BosonSampling with Lost Photons

BosonSampling is an intermediate model of quantum computation where linear-optical networks are used to solve sampling problems expected to be hard for classical computers. Since these devices are not expected to be universal for quantum computation, it remains an open question of whether any error-correction techniques can be applied to them, and thus it is important to investigate how robust the model is under natural experimental imperfections, such as losses and imperfect control of parameters. Here we investigate the complexity of BosonSampling under photon losses---more specifically, the case where an unknown subset of the photons are randomly lost at the sources. We show that, if $k$ out of $n$ photons are lost, then we cannot sample classically from a distribution that is $1/n^{\Theta(k)}$-close (in total variation distance) to the ideal distribution, unless a $\text{BPP}^{\text{NP}}$ machine can estimate the permanents of Gaussian matrices in $n^{O(k)}$ time. In particular, if $k$ is constant, this implies that simulating lossy BosonSampling is hard for a classical computer, under exactly the same complexity assumption used for the original lossless case.Comment: 12 pages. v2: extended concluding sectio

Measurement-Based Linear Optics

© 2017 American Physical Society. A major challenge in optical quantum processing is implementing large, stable interferometers. We offer a novel approach: virtual, measurement-based interferometers that are programed on the fly solely by the choice of homodyne measurement angles. The effects of finite squeezing are captured as uniform amplitude damping. We compare our proposal to existing (physical) interferometers and consider its performance for BosonSampling, which could demonstrate postclassical computational power in the near future. We prove its efficiency in time and squeezing (energy) in this setting

Boson Sampling is Robust to Small Errors in the Network Matrix

We demonstrate the robustness of BosonSampling to imperfections in the linear optical network that cause a small deviation in the matrix it implements. We show that applying a noisy matrix $\tilde{U}$ that is within $\epsilon$ of the desired matrix $U$ in operator norm leads to an output distribution that is within $\epsilon n$ of the desired distribution in variation distance, where $n$ is the number of photons. This lets us derive a sufficient tolerance each beamsplitters and phaseshifters in the network. This result considers only errors that result from the network encoding a different unitary than desired, and not other sources of noise such as photon loss and partial distinguishability.Comment: 8 page

Complexity Theory and its Applications in Linear Quantum Optics

This thesis is intended in part to summarize and also to contribute to the newest developments in passive linear optics that have resulted, directly or indirectly, from the somewhat shocking discovery in 2010 that the BosonSampling problem is likely hard for a classical computer to simulate. In doing so, I hope to provide a historic context for the original result, as well as an outlook on the future of technology derived from these newer developments. An emphasis is made in each section to provide a broader conceptual framework for understanding the consequences of each result in light of the others. This framework is intended to be comprehensible even without a deep understanding of the topics themselves. The first three chapters focus more closely on the BosonSampling result itself, seeking to understand the computational complexity aspects of passive linear optical networks, and what consequences this may have. Some effort is spent discussing a number of issues inherent in the BosonSampling problem that limit the scope of its applicability, and that are still active topics of research. Finally, we describe two other linear optical settings that inherit the same complexity as BosonSampling. The final chapters focus on how an intuitive understanding of BosonSampling has led to developments in optical metrology and other closely related fields. These developments suggest the exciting possibility that quantum sensors may be viable in the next few years with only marginal improvements in technology. Lastly, some open problems are presented which are intended to lay out a course for future research that would allow for a more complete picture of the scalability of the architecture developed in these chapters.Comment: PhD thesis, 121 pages, 18 figure

Routes Towards Optical Quantum Technology --- New Architectures and Applications

This thesis is based upon the work I have done during my PhD candidature at Macquarie University. In this work we develop quantum technologies that are directed towards realising a quantum computer. Specifically, we have made many theoretical advancements in a type of quantum information processing protocol called BosonSampling. This device efficiently simulates the interaction of quantum particles called bosons, which no classical computer can efficiently simulate. In this thesis we explore quantum random walks, which are the basis of how the bosons in BosonSampling interfere with each other. We explore implementing BosonSampling using the most readily available photon source technology. We invented a completely new architecture which can implement BosonSampling in time rather than space and has since been used to make the worlds largest BosonSampling experiment ever performed. We look at variations to the traditional BosonSampling architecture by considering other quantum states of light. We show a worlds first application inspired by BosonSampling in quantum metrology where measurements may be made more accurately than with any classical method. Lastly, dealing with BosonSampling, we look at reformulating the formalism of BosonSampling using a quantum optics approach. In addition, but not related to BosonSampling, we show a protocol for efficiently generating large-photon Fock states, which are a type of quantum state of light, that are useful for quantum computation. Also, we show a method for generating a specific quantum state of light that is useful for quantum error correction --- an essential component of realising a quantum computer --- by coupling together light and atoms.Comment: PhD Thesi

Marginal probabilities in boson samplers with arbitrary input states

With the recent claim of a quantum advantage demonstration in photonics by Zhong et al, the question of the computation of lower-order approximations of boson sampling with arbitrary quantum states at arbitrary distinguishability has come to the fore. In this work, we present results in this direction, building on the results of Clifford and Clifford. In particular, we show: 1) How to compute marginal detection probabilities (i.e. probabilities of the detection of some but not all photons) for arbitrary quantum states. 2) Using the first result, how to generalize the sampling algorithm of Clifford and Clifford to arbitrary photon distinguishabilities and arbitrary input quantum states. 3) How to incorporate truncations of the quantum interference into a sampling algorithm. 4) A remark considering maximum likelihood verification of the recent photonic quantum advantage experiment

Spoofing cross entropy measure in boson sampling

Cross entropy measure is a widely used benchmarking to demonstrate quantum computational advantage from sampling problems, such as random circuit sampling using superconducting qubits and boson sampling. In this work, we propose a heuristic classical algorithm that generates heavy outcomes of the ideal boson sampling distribution and consequently achieves a large cross entropy. The key idea is that there exist classical samplers that are efficiently simulable and correlate with the ideal boson sampling probability distribution and that the correlation can be used to post-select heavy outcomes of the ideal probability distribution, which essentially leads to a large cross entropy. As a result, our algorithm achieves a large cross entropy score by selectively generating heavy outcomes without simulating ideal boson sampling. We first show that for small-size circuits, the algorithm can even score a better cross entropy than the ideal distribution of boson sampling. We then demonstrate that our method scores a better cross entropy than the recent Gaussian boson sampling experiments when implemented at intermediate, verifiable system sizes. Much like current state-of-the-art experiments, we cannot verify that our spoofer works for quantum advantage size systems. However, we demonstrate our approach works for much larger system sizes in fermion sampling, where we can efficiently compute output probabilities.Comment: 14 pages, 11 figure

Tensor network algorithm for simulating experimental Gaussian boson sampling

Gaussian boson sampling is a promising candidate for showing experimental quantum advantage. While there is evidence that noiseless Gaussian boson sampling is hard to efficiently simulate using a classical computer, the current Gaussian boson sampling experiments inevitably suffer from loss and other noise models. Despite a high photon loss rate and the presence of noise, they are currently claimed to be hard to classically simulate with the best-known classical algorithm. In this work, we present a classical tensor-network algorithm that simulates Gaussian boson sampling and whose complexity can be significantly reduced when the photon loss rate is high. By generalizing the existing thermal-state approximation algorithm of lossy Gaussian boson sampling, the proposed algorithm enables us to achieve increased accuracy as the running time of the algorithm scales, as opposed to the algorithm that samples from the thermal state, which can give only a fixed accuracy. The generalization allows us to assess the computational power of current lossy experiments even though their output state is not believed to be close to a thermal state. We then simulate the largest Gaussian boson sampling implemented in experiments so far. Much like the actual experiments, classically verifying this large-scale simulation is challenging. To do this, we first observe that in our smaller-scale simulations the total variation distance, cross-entropy, and two-point correlation benchmarks all coincide. Based on this observation, we demonstrate for large-scale experiments that our sampler matches the ground-truth two-point and higher-order correlation functions better than the experiment does, exhibiting evidence that our sampler can simulate the ground-truth distribution better than the experiment can.Comment: 20 pages, 10 figure