Quantum computing with classical bits

Wetterich, C.

doi:10.1016/j.nuclphysb.2019.114776

Cited by 10 publications

(9 citation statements)

References 64 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…B showing a gain through neuromorphic sampling already at moderate system sizes. Given the efficient learnability [28] and representability of important classes of quantum states [29][30][31], and the availability of sampling schemes for neuromorphic devices [32,33], we thus expect favorable scaling properties for our approach. Thus our work opens up a path towards applications of neuromorphic hardware in quantum many-body physics.…”

Section: Discussionmentioning

confidence: 99%

Spiking neuromorphic chip learns entangled quantum states

et al. 2022

View full text Add to dashboard Cite

The approximation of quantum states with artificial neural networks has gained a lot of attention during the last years. Meanwhile, analog neuromorphic chips, inspired by structural and dynamical properties of the biological brain, show a high energy efficiency in running artificial neural-network architectures for the profit of generative applications. This encourages employing such hardware systems as platforms for simulations of quantum systems. Here we report on the realization of a prototype using the latest spike-based BrainScaleS hardware allowing us to represent few-qubit maximally entangled quantum states with high fidelities. Bell correlations of pure and mixed two-qubit states are well captured by the analog hardware, demonstrating an important building block for simulating quantum systems with spiking neuromorphic chips.

show abstract

Section: Discussionmentioning

confidence: 99%

Spiking neuromorphic chip learns entangled quantum states

et al. 2022

View full text Add to dashboard Cite

show abstract

“…Probabilistic cellular automata for bit systems are synonymous to a type of probabilistic classical computing for which the computational steps of bit-manipulations are deterministic, while initial conditions are probabilistic. Since already these simple forms of probabilistic computing show quantum features, one may ask if forms of quantum computing could be performed by classical probabilistic systems, as static memory materials, artificial neural networks or neuromorphic computing [43][44][45][46].…”

Section: Discussionmentioning

confidence: 99%

Fermionic quantum field theories as probabilistic cellular automata

Wetterich

2021

Preprint

Self Cite

View full text Add to dashboard Cite

A class of fermionic quantum field theories with interactions can be described equivalently as probabilistic cellular automata, namely cellular automata with a probability distribution for the initial states. Probabilistic cellular automata on a one-dimensional lattice are equivalent to twodimensional quantum field theories for fermions. They can be viewed as generalized Ising models on a square lattice and therefore as classical statistical systems. As quantum field theories they are quantum systems. Thus quantum mechanics emerges from classical statistics. As an explicit example for an interacting fermionic quantum field theory we describe a type of discretized Thirring model as a cellular automaton. The updating rule of the automaton is encoded in the step evolution operator that can be expressed in terms of fermionic annihilation and creation operators. The complex structure of quantum mechanics is associated to particle -hole transformations. The continuum limit exhibits Lorentz symmetry. We exploit the equivalence to quantum field theory in order to show how quantum concepts as wave functions, density matrix, non-commuting operators for observables and similarity transformations are convenient and useful concepts for the description of probabilistic cellular automata.

show abstract

“…One key advantage of this neuromorphic system as compared with simulated generative models is that scaling to larger network sizes does not increase the time needed to collect a desired number of samples. Given the efficient learnability [28] and representability of quantum states [29][30][31], as well as sampling schemes for neuromorphic devices [32,33], we thus expect favorable scaling properties for our approach. Thus our work opens up a path towards applications of neuromorphic hardware in quantum many-body physics.…”

Section: Discussion and Outlookmentioning

confidence: 99%

Spiking neuromorphic chip learns entangled quantum states

Czischek,

Baumbach,

Billaudelle

et al. 2020

Preprint

View full text Add to dashboard Cite

Neuromorphic systems are designed to emulate certain structural and dynamical properties of biological neuronal networks, with the aim of inheriting the brain's functional performance and energy efficiency in artificial-intelligence applications [1,2]. Among the platforms existing today, the spike-based BrainScaleS system stands out by realizing fast analog dynamics which can boost computationally expensive tasks [3]. Here we use the latest BrainScaleS generation [4] for the algorithm-free simulation of quantum systems, thereby opening up an entirely new application space for these devices. This requires an appropriate spike-based representation of quantum states and an associated training method for imprinting a desired target state onto the network. We employ a representation of quantum states using probability distributions [5,6], enabling the use of a Bayesian sampling framework for spiking neurons [7]. For training, we developed a Hebbian learning scheme that explicitly exploits the inherent speed of the substrate, which enables us to realize a variety of network topologies. We encoded maximally entangled states of up to four qubits and observed fidelities that imply genuine N -partite entanglement. In particular, the encoding of entangled pure and mixed two-qubit states reaches a quality that allows the observation of Bell correlations, thus demonstrating that non-classical features of quantum systems can be captured by spiking neural dynamics. Our work establishes an intriguing connection between quantum systems and classical spiking networks, and demonstrates the feasibility of simulating quantum systems with neuromorphic hardware.

show abstract

Quantum computing with classical bits

Cited by 10 publications

References 64 publications

Spiking neuromorphic chip learns entangled quantum states

Spiking neuromorphic chip learns entangled quantum states

Fermionic quantum field theories as probabilistic cellular automata

Spiking neuromorphic chip learns entangled quantum states

Contact Info

Product

Resources

About