Sampling from a quantum distribution can be exponentially hard for classical computers and yet could be performed efficiently by a noisy intermediate-scale quantum device. A prime example of a distribution that is hard to sample is given by the output states of a linear interferometer traversed by N identical boson particles. Here, we propose a scheme to implement such a boson-sampling machine with ultracold atoms in a polarization-synthesized optical lattice. We experimentally demonstrate the basic building block of such a machine by revealing the Hong-Ou-Mandel interference of two bosonic atoms in a four-mode interferometer. To estimate the sampling rate for large N , we develop a theoretical model based on a master equation that accounts for particle losses, but does not include technical errors. Our results show that atomic samplers have the potential to achieve a quantum advantage over today's best supercomputers with N ≳ 40 . Published by the American Physical Society 2024
Sampling from a quantum distribution can be exponentially hard for classical computers and yet could be performed efficiently by a noisy intermediate-scale quantum device. A prime example of a distribution that is hard to sample is given by the output states of a linear interferometer traversed by N identical boson particles. Here, we propose a scheme to implement such a boson-sampling machine with ultracold atoms in a polarization-synthesized optical lattice. We experimentally demonstrate the basic building block of such a machine by revealing the Hong-Ou-Mandel interference of two bosonic atoms in a four-mode interferometer. To estimate the sampling rate for large N , we develop a theoretical model based on a master equation that accounts for particle losses, but does not include technical errors. Our results show that atomic samplers have the potential to achieve a quantum advantage over today's best supercomputers with N ≳ 40 . Published by the American Physical Society 2024
Abstract A boson sampler implements a restricted model of quantum computing. It is defined by the ability to sample from the distribution resulting from the interference of identical bosons propagating ac- cording to programmable, non-interacting dynamics [1]. Here, we demonstrate a new combination of tools for implementing boson sampling using ultracold atoms in a two-dimensional, tunnel-coupled optical lattice. These tools include fast and programmable preparation of large ensembles of nearly identical bosonic atoms (99.5+0.5 −1.6% indistinguishability) by means of rearrangement with optical tweezers and high-fidelity optical cooling, propagation for variable evolution time in the lattice with low loss (5.0(2) %, independent of evolution time), and high fidelity detection of the atom positions after their evolution (typically 99.8(1) %). With this system, we study specific instances of boson sampling involving up to 180 atoms distributed among ∼ 1000 sites in the lattice. Direct verifi- cation of a given boson sampling distribution is not feasible in this regime. Instead, we introduce and perform targeted tests to determine the indistinguishability of the prepared atoms, to char- acterize the applied family of single particle unitaries, and to observe expected bunching features due to interference for a large range of atom numbers. When extended to interacting systems, our work demonstrates the core capabilities required to directly assemble ground and excited states in simulations of various Hubbard models [2, 3].
Abstract Boson Sampling is a task that is conjectured to be computationally hard for a classical computer, but which can be efficiently solved by linear-optical interferometers with Fock state inputs. Significant advances have been reported in the last few years, with demonstrations of small- and medium-scale devices, as well as implementations of variants such as Gaussian Boson Sampling. Besides the relevance of this class of computational models in the quest for unambiguous experimental demonstrations of quantum advantage, recent results have also proposed the first applications for hybrid quantum computing. Here, we introduce the adoption of non-linear photon–photon interactions in the Boson Sampling framework, and analyze the enhancement in complexity via an explicit linear-optical simulation scheme. By extending the computational expressivity of Boson Sampling, the introduction of non-linearities promises to disclose novel functionalities for this class of quantum devices. Hence, our results are expected to lead to new applications of near-term, restricted photonic quantum computers.
Experimental demonstration of the quantum advantage over classical simulations with Boson Sampling is currently under intensive investigation. There seems to be a scalability issue to the necessary number of bosons on the linear optical platforms and the experiments, such as the recent Boson Sampling with 20 photons on 60-port interferometer by H. Wang et al., Phys. Rev. Lett.123 (2019) 250503, are usually carried out on a small interferometer, much smaller than the size necessary for the no-collision regime. Before demonstration of quantum advantage, it is urgent to estimate exactly how the classical computations necessary for sampling from the output distribution of Boson Sampling are reduced when a smaller-size interferometer is used. This work supplies such a result, valid with arbitrarily close to 1 probability, which reduces in the no-collision regime to the previous estimate by Clifford and Clifford. One of the results with immediate application to current experiments with Boson Sampling is that classically sampling from the interference of [Formula: see text] single bosons on an [Formula: see text]-port interferometer is at least as hard as that with [Formula: see text] single bosons in the no-collision regime, i.e. on a much larger interferometer with at least [Formula: see text] ports.
Computers based quantum logic are believed to solve problems faster and more efficiently than computers based on classical boolean logic. However, a large-scale universal quantum computer with error correction may not be realized in near future. But we can ask the question: can we devise a specific problem that a quantum device can solve faster than current state of the art super computers? One such problem is the so called "Boson Sampling" problem introduced by Aaronson and Arkhipov. The problem is to generate random numbers according to same distribution as the output number configurations of photons in linear optics. It was shown that this is a very hard task for classical computers, but can be realized using quantum devices. However, realizing this in photonic systems is proving to be harder than ever because of various challenges in preparation and measurements. We propose and analyze an alternate platform to implement this problem, ultra-cold atoms trapped in one-dimensional optical lattices.
Boson sampling is a computational problem that has recently been proposed as a candidate to obtain an unequivocal quantum computational advantage. The problem consists in sampling from the output distribution of indistinguishable bosons in a linear interferometer. There is strong evidence that such an experiment is hard to classically simulate, but it is naturally solved by dedicated photonic quantum hardware, comprising single photons, linear evolution, and photodetection. This prospect has stimulated much effort resulting in the experimental implementation of progressively larger devices. We review recent advances in photonic boson sampling, describing both the technological improvements achieved and the future challenges. We also discuss recent proposals and implementations of variants of the original problem, theoretical issues occurring when imperfections are considered, and advances in the development of suitable techniques for validation of boson sampling experiments. We conclude by discussing the future application of photonic boson sampling devices beyond the original theoretical scope.
The implementation of large-scale universal quantum computation represents a challenging and ambitious task on the road to quantum processing of information. In recent years, an intermediate approach has been pursued to demonstrate quantum computational advantage via non-universal computational models. A relevant example for photonic platforms has been provided by the Boson Sampling paradigm and its variants, which are known to be computationally hard while requiring at the same time only the manipulation of the generated photonic resources via linear optics and detection. Beside quantum computational advantage demonstrations, a promising direction towards possibly useful applications can be found in the field of quantum machine learning, considering the currently almost unexplored intermediate scenario between non-adaptive linear optics and universal photonic quantum computation. Here, we report the experimental implementation of quantum machine learning protocols by adding adaptivity via post-selection to a Boson Sampling platform based on universal programmable photonic circuits fabricated via femtosecond laser writing. Our experimental results demonstrate that Adaptive Boson Sampling is a viable route towards dimension-enhanced quantum machine learning with linear optical devices. Extending quantum photonics’ capabilities from simple linear-optics-based schemes to universal quantum computing presents several challenges, but intermediate regimes with some degree of adaptivity might already bring practical advantages. Here, the authors experimentally emulate an adaptive Boson Sampling scheme using post-selection, and apply it to a data classification task.