arXiv subjects

Kamil Brádler

Publications and source records attributed to Kamil Brádler.

13 recordsLinked to original sources

arXiv2023

Flexible entangled state generation in linear optics

Fault-tolerant quantum computation can be achieved by creating constant-sized, entangled resource states and performing entangling measurements on subsets of their qubits. Linear optical quantum computers can be designed based on this approach, even though entangling operations at the qubit level are non-deterministic in this platform. Probabilistic generation and measurement of entangled states must be pushed beyond the required threshold by some combination of scheme optimisation, introduction of redundancy and auxiliary state assistance. We report progress in each of these areas. We explore multi-qubit fusion measurements on dual-rail photonic qubits and their role in measurement-based resource state generation, showing that it is possible to boost the success probability of photonic GHZ state analysers with single photon auxiliary states. By incorporating generators of basic entangled "seed" states, we provide a method that simplifies the process of designing and optimising generators of complex, encoded resource states by establishing links to ZX diagrams.

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arXiv2023

High photon-loss threshold quantum computing using GHZ-state measurements

We propose fault-tolerant architectures based on performing projective measurements in the Greenberger-Horne-Zeilinger (GHZ) basis on constant-sized, entangled resource states. We present linear-optical constructions of the architectures, where the GHZ-state measurements are encoded to suppress the errors induced by photon loss and the probabilistic nature of linear optics. Simulations of our constructions demonstrate high single-photon loss thresholds compared to the state-of-the-art linear-optical architecture realized with encoded two-qubit fusion measurements performed on constant-sized resource states. We believe this result shows a resource-efficient path to achieving photonic fault-tolerant quantum computing.

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arXiv2021

Error mitigation on a near-term quantum photonic device

Photon loss is destructive to the performance of quantum photonic devices and therefore suppressing the effects of photon loss is paramount to photonic quantum technologies. We present two schemes to mitigate the effects of photon loss for a Gaussian Boson Sampling device, in particular, to improve the estimation of the sampling probabilities. Instead of using error correction codes which are expensive in terms of their hardware resource overhead, our schemes require only a small amount of hardware modifications or even no modification. Our loss-suppression techniques rely either on collecting additional measurement data or on classical post-processing once the measurement data is obtained. We show that with a moderate cost of classical post processing, the effects of photon loss can be significantly suppressed for a certain amount of loss. The proposed schemes are thus a key enabler for applications of near-term photonic quantum devices.

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arXiv2020

Efficient sampling from shallow Gaussian quantum-optical circuits with local interactions

We prove that a classical computer can efficiently sample from the photon-number probability distribution of a Gaussian state prepared by using an optical circuit that is shallow and local. Our work generalizes previous known results for qubits to the continuous-variable domain. The key to our proof is the observation that the adjacency matrices characterizing the Gaussian states generated by shallow and local circuits have small bandwidth. To exploit this structure, we devise fast algorithms to calculate loop hafnians of banded matrices. Since sampling from deep optical circuits with exponential-scaling photon loss is classically simulable, our results pose a challenge to the feasibility of demonstrating quantum supremacy on photonic platforms with local interactions.

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arXiv2020

Quantum precision of beam pointing

We consider estimating a small transverse displacement of an optical beam over a line-of-sight propagation path: a problem that has numerous important applications ranging from establishing a lasercom link, single-molecule tracking, guided munition, to atomic force microscopy. We establish the ultimate quantum limit of the accuracy of sensing a beam displacement, and quantify the classical-quantum gap. Further, using normal-mode decomposition of the Fresnel propagation kernel, and insights from recent work on entanglement-assisted sensing, we find a near-term realizable multi-spatio-temporal-mode continuous-variable entangled-state probe and a receiver design, which attains the quantum precision limit. We find a Heisenberg-limited sensitivity enhancement in terms of the number of entangled temporal modes, and a curious super-Heisenberg quantum enhanced scaling in terms of the number of entangled spatial modes permitted by the diffraction-limited beam propagation geometry.

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arXiv2019

A quantum hardware-induced graph kernel based on Gaussian Boson Sampling

A device called a 'Gaussian Boson Sampler' has initially been proposed as a near-term demonstration of classically intractable quantum computation. As recently shown, it can also be used to decide whether two graphs are isomorphic. Based on these results we construct a feature map and graph similarity measure or 'graph kernel' using samples from the device. We show that the kernel performs well compared to standard graph kernels on typical benchmark datasets, and provide a theoretical motivation for this success, linking the distribution of a Gaussian Boson Sampler to the number of matchings in subgraphs. Our results contribute to a new way of thinking about kernels as a (quantum) hardware-efficient feature mapping, and lead to an interesting application for near-term quantum computing.

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arXiv2019

A Quantum Approximate Optimization Algorithm for continuous problems

We introduce a quantum approximate optimization algorithm (QAOA) for continuous optimization. The algorithm is based on the dynamics of a quantum system moving in an energy potential which encodes the objective function. By approximating the dynamics at finite time steps, the algorithm can be expressed as alternating evolution under two non-commuting Hamiltonians. We show that each step of the algorithm updates the wavefunction in the direction of its local gradient, with an additional momentum-dependent displacement. For initial states in a superposition over many points, this method can therefore be interpreted as a coherent version of gradient descent, i.e., 'gradient descent in superposition.' This approach can be used for both constrained and unconstrained optimization. In terms of computational complexity, we show how variants of the algorithm can recover continuous-variable Grover search, and how a single iteration can replicate continuous-variable instantaneous quantum polynomial circuits. We also discuss how the algorithm can be adapted to solve discrete optimization problems. Finally, we test the algorithm through numerical simulation in optimizing the Styblinski-Tang function.

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arXiv2018

Linear multiport photonic interferometers: loss analysis of temporally-encoded architectures

Implementing spatially-encoded universal linear multiport interferometers on integrated photonic platforms with high controllability becomes increasingly difficult as the number of modes increases. In this work, we consider an architecture in which temporally-encoded modes are injected into a chain of reconfigurable beamsplitters and delay loops, and provide a detailed loss analysis for this and other comparable architectures. This analysis also yields a straightforward and explicit description of the control sequence needed for implementing an arbitrary temporally-encoded transformation. We consider possible physical implementations of these architectures and compare their performance. We conclude that implementing a chain of reconfigurable beamsplitters and delay loops on an integrated lithum niobate platform could outperform comparable architectures in the near future.

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arXiv2018

Machine learning method for state preparation and gate synthesis on photonic quantum computers

We show how techniques from machine learning and optimization can be used to find circuits of photonic quantum computers that perform a desired transformation between input and output states. In the simplest case of a single input state, our method discovers circuits for preparing a desired quantum state. In the more general case of several input and output relations, our method obtains circuits that reproduce the action of a target unitary transformation. We use a continuous-variable quantum neural network as the circuit architecture. The network is composed of several layers of optical gates with variable parameters that are optimized by applying automatic differentiation using the TensorFlow backend of the Strawberry Fields photonic quantum computer simulator. We demonstrate the power and versatility of our methods by learning how to use short-depth circuits to synthesize single photons, Gottesman-Kitaev-Preskill states, NOON states, cubic phase gates, random unitaries, cross-Kerr interactions, as well as several other states and gates. We routinely obtain high fidelities above 99\% using short-depth circuits, typically consisting of a few hundred gates. The circuits are obtained automatically by simply specifying the target state or gate and running the optimization algorithm.

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arXiv2018

Implementing quantum algorithms on temporal photonic cluster states

Implementing quantum algorithms is essential for quantum computation. We study the implementation of three quantum algorithms by performing homodyne measurements on a two-dimensional temporal continuous-variable cluster state. We first review the generation of temporal cluster states and the implementation of gates using the measurement-based model. Alongside this we discuss methods to introduce non-Gaussianity into the cluster states. The first algorithm we consider is Gaussian Boson Sampling in which only Gaussian unitaries need to be implemented. Taking into account the fact that input states are also Gaussian, the errors due to the effect of finite squeezing can be corrected, provided a moderate amount of online squeezing is available. This helps to construct a large Gaussian Boson Sampling machine. The second algorithm is the continuous-variable Instantaneous Quantum Polynomial circuit in which one needs to implement non-Gaussian gates, such as the cubic phase gate. We discuss several methods of implementing the cubic phase gate and fit them into the temporal cluster state architecture. The third algorithm is the continuous-variable version of Grover's search algorithm, the main challenge of which is the implementation of the inversion operator. We propose a method to implement the inversion operator by injecting a resource state into a teleportation circuit. The resource state is simulated using the Strawberry Fields quantum software package.

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arXiv2018

Correcting finite squeezing errors in continuous-variable cluster states

We introduce an efficient scheme to correct errors due to the finite squeezing effects in continuous-variable cluster states. Specifically, we consider the typical situation where the class of algorithms consists of input states that are known. By using the knowledge of the input states, we can diagnose exactly what errors have occurred and correct them in the context of temporal continuous-variable cluster states. We illustrate the error correction scheme for single-mode and two-mode unitaries implemented by spatial continuous-variable cluster states. We show that there is no resource advantage to error correcting multimode unitaries implemented by spatial cluster states. However, the generalization to multimode unitaries implemented by temporal continuous-variable cluster states shows significant practical advantages since it costs only a finite number of optical elements (squeezer, beam splitter, etc).

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arXiv2017

Gaussian Boson Sampling for perfect matchings of arbitrary graphs

A famously hard graph problem with a broad range of applications is computing the number of perfect matchings, that is the number of unique and complete pairings of the vertices of a graph. We propose a method to estimate the number of perfect matchings of undirected graphs based on the relation between Gaussian Boson Sampling and graph theory. The probability of measuring zero or one photons in each output mode is directly related to the hafnian of the adjacency matrix, and thus to the number of perfect matchings of a graph. We present encodings of the adjacency matrix of a graph into a Gaussian state and show strategies to boost the sampling success probability. With our method, a Gaussian Boson Sampling device can be used to estimate the number of perfect matchings significantly faster and with lower energy consumption compared to a classical computer.

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arXiv2016

One-shot decoupling and Page curves from a dynamical model for black hole evaporation

One-shot decoupling is a powerful primitive in quantum information theory and was hypothesized to play a role in the black hole information paradox. We study black hole dynamics modeled by a trilinear Hamiltonian whose semiclassical limit gives rise to Hawking radiation. An explicit numerical calculation of the discretized path integral of the S-matrix shows that decoupling is exact in the continuous limit, implying that quantum information is perfectly transferred from the black hole to radiation. A striking consequence of decoupling is the emergence of an output radiation entropy profile that follows Page's prediction. We argue that information transfer and the emergence of Page curves is a robust feature of any multi-linear interaction Hamiltonian with a bounded spectrum.

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