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arXiv:2009.04488 · Physical Review A

Variational preparation of the thermofield double state of the Sachdev-Ye-Kitaev model

Vincent Paul Su

Published Jul 29, 2021• 14 citations
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Abstract

We provide an algorithm for preparing the thermofield double (TFD) state of the Sachdev-Ye-Kitaev (SYK) model without the need for an auxiliary bath. Following previous work, the TFD can be cast as the approximate ground state of a Hamiltonian, ${H}_{\text{TFD}}$. Using variational quantum circuits, we propose and implement a gradient-based algorithm for learning parameters that find this ground state, an application of the variational quantum eigensolver. Concretely, we find shallow quantum circuits that prepare the ground state of ${H}_{\text{TFD}}$ for the $q=4$ SYK model for $N=8$ Majoranas per side. For $N=12$, we achieve a variational energy within 1% of the true ground-state energy.

DOI
10.1103/physreva.104.012427

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We provide an algorithm for preparing the thermofield double (TFD) state of the Sachdev-Ye-Kitaev (SYK) model without the need for an auxiliary bath. Following previous work, the TFD can be cast as the approximate ground state of a Hamiltonian, HTFD{H}_{\text{TFD}}HTFD​. Using variational quantum circuits, we propose and implement a gradient-based algorithm for learning parameters that find this ground state, an application of the variational quantum eigensolver. Concretely, we find shallow quantum circuits that prepare the ground state of HTFD{H}_{\text{TFD}}HTFD​ for the q=4q=4q=4 SYK model for N=8N=8N=8 Majoranas per side. For N=12N=12N=12, we achieve a variational energy within 1% of the true ground-state energy.
Variational preparation of thermofield double (TFD) states can require deep quantum circuits, particularly for interacting many-body systems. Reducing these circuits while retaining high fidelity is therefore crucial for TFD-state preparation on noisy quantum processors. We study this problem by applying the multi-angle quantum approximate optimization algorithm (ma-QAOA) to TFD-state preparation and introducing two top-down sequential angle-pruning algorithms. Starting from the optimized initial ma-QAOA circuit, both algorithms sequentially remove Pauli-string evolutions with small optimized angles and reoptimize the remaining parameters after each removal. We apply these algorithms to Gaussian and binary Sachdev--Ye--Kitaev (SYK) models in both dense and sparse cases. We find that ma-QAOA prepares the target TFD states with high fidelity and that sequential small-angle pruning retains high fidelity while reducing the circuit depth, particularly at low temperature. Moreover, using the post-reoptimization cost in sequential small-angle pruning further improves the fidelity. For the binary sparse N=10N=10N=10 SYK model at β=10β=10β=10, 88.8%88.8\%88.8%--92.1%92.1\%92.1% of the nonlocal Pauli-string evolutions are removed while retaining an average fidelity of approximately 95%95\%95%. Finally, we propose extensions of the sequential pruning algorithms toward quantum--classical hybrid implementation.
We analyze a simple and efficient experimental protocol to cool the Sachdev-Ye-Kitaev (SYK) model to low temperatures. The protocol utilizes local couplings between two copies of an SYK model to create a gapped adiabatic path, between a high temperature product state and a low temperature thermofield double state. By smoothly varying the coupling strength between these two limits, one efficiently cools the SYK model. We support these predictions-and demonstrate fast cooling to the low-temperature gravitational regime-via exact numerical solutions to the large-N equations of motion that govern the ground state and dynamical properties of the coupled system. Finally, we present a theoretical framework based upon eigenstate thermalization that provides a microscopic explanation for the efficacy of the cooling protocol; intriguingly, this suggests that the protocol may be applicable for cooling strongly-interacting quantum Hamiltonians more broadly.
A major promise of quantum computers is the controlled preparation of many-body quantum states beyond the reach of efficient classical computation. Among the most important targets are thermal mixed states and their thermofield double (TFD) purifications, which play central roles in quantum many-body physics and quantum gravity. For target systems with a bounded energy spectrum that obey the eigenstate thermalization hypothesis (ETH), we present a parent Hamiltonian built from two copies of the target Hamiltonian and ultra-local couplings between the copies, which we argue is gapped with a ground state that approximates a TFD state of the target Hamiltonian. By adiabatically evolving down from strong coupling, we can thus prepare a high-fidelity TFD state. We study two variants of the parent Hamiltonian using numerical methods in two classes of models: mixed field Ising models in one and two dimensions and non-local "spin Sachdev-Ye-Kitaev'' models. In the simpler variant, the parent Hamiltonian ground state has high overlap with a TFD for system sizes accessible to near-term quantum devices. However, the global overlap decays exponentially with the number of qubits, with a small error per degree of freedom. The second variant introduces an additional penalty term which can be tuned to reduce or remove the decay of the overlap with system size. Together with a general ETH-based analysis, these results suggest a broadly applicable method for TFD preparation that is not limited to particular temperatures or geometric locality.
We study the preparation of thermal states of the dense and sparse Sachdev-Ye-Kitaev (SYK) model using a variational quantum algorithm for 6≤N≤126 \le N \le 126≤N≤12 Majorana fermions over a wide range of temperatures. Utilizing IBM's 127-qubit quantum processor, we perform benchmark computations for the dense SYK model with N=6N = 6N=6, showing good agreement with exact results. The preparation of thermal states of a non-local random Hamiltonian with all-to-all coupling using the simulator and quantum hardware represents a significant step toward future computations of thermal out-of-time order correlators in quantum many-body systems.
Given a class of q-local Hamiltonians, is it possible to find a simple variational state whose energy is a finite fraction of the ground-state energy in the thermodynamic limit?Whereas product states often provide an affirmative answer in the case of bosonic (or qubit) models, we show that Gaussian states fail dramatically in the fermionic case, such as for the Sachdev-Ye-Kitaev (SYK) models.This prompts us to propose a new class of wave functions for SYK models inspired by the variational coupled cluster algorithm.We introduce a static "(0+0)-dimensional" large-N field theory to study the energy, two-point correlators, and entanglement properties of these states.Most importantly, we demonstrate a finite disorder-averaged approximation ratio of r ≈ 0.62 between the variational and ground-state energy of the SYK model for q = 4.Moreover, the variational states provide an exact description of spontaneous symmetry breaking in a related two-flavor SYK model.
The efficient preparation of correlated thermal states, such as the Thermofield Double (TFD) state, is a fundamental prerequisite for simulating quantum gravity models and many-body thermodynamics on quantum processors. In this work, we investigate the ground state preparation of the Two Coupled Sachdev-Ye-Kitaev model, known as the Maldacena-Qi model, which is dual to a traversable wormhole in AdS2AdS_2AdS2​, utilizing feedback-based quantum algorithms. We demonstrate that the standard feedback-based quantum algorithm (FALQON) and its time-rescaled variant (TR-FALQON) face severe kinetic limitations in this system, failing to converge to the highly entangled ground state when initialized in trivial product states. To overcome these barriers, we propose the hybrid ITE-TR-FALQON protocol, which integrates the imaginary-time evolution present in imaginary-time-enhanced FALQON (ITE-FALQON) with the time-rescaling mechanism. Our numerical results indicate that the introduction of non-unitary dynamics is strictly necessary to break symmetry traps and filter out excited states, while time-rescaling drastically accelerates algorithm convergence. The proposed method achieves fidelities close to unity and reproduces the von Neumann and Rényi entropy spectra of the exact TFD state with high precision.