Brian Coyle | LIP6 - Équipe QI

Probably approximately correct quantum source coding

Tue, 04 Jan 2022 00:00:00 +0000

Information-theoretic lower bounds are often encountered in several branches of computer science, including learning theory and cryptography. In the quantum setting, Holevo’s and Nayak’s bounds give an estimate of the amount of classical information that can be stored in a quantum state. Previous works have shown how to combine information-theoretic tools with a counting argument to lower bound the sample complexity of distribution-free quantum probably approximately correct (PAC) learning. In our work, we establish the notion of Probably Approximately Correct Source Coding and we show two novel applications in quantum learning theory and delegated quantum computation with a purely classical client. In particular, we provide a lower bound of the sample complexity of a quantum learner for arbitrary functions under the Zipf distribution, and we improve the security guarantees of a classically-driven delegation protocol for measurement-based quantum computation (MBQC).

Graph neural network initialisation of quantum approximate optimisation

Sat, 01 Jan 2022 00:00:00 +0000

Approximate combinatorial optimisation has emerged as one of the most promising application areas for quantum computers, particularly those in the near term. In this work, we focus on the quantum approximate optimisation algorithm (QAOA) for solving the Max-Cut problem. Specifically, we address two problems in the QAOA, how to select initial parameters, and how to subsequently train the parameters to find an optimal solution. For the former, we propose graph neural networks (GNNs) as an initialisation routine for the QAOA parameters, adding to the literature on warm-starting techniques. We show the GNN approach generalises across not only graph instances, but also to increasing graph sizes, a feature not available to other warm-starting techniques. For training the QAOA, we test several optimisers for the MaxCut problem. These include quantum aware/agnostic optimisers proposed in literature and we also incorporate machine learning techniques such as reinforcement and meta-learning. With the incorporation of these initialisation and optimisation toolkits, we demonstrate how the QAOA can be trained as an end-to-end differentiable pipeline.

A Continuous Variable Born Machine

Tue, 05 Jan 2021 00:00:00 +0000

Generative Modelling has become a promising use case for near term quantum computers. In particular, due to the fundamentally probabilistic nature of quantum mechanics, quantum computers naturally model and learn probability distributions, perhaps more efficiently than can be achieved classically. The Born machine is an example of such a model, easily implemented on near term quantum computers. However, in its original form, the Born machine only naturally represents discrete distributions. Since probability distributions of a continuous nature are commonplace in the world, it is essential to have a model which can efficiently represent them. Some proposals have been made in the literature to supplement the discrete Born machine with extra features to more easily learn continuous distributions, however, all invariably increase the resources required to some extent. In this work, we present the continuous variable Born machine, built on the alternative architecture of continuous variable quantum computing, which is much more suitable for modelling such distributions in a resource-minimal way. We provide numerical results indicating the models ability to learn both quantum and classical continuous distributions, including in the presence of noise.

Variational Quantum Cloning: Improving Practicality for Quantum Cryptanalysis

Tue, 05 Jan 2021 00:00:00 +0000

Cryptanalysis on standard quantum cryptographic systems generally involves finding optimal adversarial attack strategies on the underlying protocols. The core principle of modelling quantum attacks in many cases reduces to the adversary’s ability to clone unknown quantum states which facilitates the extraction of some meaningful secret information. Explicit optimal attack strategies typically require high computational resources due to large circuit depths or, in many cases, are unknown. In this work, we propose variational quantum cloning (VQC), a quantum machine learning based cryptanalysis algorithm which allows an adversary to obtain optimal (approximate) cloning strategies with short depth quantum circuits, trained using hybrid classical-quantum techniques. The algorithm contains operationally meaningful cost functions with theoretical guarantees, quantum circuit structure learning and gradient descent based optimisation. Our approach enables the end-to-end discovery of hardware efficient quantum circuits to clone specific families of quantum states, which in turn leads to an improvement in cloning fidelites when implemented on quantum hardware: the Rigetti Aspen chip. Finally, we connect these results to quantum cryptographic primitives, in particular quantum coin flipping. We derive attacks on two protocols as examples, based on quantum cloning and facilitated by VQC. As a result, our algorithm can improve near term attacks on these protocols, using approximate quantum cloning as a resource.

Quantum versus Classical Generative Modelling in Finance

Tue, 15 Dec 2020 00:00:00 +0000

Finding a concrete use case for quantum computers in the near term is still an open question, with machine learning typically touted as one of the first fields which will be impacted by quantum technologies. In this work, we investigate and compare the capabilities of quantum versus classical models for the task of generative modelling in machine learning. We use a real world financial dataset consisting of correlated currency pairs and compare two models in their ability to learn the resulting distribution - a restricted Boltzmann machine, and a quantum circuit Born machine. We provide extensive numerical results indicating that the simulated Born machine always at least matches the performance of the Boltzmann machine in this task, and demonstrates superior performance as the model scales. We perform experiments on both simulated and physical quantum chips using the Rigetti forest platform, and also are able to partially train the largest instance to date of a quantum circuit Born machine on quantum hardware. Finally, by studying the entanglement capacity of the training Born machines, we find that entanglement typically plays a role in the problem instances which demonstrate an advantage over the Boltzmann machine.

The Born supremacy: quantum advantage and training of an Ising Born machine

Wed, 08 Jul 2020 00:00:00 +0000

The search for an application of near-term quantum devices is widespread. Quantum machine learning is touted as a potential utilisation of such devices, particularly those out of reach of the simulation capabilities of classical computers. In this work, we study such an application in generative modelling, focussing on a class of quantum circuits known as Born machines. Specifically, we define a subset of this class based on Ising Hamiltonians and show that the circuits encountered during gradient-based training cannot be efficiently sampled from classically up to multiplicative error in the worst case. Our gradient-based training methods use cost functions known as the Sinkhorn divergence and the Stein discrepancy, which have not previously been used in the gradientbased training of quantum circuits, and we also introduce quantum kernels to generative modelling. We show that these methods outperform the previous standard method, which used maximum mean discrepancy (MMD) as a cost function, and achieve this with minimal overhead. Finally, we discuss the ability of the model to learn hard distributions and provide formal definitions for ‘quantum learning supremacy’. We also exemplify the work of this paper by using generative modelling to perform quantum circuit compilation.

Certified Randomness From Steering Using Sequential Measurements

Sun, 01 Dec 2019 00:00:00 +0000

The generation of certifiable randomness is one of the most promising applications of quantum technologies. Furthermore, the intrinsic non-locality of quantum correlations allow us to certify randomness in a device-independent way, i.e. one need not make assumptions about the devices used. Due to the work of Curchod et. al., a single entangled two-qubit pure state can be used to produce arbitrary amounts of certified randomness. However, the obtaining of this randomness is experimentally challenging as it requires a large number of measurements, both projective and general. Motivated by these difficulties in the device-independent setting, we instead consider the scenario of one-sided device independence where certain devices are trusted, and others not; a scenario motivated by asymmetric experimental set-ups such as ion-photon networks. We show how certain aspects of previous work can be adapted to this scenario and provide theoretical bounds on the amount of randomness which can be certified. Furthermore, we give a protocol for unbounded randomness certification in this scenario, and provide numerical results demonstrating the protocol in the ideal case. Finally, we numerically test the possibility of implementing this scheme on near-term quantum technologies, by considering the performance of the protocol on several physical platforms.

The Born Supremacy: Quantum Advantage and Training of an Ising Born Machine

Tue, 25 Jun 2019 00:00:00 +0000

The search for an application of near-term quantum devices is widespread. Quantum Machine Learning is touted as a potential utilisation of such devices, particularly those which are out of the reach of the simulation capabilities of classical computers. In this work, we propose a generative Quantum Machine Learning Model, called the Ising Born Machine (IBM), which we show cannot, in the worst case, and up to suitable notions of error, be simulated efficiently by a classical device. We also show this holds for all the circuit families encountered during training. In particular, we explore quantum circuit learning using non-universal circuits derived from Ising Model Hamiltonians, which are implementable on near term quantum devices. We propose two novel training methods for the IBM by utilising the Stein Discrepancy and the Sinkhorn Divergence cost functions. We show numerically, both using a simulator within Rigetti’s Forest platform and on the Aspen-1 16Q chip, that the cost functions we suggest outperform the more commonly used Maximum Mean Discrepancy (MMD) for differentiable training. We also propose an improvement to the MMD by proposing a novel utilisation of quantum kernels which we demonstrate provides improvements over its classical counterpart. We discuss the potential of these methods to learn hard' quantum distributions, a feat which would demonstrate the advantage of quantum over classical computers, and provide the first formal definitions for what we call Quantum Learning Supremacy’. Finally, we propose a novel view on the area of quantum circuit compilation by using the IBM to `mimic’ target quantum circuits using classical output data only.

One-Sided Device-Independent Certification of Unbounded Random Numbers

Mon, 02 Jul 2018 00:00:00 +0000

The intrinsic non-locality of correlations in Quantum Mechanics allow us to certify the behaviour of a quantum mechanism in a device independent way. In particular, we present a new protocol that allows an unbounded amount of randomness to be certified as being legitimately the consequence of a measurement on a quantum state. By using a sequence of non-projective measurements on single state, we show a more robust method to certify unbounded randomness than the protocol of [5], by moving to a one-sided device independent scenario. This protocol also does not assume any specific behaviour of the adversary trying to fool the participants in the protocol, which is an advantage over previous steering based protocols. We present numerical results which confirm the optimal functioning of this protocol in the ideal case. Furthermore, we also study an experimental scenario to determine the feasibility of the protocol in a realistic implementation. The effect of depolarizing noise is examined, by studying a potential state produced by a networked system of ion traps.