What is Quantum Computing?
Quantum computers use the principles of quantum mechanics to perform calculations. By using special hardware, quantum computing exploits the properties of particles and waves on the minute scale. There is no explanation for the operation of these quantum devices in classical physics, and quantum computers with scalable capabilities can perform some calculations exponentially faster than modern “classical” computing devices. An effective quantum computer might be able to crack common encryption schemes and facilitate physical simulations. Nonetheless, in 2023, quantum computers are still largely experimental and impractical.
The Difference Between Quantum & Classical Computing
There is a fundamental difference between quantum computers and classical computers in how they process information. By replacing transistors with qubits, quantum computers can simultaneously represent 0’s and 1’s of binary information.
The power of a quantum computer increases exponentially with the number of qubits it contains. This is in stark contrast to a conventional computer whose power is directly proportional to the number of transistors it has. This could lead to scenarios in some types of computation, where quantum computers could eventually be superior to classical computers for this reason.
Although quantum computers have the potential to significantly outperform (or is the general consensus among many in the industry) classical computers in certain tasks such as simulating chemical reactions or optimization tasks, they are difficult to manufacture and are not expected to offer many advantages in other computations.
Despite the increased power of quantum computers, most everyday operations will likely still be performed in a more effective way by conventional computers.
3 Advantages of Quantum Computing
1. Chemical Simulation
In the field of chemical simulation, quantum computing has the potential to significantly improve processes and bring several benefits.
Scientists may be able to explore larger and more complex molecular structures using this increased computing power, enabling them to achieve more accurate and detailed simulations of chemical systems due to the exponential complexity of the quantum world, which classical computers have difficulty accurately simulating.
Quantum chemical simulation uses a variety of methods ranging from computational accuracy and cost. Here’s an example of all three:
- Density Functional Theory (DFT) determines the electronic density rather than the wave function and is a widely used method. This algorithm offers a good balance between accuracy and computational cost, and is also capable of handling large systems.
- Hartree-Fock theory (HF) approximates electron-electron interactions and solves the Schrödinger equation for the behavior of the average electron. Despite its usefulness for more advanced calculations, it ignores the effect of electron correlation.
- Another thing worth mentioning is the Post-Hartree-Fock Method, which goes beyond the Hartree-Fock approach to include the effect of electron correlation more accurately. Examples include the configuration interaction (CI) method, coupled cluster (CC), and multi-configuration self-consistent field (MCSCF).
As a result of quantum technology, route planning and logistics have also changed. Using a quantum computer can help reduce the cost of transporting goods and significantly improve customer satisfaction by supporting global routing optimization and frequent re-optimization.
In the field of quantum optimization, the Quantum Approximate Optimization Algorithm (QAOA) has become one of the most famous algorithms. In QAOA, classical optimization techniques are combined with quantum computing to achieve approximate solutions to optimization problems.
Quantum Annealing (QA) is another approach that uses quantum fluctuations to find optimal solutions at low energy levels. Quadratic Unconstrained Binary Optimization (QUBO) problems and the famous NP-hard Ising model are very useful QA applications.
3. Machine Learning
Another important – and now relevant since the emergence of ChatGPT late last year – is the possible contribution of quantum computing to the development of the next generation of artificial intelligence (AI), although it is debatable whether QML will have any advantage at all.
The ability to handle complexity and keep possibilities open is a clear advantage for the status-quo machine learning (ML), which is often hindered by limited scope, inability to adapt to new situations, and lack of generalizability. Thus, quantum computers could enable the development of artificial general intelligence (AGI), although there are some who see this as a major danger.
In a tweet last month, Nick Farina, CEO of EeroQ, wants to know if “QC will be commercially useful for AI”. He himself could find no evidence. Replies from two interesting experts:
“The most interesting demos of the potential advantages all involve QML applied to quantum data. That means we should consider storing “Data in the Quantum Domain” — a quantum sensor with coherent output coupled to a QC using coherent communication. Possible & exciting, but far in the future.
“QC is generally very good at basic math, so you would expect big gains for some problems if you implement QRAM successfully.”
What Do Experts Say?
John Preskill’s founding
John Preskill, an American theoretical physicist and the Richard P. Feynman Professor of Theoretical Physics at the California Institute of Technology, and Director of the Quantum Material and Information Institute, said in interview for the Caltech Science Exchange:
“Hype is natural. Everyone understands that calculations are important, affect our daily lives, have economic value. We’ve seen in recent years a sharp increase in interest within the technology industry and from investors in quantum computing. That’s a good thing in a number of ways. This accelerates progress and provides opportunities for people to work in the field. But we have to be realistic about the time scales of quantum computing having a big practical impact. And we also have to appreciate that quantum computers probably won’t be able to accelerate everything we want to do with computers but will do so for a special class of problems — and we still have a partial understanding of what those problems are. We will understand it better when we have a quantum computer and can experiment with it.”
Ilana Wisby’s attitude
In the interview with Silicon Republic in 2021, Ilana Wisby, founding CEO of Oxford Quantum Circuits, stated just that
“The power of quantum computing will allow us to transform the modern laboratory through massively enhanced materials modeling and discovery, deliver tremendous impact and innovation in enabling drug discovery, develop new battery technologies, and much more.
Quantum computing has the potential to reshape the world as we know it: revolutionize business, pioneer new approaches in all fields, and solve some of the world’s most intractable problems.”
Christopher Savoie Founding
Christopher Savoie, meanwhile, Co-founder and CEO at Zapata Computing, said in interview with University of Rhode Island Magazine:
“Quantum computing could produce fuel cells that are 100 percent efficient, sweep through advances in drug discovery and personalized medicine, and maybe even become a catalyst for removing pollution from the air.”
Where Is Quantum Computing Going?
We are still in the early stages of developing quantum computing hardware. In the near future, quantum computing hardware (as well as software) will likely be very different than it is today. First, a high degree of parallelization (because parallel operations are essential for correcting errors) and scalability will be required. We also need to consider storage errors, which affect qubits that are not acted upon by gates, in addition to errors introduced by the quantum gates themselves.
Difficult? Are these problems insurmountable? No, but that would surely be difficult but is the only way to improve our current position.
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Featured image: Rigetti Computing/ Justin Fantl