Showing papers on "Quantum sort published in 2021"

Quantum agents in the Gym: a variational quantum algorithm for deep Q-learning.

Abstract: Quantum machine learning (QML) has been identified as one of the key fields that could reap advantages from near-term quantum devices, next to optimization and quantum chemistry Research in this area has focused primarily on variational quantum algorithms (VQAs), and several proposals to enhance supervised, unsupervised and reinforcement learning (RL) algorithms with VQAs have been put forward Out of the three, RL is the least studied and it is still an open question whether VQAs can be competitive with state-of-the-art classical algorithms based on neural networks (NNs) even on simple benchmark tasks In this work, we introduce a training method for parametrized quantum circuits (PQCs) to solve RL tasks for discrete and continuous state spaces based on the deep Q-learning algorithm To evaluate our model, we use it to solve two benchmark environments from the OpenAI Gym, Frozen Lake and Cart Pole We provide insight into why the performance of a VQA-based Q-learning algorithm crucially depends on the observables of the quantum model and show how to choose suitable observables based on the RL task at hand We compare the performance of our model to that of a NN for agents that need similar time to convergence, and find that our quantum model needs approximately one-third of the parameters of the classical model to solve the Cart Pole environment in a similar number of episodes on average We also show how recent separation results between classical and quantum agents for policy gradient RL can be adapted to quantum Q-learning agents, which yields a quantum speed-up for Q-learning This work paves the way towards new ideas on how a quantum advantage may be obtained for real-world problems in the future

Near Term Algorithms for Linear Systems of Equations

Abstract: Finding solutions to systems of linear equations is a common prob\-lem in many areas of science and engineering, with much potential for a speedup on quantum devices. While the Harrow-Hassidim-Lloyd (HHL) quantum algorithm yields up to an exponential speed-up over classical algorithms in some cases, it requires a fault-tolerant quantum computer, which is unlikely to be available in the near term. Thus, attention has turned to the investigation of quantum algorithms for noisy intermediate-scale quantum (NISQ) devices where several near-term approaches to solving systems of linear equations have been proposed. This paper focuses on the Variational Quantum Linear Solvers (VQLS), and other closely related methods. This paper makes several contributions that include: the first application of the Evolutionary Ansatz to the VQLS (EAVQLS), the first implementation of the Logical Ansatz VQLS (LAVQLS), based on the Classical Combination of Quantum States (CQS) method, the first proof of principle demonstration of the CQS method on real quantum hardware and a method for the implementation of the Adiabatic Ansatz (AAVQLS). These approaches are implemented and contrasted.

Provable Advantage in Quantum Phase Learning via Quantum Kernel Alphatron

Abstract: Can we use a quantum computer to speed up classical machine learning in solving problems of practical significance? Here, we study this open question focusing on the quantum phase learning problem, an important task in many-body quantum physics. We prove that, under widely believed complexity theory assumptions, quantum phase learning problem cannot be efficiently solved by machine learning algorithms using classical resources and classical data. Whereas using quantum data, we theoretically prove the universality of quantum kernel Alphatron in efficiently predicting quantum phases, indicating quantum advantages in this learning problem. We numerically benchmark the algorithm for a variety of problems,including recognizing symmetry-protected topological phases and symmetry-broken phases. Our results highlight the capability of quantum machine learning in efficient prediction of quantum phases.