Noise-Assisted Variational Quantum Thermalization

• URL: http://arxiv.org/abs/2111.03935v1
• Date: Sat, 6 Nov 2021 18:24:01 GMT
• Title: Noise-Assisted Variational Quantum Thermalization
• Authors: Jonathan Foldager, Arthur Pesah, Lars Kai Hansen
• Abstract summary: Variational circuits have been proposed for simulating thermal states on quantum computers. We propose a new algorithm for thermal state preparation by exploiting the noise of quantum circuits. We show that the ability for our algorithm to learn the thermal state strongly depends on the temperature.
• Score: 4.118741675173778
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Preparing thermal states on a quantum computer can have a variety of applications, from simulating many-body quantum systems to training machine learning models. Variational circuits have been proposed for this task on near-term quantum computers, but several challenges remain, such as finding a scalable cost-function, avoiding the need of purification, and mitigating noise effects. We propose a new algorithm for thermal state preparation that tackles those three challenges by exploiting the noise of quantum circuits. We consider a variational architecture containing a depolarizing channel after each unitary layer, with the ability to directly control the level of noise. We derive a closed-form approximation for the free-energy of such circuit and use it as a cost function for our variational algorithm. By evaluating our method on a variety of Hamiltonians and system sizes, we find several systems for which the thermal state can be approximated with a high fidelity. However, we also show that the ability for our algorithm to learn the thermal state strongly depends on the temperature: while a high fidelity can be obtained for high and low temperatures, we identify a specific range for which the problem becomes more challenging. We hope that this first study on noise-assisted thermal state preparation will inspire future research on exploiting noise in variational algorithms.

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