Quantum algorithms from fluctuation theorems: Thermal-state preparation

Abstract

Fluctuation theorems provide a correspondence between properties of quantum systems in thermal equilibrium and a work distribution arising in a non-equilibrium process that connects two quantum systems with Hamiltonians H0 and H1=H0+V. Building upon these theorems, we present a quantum algorithm to prepare a purification of the thermal state of H1 at inverse temperature β≥0 starting from a purification of the thermal state of H0. The complexity of the quantum algorithm, given by the number of uses of certain unitaries, is O~(eβ(ΔA−wl)/2), where ΔA is the free-energy difference between H1 and H0, and wl is a work cutoff that depends on the properties of the work distribution and the approximation error ϵ>0. If the non-equilibrium process is trivial, this complexity is exponential in β‖V‖, where ‖V‖ is the spectral norm of V. This represents a significant improvement of prior quantum algorithms that have complexity exponential in β‖H1‖ in the regime where ‖V‖≪‖H1‖. The dependence of the complexity in ϵ varies according to the structure of the quantum systems. It can be exponential in 1/ϵ in general, but we show it to be sublinear in 1/ϵ if H0 and H1 commute, or polynomial in 1/ϵ if H0 and H1 are local spin systems. The possibility of applying a unitary that drives the system out of equilibrium allows one to increase the value of wl and improve the complexity even further. To this end, we analyze the complexity for preparing the thermal state of the transverse field Ising model using different non-equilibrium unitary processes and see significant complexity improvements.

Variational Gibbs State Preparation on NISQ devices

“…However, other measures can be used, which have different interpretations. One example is the trace distance [41], which enjoys the property that, if its value between the two states is bounded by , expectation values computed on the effectively prepared state, differ from those taken on the Gibbs state by an amount that is, at most, proportional to [34]. Another choice is the relative entropy [41], which describes the distinguishability between the two states as the surprise that occurs when an event happens that is not possible with the true Gibbs state [42].…”

“…Recent methods also propose using rounding promises [33], fluctuation theorems [34], pure thermal shadow tomography [35], and minimally entan-gled typical thermal states for finite temperature simulations [36].…”