of a lithium hydride molecule—the distance between the lithium atom and the hydrogen atom in that molecule, which corresponds to the lowest energy state.
Here are the results: the orange points from the quantum simulation compared to the exact solution show that the lowest energy is the correct distance between the atoms at 1.5 angstroms. This is a simple simulation, but the fact that I’m running it on an actual quantum computer is exciting.
What’s really cool is that this is open to the public, so anyone can write code and access IBM’s quantum computers. This is done using a software library called Qiskit, and I learned how to use it through Qiskit’s YouTube channel, which has excellent tutorials on various aspects of quantum computing.
Many people have asked how to get into quantum computing or learn quantum physics, and I think this is a great way to do it. It provides motivation to learn about features of quantum physics, like superposition and entanglement, by getting hands-on experience.
So, how long until we have a quantum computer capable of solving real-world problems? IBM has released their roadmap, aiming to produce a system with over a thousand qubits by 2023, and both Google and IBM hope to create a system with over a million qubits by 2030. Over the next decade, we can expect significant development, likely reaching a point where they can solve useful problems that classical computers cannot.
However, just increasing the qubit count is only one side of the equation; improving the quality of the qubits is also crucial for scalability. There are skeptics and significant challenges to overcome, such as routing the wiring to address a million qubits, which is currently a problem. Additionally, we need to implement quantum error correction at scale, which works in theory but hasn’t been achieved in practice yet.
There are still open questions, and skepticism exists, but I believe we’ve seen enough progress over the past few years to be optimistic. We don’t know how difficult it will be to scale up these systems, but the only way to find out is to invest resources and build them. If we can create large-scale quantum computers, they will help solve real-world problems, making the investment worthwhile.
That’s all I wanted to say about quantum simulation. I hope you enjoyed the video, and I wish you happy holidays! I’ll be taking a break to learn more science facts and will return in the new year with more videos. See you then!
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This version removes informal language, filler words, and maintains a more professional tone while preserving the core content.
Quantum – Relating to the smallest discrete quantity of some physical property that a system can possess, often used in the context of quantum mechanics. – In quantum mechanics, particles can exist in multiple states at once, a phenomenon that challenges classical physics.
Simulation – The imitation of the operation of a real-world process or system over time, often used in computer science to model complex systems. – The physics department used a simulation to model the behavior of particles in a collider experiment.
Materials – Substances or components with certain physical properties used in the creation of physical objects, often studied in materials science. – Researchers in materials science are developing new materials that can withstand extreme temperatures for use in aerospace applications.
Physics – The natural science that studies matter, its motion, and behavior through space and time, and the related entities of energy and force. – The physics lecture today covered the fundamental forces that govern the universe.
Superconductivity – A phenomenon where a material can conduct electricity without resistance when it is below a certain temperature. – The discovery of superconductivity in certain materials at higher temperatures has significant implications for energy transmission.
Chemistry – The branch of science concerned with the substances of which matter is composed, their properties, and reactions. – Understanding the chemistry of semiconductors is crucial for developing more efficient solar cells.
Computing – The use or operation of computers, often involving the processing of data or the development of algorithms. – Quantum computing promises to revolutionize the way we solve complex problems by leveraging the principles of quantum mechanics.
Qubits – The basic unit of quantum information, analogous to a bit in classical computing, but capable of existing in multiple states simultaneously. – The power of quantum computing lies in its use of qubits, which can perform complex calculations much faster than classical bits.
Entanglement – A quantum phenomenon where particles become interconnected and the state of one instantly influences the state of another, regardless of distance. – Quantum entanglement is a key principle that enables quantum teleportation and secure communication.
Superposition – A fundamental principle of quantum mechanics where a physical system exists simultaneously in all its possible states until it is measured. – The concept of superposition allows quantum computers to process a vast amount of possibilities at once, unlike classical computers.
