Computers are used to solve problems, and the type of problems they solve depends on their algorithms and hardware as well as their capabilities and limitations. Imagine what would happen if you could lift these limitations once and for all? What would modern quantum computers be able to do then?

What has made quantum computers so exciting lately? Perhaps the fact we are approaching the limits of computing capabilities of the kinds of transistor-based machines we have long been using. They are limited by the laws of physics, which prevent us from packing more processors on a chip board.

The old transistor computer …

… and its kid brother, the quantum computer

While the difference seems to be minor, a superpositioned qubit can execute multiple calculation commands at a time, helped by the fundamental laws of quantum physics. Physically, a qubit can be represented by any quantum system with two different fundamental states such as an electron or atom spin, two energy levels in an atom, or two levels of photon polarization — vertical and horizontal.

This completely unreal situation becomes reality when using a quantum computer. A machine of this kind can process data even hundreds of thousands, and theoretically millions of times faster than devices relying on advanced silicon components! An ideal use for such a machine would be to recognize objects in a huge stock of photos, process large numbers, or encrypt and decrypt codes. By using mathematical data, we can theoretically increase the performance advantage of a quantum over a conventional computer to 1:18 000 000 000 000 000 000 times!

Probabilistic algorithms

Quantum computers are perfectly suited for specific highly-specialized calculations that rely on algorithms to harness their full power. Some of the most common applications of probabilistic algorithms are the Miller-Rabin’s test of numbers for primality (which has extensive applications in cryptography) and Quicksort, a rapid sorting algorithm. All this means that quantum computers are unlikely to appear on every desktop or in every home any time soon.

However, no matter how long it takes for a given algorithm to generate a result, we can easily imagine, even today, a scenario in which a quantum machine is needed to solve a specific problem.

Mathematics, physics, astronomy … and code breaking

Despite the impressive computational power of quantum computers, they are not simply machines that can run existing software a billion times faster. Rather, quantum computers are good at solving specific types of problems.

Smarter artificial intelligence

This is an example of feedback-based machine learning. In a nutshell, the action model is based on the calculated probability of many possible choices. Artificial intelligence is ideal for quantum computing, where probabilities drive the operation of quantum computer algorithms.

A good example is the startup Rigetti which designs neural networks using quantum computers. Rigetti has recently announced having designed a data clustering algorithm for quantum computers. In tests, the algorithm proved to cluster data faster than algorithms run on classic computers. The feat was accomplished on a 19-qubit quantum machine. What speeds can more advanced computers achieve? When it comes to quantum computing, the increase in computational power between 19 and, say, 30 qubits, is not linear but exponential. While many companies use quantum computers to run artificial intelligence algorithms, this example is one of the first successful attempts to make neural networks and quantum computers work together.

Quantum computers and artificial intelligence share another common feature: huge, exponential scalability. The power of quantum computers is measured in qubits, with the most advanced quantum computers reaching about 50Q. With such power, they are equivalent to a single supercomputer. A power increase to just 60Q would produce a machine that exceeds the collective computational power of all of the world’s supercomputers.

Quantum machine learning is the latest field of research and emerging technology that attempts to harness the power of quantum computers to accelerate the performance of classic machine-learning algorithms.

Even today’s AI systems with their machine learning algorithms are capable of processing incredible amounts of data. The process the algorithms use to search databases could be improved by employing quantum computing. Both such algorithms and quantum computer capabilities are expected to be available within a few years. Once they are here, the speed of neural networks will rise well beyond an ordinary surge. We will see it skyrocket, multiplied by a factor of millions.

Theoretically speaking, self-replicating artificial intelligence could scale itself with the expansion of hardware and cloud computing networks. This would allow artificial intelligence to create algorithms of a complexity that far exceeds any human creations. All to harness the full power of quantum computing.

An aerospace company is planning to use a quantum computer to test autopilot software used on board aircraft. The latest models of behavior of the neural networks and algorithms used in such autopilots are too complex for conventional computers to handle. Quantum computers are also used to design software that can spot and mark autonomous vehicles.

We have already reached the point where AI creates new artificial intelligence without human involvement. All this thanks to quantum computers as well as the principles of quantum physics that underpin their operation.

Qubits harnessed for molecular modeling

It is the computing power of computers in academic data centers that determines the accuracy with which various phenomena are simulated, often down to individual molecules. In highly complex systems, rather than relying exclusively on simplified presumptions to map interactions between molecules and molecule sets, as is the practice today, quantum computers will render such interactions mappable in environments that closely mimic real-life conditions. This will be possible thanks to the quantum nature of chemical reactions. By their very design and owing to their operating principles, quantum computers would have no difficulty simulating and evaluating even the most complex molecular processes. Molecular modeling is applied in nanotechnology, drug design, the exploration of biological structures having known sequences but unknown structures and functions, learning about the dynamics and thermodynamics of chemical compounds, and material research. The applications do not end — there is, in fact, a multitude of others. The main constraint on their development is the limited computing power of conventional computers.

In the early 2018, scientists from the Institute of Quantum Optics and Quantum Information of the University of Innsbruck applied an algorithm of a programmable quantum system to simulate interactions among protein molecules. As part of an experiment described in Nature, a team of researchers used a basic quantum computer to test the impact of external factors on particular ions of a molecule. In the experiment, the external factor was an attempted alteration of molecules to create a new chemical compound. The simulation showed it was possible to modify a real-life environment in such manner. This means one can build new, stable particles. The quantum simulator worked perfectly in the experiment.

Fundamentals of cryptography for code breaking

To break a private key or crack an encryption method, factorization algorithms must painstakingly attempt to make divisions by successive numbers. While the task can be completed by today’s supercomputers, it would make no financial sense to use them. The estimated time that a conventional computer would need to break a 4096-bit RSA key would exceed the time that has passed since the formation of our galaxy!

This makes breaking codes and keys costly and impractical. However, a quantum algorithm would allow one to check all potential combinations simultaneously and generate the correct solution in an instant. What this means is that today’s asymmetric encryption algorithms that rely on a a public and private key will no longer be secure and that other methods of securing data, transactions or system access will have to be found.

We can nevertheless rest easy. Very little has been achieved in the practical application of quantum factorization. Besides, ever more asymmetric encryption methods are being developed that will resist quantum computers’ attempts to crack them.

Quantum finance

Asset managers responsible for investment funds can only dream of having a perfectly balanced portfolio designed for them. To rebalance their portfolios, change the proportions of their components and restore the original desired level of asset allocation, they either buy or sell assets. Let’s say that the original ratio of shares to bonds in the portfolio is 50/50. If shares in the portfolio perform well in a given period, their proportion could increase to 70%. To restore the desired 50/50 ratio, the asset manager may have to sell some shares and buy some bonds. This means incurring transaction costs. In a market where most funds only generate short-term single-digit profits, the loss of a few percent in transaction costs to rebalance the portfolio could be devastating. And the portfolio may have to be rebalanced multiple times during a reporting period.

Quantum computers may optimize investment portfolios significantly faster than the algorithms used in classic computers, not to mention humans.

This is just one example of how quantum computers can handle the huge challenges faced by fund managers. A few years from now, quantum algorithms should be stable enough to replace people in both designing and managing investment portfolios.

At a financial conference of the Singularity University held in December 2017, the CEO of 1Qbit, Andrew Fursman said that quantum computers that rely on the most fundamental laws of nature will appear sooner than we think. One of their key applications will be in quantum finance.

Quantum weather forecasting

Although reliable weather forecasting has long been the goal of scientists, the equations governing the weather contain such a large number of variables and data that classic computer simulations are unable to perform the required calculations and produce outcomes within reasonable time limits. Figuratively speaking, for the current supercomputers, the simulation of the weather forecast for the next 4 days will take three weeks. This is not a problem of having no access to data or bad algorithms, but merely of computational power. As Seth Lloyd, a researcher who applies quantum computers for weather forecasting, pointed out: “Using a classic computer to perform such analysis might take much longer than it takes the actual weather to evolve”. The use of quantum computers would reduce processing time from weeks to hours.

While quantum physics in the form of quantum computers is already hugely impacting the areas listed above, you can easily imagine many other applications. Quantum technology and quantum algorithms are evolving. What will they bring? I hope a lot of good.

Works cited:

Intel, Over 50 years of Moore’s Law, link, 2018.

The Guardian, Ben Tarnoff, Weaponised AI is coming. Are algorithmic forever wars our future?,link, 2018.

Science Alert, Dawid Niels,Google’s Quantum Computer Is 100 Million Times Faster Than Your Laptop, link, 2018.

DI Management, RSA Algorithm, link, 2018.

NOAA, National Oceanic and Athmospheric Administration, link, 2018.

Bae Systems, Taranis, link, 2018.

DARPA, Faster, Lighter, Smarter: DARPA Gives Small Autonomous Systems a Tech Boost, Researchers demo latest quadcopter software to navigate simulated urban environments, performing real-world tasks without human assistance, link, 2018.

Technology is my passion. Head of Microsoft Services CEE. Private opinions only