Quantum-inspired estimators for F_alpha(P) and F_alpha(rho) achieve optimal sample complexity n ~ alpha and minimax MSE rate alpha/n, improving prior O(alpha^2) bounds.
A Variational Quantum Algorithm for Preparing Quantum Gibbs States
3 Pith papers cite this work. Polarity classification is still indexing.
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abstract
Preparation of Gibbs distributions is an important task for quantum computation. It is a necessary first step in some types of quantum simulations and further is essential for quantum algorithms such as quantum Boltzmann training. Despite this, most methods for preparing thermal states are impractical to implement on near-term quantum computers because of the memory overheads required. Here we present a variational approach to preparing Gibbs states that is based on minimizing the free energy of a quantum system. The key insight that makes this practical is the use of Fourier series approximations to the logarithm that allows the entropy component of the free-energy to be estimated through a sequence of simpler measurements that can be combined together using classical post processing. We further show that this approach is efficient for generating high-temperature Gibbs states, within constant error, if the initial guess for the variational parameters for the programmable quantum circuit are sufficiently close to a global optima. Finally, we examine the procedure numerically and show the viability of our approach for five-qubit Hamiltonians using Trotterized adiabatic state preparation as an ansatz.
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quant-ph 3representative citing papers
Quantum Gibbs samplers thermalize to Gibbs states in polynomial time at high temperatures for Lieb-Robinson bounded Hamiltonians and are BQP-complete at low temperatures via circuit-to-Hamiltonian reductions.
The work presents a quantum computing framework for semiconductor optical spectroscopy that matches classical results for GaAs in the noiseless limit and notes that NISQ noise acts as extra scattering.
citing papers explorer
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Towards Minimax Estimation of High-Order Functionals by Quantum Arguments
Quantum-inspired estimators for F_alpha(P) and F_alpha(rho) achieve optimal sample complexity n ~ alpha and minimax MSE rate alpha/n, improving prior O(alpha^2) bounds.
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Efficient thermalization and universal quantum computing with quantum Gibbs samplers
Quantum Gibbs samplers thermalize to Gibbs states in polynomial time at high temperatures for Lieb-Robinson bounded Hamiltonians and are BQP-complete at low temperatures via circuit-to-Hamiltonian reductions.
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Quantum simulations of ultrafast optical spectroscopy of semiconductors on digital quantum computers in the semi-classical approximation
The work presents a quantum computing framework for semiconductor optical spectroscopy that matches classical results for GaAs in the noiseless limit and notes that NISQ noise acts as extra scattering.