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Reverse quantum annealing approach to portfolio optimization problems

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We investigate a hybrid quantum-classical solution method to the mean-variance portfolio optimization problems. Starting from real financial data statistics and following the principles of the Modern Portfolio Theory, we generate parametrized samples of portfolio optimization problems that can be related to quadratic binary optimization forms programmable in the analog D-Wave Quantum Annealer 2000QTM. The instances are also solvable by an industry-established genetic algorithm approach, which we use as a classical benchmark. We investigate several options to run the quantum computation optimally, ultimately discovering that the best results in terms of expected time-to-solution as a function of number of variables for the hardest instances set are obtained by seeding the quantum annealer with a solution candidate found by a greedy local search and then performing a reverse annealing protocol. The optimized reverse annealing protocol is found to be more than 100 times faster than the corresponding forward quantum annealing on average.

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  1. Collateralized Synthetic Obligation (CSO) is a type of Collateralized Debt Obligation (CDO) where credit exposure to the reference names is provided in synthetic form via single name Credit Default Swaps (CDS). A typical CSO references between 100 and 125 equally weighted names.

  2. Note that the graph is not ideal, there is a set of 17 qubits that have not been calibrated successfully and are unoperable (see Fig. 4 in Appendix 2).

  3. In the actual embedding employed, it might happen that some pairs of logical variables i,j could have two pairs that can be coupled, instead of one. In that case, we activate both couplings at a strength Jij/2 to preserve the classical value of the objective function.

  4. Many options are possible, since the duration of the three phases can be chosen arbitrarily within limited but wide ranges.

  5. We note that optimal portfolios constructed through minimization of objective function (2) and the number of asset constraints with QUBO coefficients given by Table 1 have typically better Sharpe ratios than alternative portfolios constructed from the individually best assets where the ai coefficients have not been coarse-grained in buckets.

  6. This algorithm is inspired by the routine provided by D-Wave Systems to decode the binary value of a set of qubit measurements that are originally associated to a single logical variable si (i.e., the Nc spins ferromagnetically coupled during embedding—see Eq. (5) and Ref. King and McGeoch 2014).

  7. For the largest problems studied, according to Hamerly et al. (2018), there might be an advantage in varying τ, but this is usually a small prefactor. See also Appendix 3 for results on a limited set of instances.

  8. We believe that the non-monotonic behavior for N = 54 is not of fundamental significance but it is due to the finite small size of our instance set for reverse annealing.

  9. Programming time, post-programming thermalization time, readout time; respectively 7.575 ms, 1 ms, 124.98 μs for the current experiments.

  10. The reported median TTS on these runs seems to be in general faster than the results in the main paper. This could be due to finite statistics effect or to general drift in performance of the machine over time, since the runs relative to Fig. 5 were performed more than a month earlier when the machine was under low utilization. The effective temperature of the machine can vary of few milliKelvins over time for uncontrollable factors, and this is known to affect the performance of quantum annealing (Boixo et al. 2016).


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The collaboration between USRA and Standard Chartered Bank has been supported by the USRA Cycle 3 Program that allowed the use of the D-Wave Quantum Annealer 2000QTM, and by funding provided by NSF award no. 1648832 obtained in collaboration with QC-Ware. We acknowledge QC-Ware and specifically thank Eric Berger, for facilitating the collaboration and contributing to the runs on the D-Wave machine. D.V. acknowledges general support from NASA Ames Research Center and useful discussions with QuAIL research team. A.K. would like to thank David Bell and USRA for the opportunity to conduct research on the quantum annealer at QuAIL.

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Correspondence to Davide Venturelli.

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The authors alone are responsible for the content and writing of the paper. The opinions expressed are those of the authors and do not necessarily reflect the views and policies of Standard Chartered Bank or the Universities Space Research Association. All figures are based on own calculations.

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Appendix 1. Geometric Brownian motion

A geometric Brownian motion (GBM) is a stochastic process S(t) that satisfies the following stochastic differential equation (SDE):

$$ dS(t) = \mu S(t) dt + \sigma S(t) dB(t) , $$

where t is continuous time and B(t) is a Brownian motion. GBM is widely used to model asset prices. If a unit of time is 1 year, then σ is interpreted as an annualized volatility (standard deviation) of asset’s log-returns, which are assumed to be normally distributed. The drift coefficient μ controls deterministic component of the asset price process.

Integrating the process, we obtain:

$$ S(t) = S(0) \exp \left( \left( \mu - \frac{1}{2}\sigma^2 \right)t + \sigma B(t)\right) . $$

Although GBM SDE can be used directly to simulate an asset process, it is better to use its solution to ensure that simulated asset prices do not turn negative—this may be the case for large enough time step. In our portfolio optimization example Δt = 1 month and we use the following discretization scheme for a single asset price process:

$$ S(t_{n}) = S(t_{n-1}) \exp \left( \left( \mu - \frac{1}{2}\sigma^2 \right){\Delta} t + \sigma z_{n} \sqrt{{\Delta} t} \right) , $$

where tn = tn− 1 + Δt and zn is a standard normal random variable. Asset prices from the N-asset portfolio are jointly simulated using the same scheme but correlated standard normal random variables (z(1),…,z(N)) are constructed via Cholesky decomposition of the correlation matrix ρ.

Appendix 2. Chimera graph of DW2000Q and embedding

In Fig. 4, we show the layout of the chip used for the experiments, belonging to the machine D-Wave 2000Q hosted at NASA Ames Research Center.

Fig. 4
figure 4

Chimera Chip of DW2000Q. Each gray dot represents an active qubit (missing dots are broken qubits), the black shaded square is representative of one unit cell. The embedding for an instance (N = 42) is highlighted: blue bonds are ferromagnetic couplings set to JF, while red and pink bonds represent logical couplings (Jij in Eq. (6))

Appendix 3. More details on parameter setting for reverse annealing

Figure 5 displays median TTS results obtained for the mapping schemes provided by Table 1 and annealing times 1 µs and 10 µs, obtained for the first 10 instances of the benchmark ensamble on an independent set of runs with respect to the results presented in Fig. 3.Footnote 10 It is clear that the choice of τ = 1 µs is the most advantageous.

Fig. 5
figure 5

Time-to-solution (99% confidence level) for different trun (median over 10 instances)

In Fig. 6, we show on an example how the optimal parameter setting is performed to generate data in Figs. 2 and 3. Scans are performed for different JF and sp and the best TTS is selected, instance by instance.

Fig. 6
figure 6

Time-to-solution (99% confidence level) as a function of annealing parameter sp. Results for a single instance, N = 42, τ = 1 µs. Circles point out to the best found (JF, sp) for these three illustrative cases

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Venturelli, D., Kondratyev, A. Reverse quantum annealing approach to portfolio optimization problems. Quantum Mach. Intell. 1, 17–30 (2019).

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