Quantum Monte Carlo with Coupled-Cluster wave functions

We introduce a novel many body method which combines two powerful many body techniques, viz., quantum Monte Carlo and coupled cluster theory. Coupled cluster wave functions are introduced as importance functions in a Monte Carlo method designed for the configuration interaction framework to provide rigorous upper bounds to the ground state energy. We benchmark our method on the homogeneous electron gas in momentum space. The importance function used is the coupled cluster doubles wave function. We show that the computational resources required in our method scale polynomially with system size. Our energy upper bounds are in very good agreement with previous calculations of similar accuracy, and they can be systematically improved by including higher order excitations in the coupled cluster wave function.Comment: Submitted to Physical Review Letter

Faster spectral density calculation using energy moments

Accurate predictions of inclusive scattering cross sections in the linear response regime require efficient and controllable methods to calculate the spectral density in a strongly-correlated many-body system. In this work we reformulate the recently proposed Gaussian Integral Transform technique in terms of Fourier moments of the system Hamiltonian which can be computed efficiently on a quantum computer. One of the main advantages of this framework is that it allows for an important reduction of the computational cost by exploiting previous knowledge about the energy moments of the spectral density. For a simple model of medium mass nucleus like $^{40}$Ca and target energy resolution of $1$ MeV we find an expected speed-up of $\approx 125$ times for the calculation of the giant dipole response and of $\approx 50$ times for the simulation of quasi-elastic electron scattering at typical momentum transfers.Comment: 13 pages, 4 figure

Importance sampling for stochastic quantum simulations

Simulating complex quantum systems is a promising task for digital quantum computers. However, the depth of popular product formulas scales with the number of summands in the Hamiltonian, which can therefore be challenging to implement on near-term as well as fault-tolerant devices. An efficient solution is given by the stochastic compilation protocol known as qDrift, which builds random product formulas by sampling from the Hamiltonian according to the magnitude of their coefficients. In this work, we unify the qDrift protocol with importance sampling, allowing us to sample from arbitrary distributions while controlling both the bias as well as the statistical fluctuations. We show that the simulation cost can be reduced while achieving the same accuracy by considering the individual simulation cost during the sampling stage. Moreover, we incorporate recent work on composite channel and compute rigorous bounds on the bias and variance showing how to choose the number of samples, experiments, and time steps for a given target accuracy. These results lead to a more efficient implementation of the qDrift protocol, both with and without the use of composite channels. Theoretical results are confirmed by numerical simulations performed on a lattice nuclear effective field theory.Comment: 15 pages, 10 pages supplemental materia