Emerging technologies like artificial intelligence, blockchain, robotics, internet of things (IoT) and quantum computing will power major advances in robo-advice capabilities.
Robo-advisors are digital/mobile asset management and financial advice platforms, a class of financial advisors that provide financial advice or portfolio management online with minimal human intervention and based on mathematical rules or algorithms.
Various quantum algorithms oﬀer substantial speedups relative to classical algorithms
The innovation behind quantum computing is in the way it takes advantage of quantum-mechanical phenomena such as superposition and entanglement that occur at the subatomic level to perform operations on data.
Whereas common digital computing requires that the data be encoded into binary digits (bits), each of which is always in one of two definite states (0 or 1), quantum computation uses quantum bits or qubit. A qubit is a unit of quantum information and the quantum counterpart of the classical bit.
A qubit can be zero and one at the same time, which is called a superposition of states. It is this property which allows quantum computers to perform parallel computations on a massive scale that offer substantial speedups relative to classical systems we use today.
Only small quantum processors are currently available, however there is a widespread belief that quantum computing will experience an enormous growth rate in the near future
There are quantum computers based on the quantum gate model and quantum circuits, which are the most like our current classical computers based on logical gates, and quantum annealers, which are designed with the purpose of ﬁnding local minima in combinatorial optimization problems.
The main companies currently developing general-purpose quantum processors are Alibaba, IBM, Google, and Rigetti, IonQ, Xanadu, and Microsoft.
On the other hand, some experimental quantum annealers are already commercially available. The most prominent example is the D-Wave processor which has been heavily tested in laboratories and companies worldwide, including Google, LANL, Texas A&M, USC, and more.
Other small-scale quantum annealers are already pursued by initiatives and start-ups, such as Qilimanjaro and NTT.
How can we use the quantum technology for robo-advisors: Optimization models, Machine learning methods, and Monte-Carlo-based methods
The modern robo-advisors in achieving their goals of the personalization, science and technology implementation, providing objectivity and trust, usability and security, democratization of the services automate the core business processes and functions such as financial planning, account aggregation, rebalancing, dividend reinvestment, asset allocations and others.
By targeting inefficiencies in the wealth management value chain using optimized digital processes, robo-advisors demonstrate more efficient workflows, more optimal portfolio management, increased access to assets, improved client experiences and greater transparency.
The best algorithms should be implemented by the robo-advisors for solving such problems as which assets should be included in an optimum portfolio, how should the composition of the portfolio change according to what happens in the market, how to detect opportunities in the diﬀerent assets in the market, take proﬁt by trading with them, and how to estimate the risk and return of a portfolio.
These are tasks which are particularly hard for classical computers, but ﬁnd a natural formulation using quantum optimization methods. In recent years, this ﬁeld has known a tremendous growth, partly due to the commercial availability of quantum annealers
At this point of time, quantum computing is best suited to solving these problems using three types of algorithms: optimization, sampling, and machine learning.
Optimization—the primary area of focus in quantum computing. Optimization problems are the challenges where the goal is to find the best decision out of a large number of possible decisions. Quantum computing shows promise in helping to determine attractive portfolios given thousands of assets with interconnecting dependencies.
Another way to approach these problems is to search for patterns in past data. This is a natural way to consider economic forecasting problems, an area where machine learning methods have proved to be extremely successful.
Moreover, the behavior of some ﬁnancial systems can be predicted by applying Monte Carlo methods. The stochastic approach is typically used to simulate the eﬀect of uncertainties aﬀecting the ﬁnancial object in question, which could be a stock, a portfolio, or an option. This makes Monte Carlo methods applicable to portfolio evaluation, personal ﬁnance planning, risk evaluation, and others.
Qubits also have some very peculiar properties. It is not possible to copy qubits. This is another big story (quantum internet and cybersecurity) for the future quantum computers application in the robo-advisors’ industry.
1. Robo-advisor is not a bot, why not by Katarina Prozorova, FTC Institute
2. Quantum computing for ﬁnance: overview and prospects by Roman Orus, Samuel Mugel, and Enrique Lizaso
3. The Quantum Internet and Quantum Computers: How Will They Change the World? Delft University of Technology, edX.