In this blog post
by Shalini Milcah
Yes, that’s the power of superposition, which allows a quantum computer to simultaneously process every possible combination of 0 and 1. This leads to a misconception that quantum computers can achieve exponential speedups by simply processing every possible input in parallel, via superposition but the catch is that superposition alone is useless, because the measurement of a superposition is inherently random. At its core, the real innovation of quantum algorithms is the use of wave interference to de-randomize the superposition. For most cases, the overhead of the quantum computing model is costly enough that the quantum speedup is small or non-existent but for some important cases, quantum does indeed achieve an exponential speedup over classical (non-quantum) algorithms.
Classical computers use bits to represent and store information while quantum computers are different however. They use quantum bits also known as qubits. These ‘qubits’ can be a one, a zero or a superposition of them both at the same time which can create nontrivial correlated states of several qubits, so-called ‘entangled states’. One good way to think of this is to imagine a sphere where each pole is a different state. So, a classical ‘bit’ can be in one of two states, at either of the two poles of the sphere. The difference with a quantum bit is that it can be at any point on the sphere, significantly increasing the number of states. This means that a quantum computer that uses qubits can store a ridiculous amount of information using less energy compared to a classical computer. Since quantum bits can have multiple states, quantum computers will be millions of times faster than even the most powerful super computers that are in existence today.
Fortunately, now, quantum comp
uters are publicly cloud accessible. Just five years ago, quantum computers were restricted to privately run laboratories but the landscape has changed dramatically since 2016, when IBM launched their ‘Quantum Experience‘, a free and publicly accessible quantum computer. Since then, other companies have also announced similar cloud services, and several more are expected in the next couple of years.
Broadly, two relevant timescales can be observed in the history of Quantum computing here: Quantum computers in the 5-year horizon will be limited by error rates of about 0.1%, which means they will only support ~1000 instructions before failing. By contrast, the original algorithms (e.g. factorization, search) that motivated QC require hundreds of thousands of operations. Consequently, the dominant view till recently was that quantum computers would only achieve practical value in the 10+ year horizon, while we already have computers big enough to support error correction algorithms. However, the recent discovery of hybrid classical + quantum algorithms has motivated a new timescale of relevance: the NISQ (Noisy Intermediate Scale Quantum) era which aims to achieve practical quantum speedups on computers that are being built now and in the next 5–10 years.
However, these NISQ hybrid algorithms are not rigorously proven to outperform classical algorithms. Nonetheless, there are strong empirical indications that NISQ algorithms will win. This is exciting because it means that quantum computing is very much at a point where reality and relevance meet.
Quantum computing is just the next step. There are still a lot of problems that we cannot solve very easily, like solving a linear system of equations, optimizing parameters for support vector machines, finding the shortest path through some arbitrary graph, or searching through an unstructured list. They are rather abstract problems right now but knowing the complexity involved in these algorithms or programming, we can see how useful this could turn out to be.
It’s not immediately clear where it will be most effective, but given the recent trends of technology, its applications might include such things as the big data revolution, where we try to use machine learning algorithms to process enormous amounts of data and another application might be cryptography. Quantum computers operate on completely different principles to existing computers, which makes them well suited to solving particular mathematical problems, like finding very large prime numbers. Since prime numbers are so important in cryptography, it’s likely that quantum computers would quickly be able to crack many of the systems that keep our online information secure. Due to these risks, researchers are already trying to develop technology that is resistant to quantum hacking, and on the flipside of it, quantum-based cryptographic systems would possibly be much more secure than their conventional analogues.
The potentially new and valuable technologies debated in Quantum Computing till date include: quantum simulation, quantum sensors, quantum imaging, quantum clocks, and quantum software and algorithms.
In quantum simulation, purpose-built quantum computers would perform quantum-mechanics-level modelling of materials, which would be impractical on today’s classical computers. The simulations would elucidate the fine structures of superconductors and map out complex chemical reactions to predict whether a newly engineered material would be stable.
Quantum sensors and quantum imaging will be especially useful in medicine. For example, they’ll allow new ways to sense the heart’s magnetic field, which could more accurately diagnose and distinguish heart diseases. Being able to obtain images of things that we’ve never been able to see before.
Quantum clocks, which track the vibrations of a single atom to provide almost unimaginable accuracy, will serve a wide range of purposes including accurate measurements of the local gravity potential and precision timing of financial transactions. It is reported that the best of these quantum clocks could be made so accurate that they’d gain or lose no more than 1 second every 30 billion years.
New quantum algorithms could allow quantum computers to process data at a much higher speed, allowing for database searches, machine learning, and image recognition with unprecedented speed. Making use of such algorithms might be made easier for a broader range of coders because of quantum compilers that Microsoft and others are working on.
At this point, the value proposition of quantum computers has not been realized. Only a very few companies (Amazon, Google, Microsoft, IBM) are spending millions on their development. If achieved, a narrow but important class of problems will finally be solvable. Some others, predict quantum computers may not work in practice because of limitations imposed by thermodynamics. Theoretically, they are well understood and extremely exciting.
