at can make the computation even more efficient.
An industry standard for Quantum Computing is still to be determined so when discussing Quantum Computers, we are really discussing Qubits and Quantum Gates. There are currently 6-7 different types of qubits (see fig.1) being applied in industry and each have their own engineering challenges.
Fig.1 Source. Michel Kurek, Feb 2021.
Depending on the application, different Qubits can be used for different problems. For example, Photons are the only Qubit that can travel and can be maintained at room temperature which makes them a prime candidate for quantum communication.
Within Quantum Computing, the approach taken by most of the IT giants (excluding Microsoft) uses the superconducting properties of electrons. This requires cryogenic freezing of the electron to around 15 milli-Kelvin (slightly colder than a January morning in Chicago).
Another approach uses ions. This involves trapping an ion particle within a vacuum and is being driven by companies like IONQ and also the conglomerate, Honeywell.
In order to process the Qubits, as in classical computing, a quantum circuit applies gates (using for example microwaves or lasers) to manipulate the quantum information and finally compute an answer. There is one other way, called Quantum Annealing which is used by D-Wave (one of the industry leaders). Quantum annealing is a metaheuristic for finding the global minimum of a given objective function over a given set of candidate solutions (candidate states), by a process using quantum fluctuations. It works well on very specific problems like optimisation and if you were interested you can pick up a Quantum Annealing computer from D-wave for between $10-15ml.
Problems with Quantum Computing
There are many. The biggest one is fault tolerance. Qubits are incredibly hard to maintain. Even the slightest bit of noise (background radiation, electromagnetism, a large sneeze) impacts the Qubit making it irrelevant. The current understanding is that 1,000,000 Qubits would only really have 1,000 usable Qubits once error correction using redundancy principle is taking into account. So the end goal for Quantum Computing is to improve the stability of the Qubits and have a Quantum Universal Computer that can handle any algorithm – which realistically is still a good 10-15 years in the future (see fig.2)
Fig.2 Source. Michel Kurek, Feb 2021.
Does that mean we wait 15-20 years to play with Quantum Computers?
No. The industry is learning to live with the current deficiencies and are implementing NISQ (Noisy Intermediate-Scale Quantum) technologies. Basically this means they are developing algorithms that can be deployed on the basis that there will be some errors which can be accounted for OR they are using a combination of quantum and classical computing.
Companies like Atos Quantum have developed emulators that simulate a 50 Qubit Quantum Computer. You can also set the fault tolerance for the Qubit in the simulator to give you a realistic result based on todays machine.
Uncluttered by the computational constraints of todays infrastructure, there is also a new branch of Quantum Inspired Algorithms which can be tested on these simulators.
Is anybody using this in finance?
Yes, there are several banks that are partnering with the IT monoliths or startups or directly investing in Quantum Computing. The obvious use cases in financial services are based around Portfolio Optimisation, Quantum machine learning (being able to train a model in a fraction of the current time with much larger datasets) and modelling (JP Morgan completed a proof of concept for option pricing) https://arxiv.org/pdf/1905.02666.pdf
The advantages of being able to run vast computations in very short time periods are obvious.
Out of the c.100 industry partners working with IBM ‘s Quantum Computers, 7 are financial services firms including JP Morgan, Barclays, Goldman Sachs and Wells Fargo. Overall there are currently 20 financial institutions in the quantum ecosystem.
Goldman Sachs and Citigroup are also investing directly in Quant