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Recent Advances in Quantum Computing and Technology (ReAQCT) is the first in a new series of scientific conferences with the aim to bring together scientists and industry experts working in the field of quantum computing and quantum technology.
Conference website: https://www.reaqct.org/
Materials science will be one of the first domains that benefits from quantum computers. We present industrially important use cases involving new materials, where the otherwise very successful approximative methods with conventional computers cease to provide adequate results and quantum computers will be required. Furthermore, we will discuss algorithms to perform materials simulation with quantum computers and report first results from running those on real quantum computers.
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We will look at the basics of quantum error correction, starting from the repetition code and then focusing on surface codes. Finally we’ll consider the outlook towards high-rate LDPC codes.
Machine learning is rapidly proving indispensable in tuning and characterising quantum devices. By facilitating the exploration of high-dimensional and complex parameter spaces, these algorithms not only allow for the identification of optimal operational conditions but also surpass human experts in the characterisation of different operational regimes. I will present the first fully autonomous tuning of a spin qubit. This is a major advancement for scaling semiconductor quantum technologies and understanding variability in nominally identical devices. My discussion will also cover the versatility of machine learning algorithms across various semiconductor devices, emphasising their role in the comparative analysis of quantum device architectures. I will demonstrate how a physics-informed machine learning approach can reveal the disorder potential in a quantum dot device, providing insights into device characteristics that were previously inaccessible. I will conclude by discussing how machine learning can bridge the gap between quantum device simulation and reality, catalysing rapid advancements in quantum technology.
Photonic quantum computing has recently emerged as a promising candidate for fault-tolerant quantum computing by photonic qubits. These protocols make use of nondeterministic gates, enabling universal quantum computation.
However, the suggested solutions heavily use particle number resolving detectors (PNRDs), which are experimentally hard to realize and are usually biased in practice. We investigate the possibility of suppressing such errors caused by such photodetector imperfections by adjusting the optimal beamsplitter and phaseshifter angles in the interferometer corresponding to nondeterministic gates. Moreover, we devise an optimization method for determining the adjusted angles, which may achieve higher output state fidelities while controlling the success probabilities of the nondeterministic gates.
In this paper, we study two well-characterized continuous variable detections, homodyne and heterodyne, and then compare their performance in phase keying or amplitude modulation schemes where indistinguishable states are being measured. We compare their performance in terms of generation rate and amount of extractable information necessary for quantum secure applications.
The computation of classical Ising partition functions, coming from statistical physics, is a natural generalization of binary optimization.
This is a notoriously hard problem in general, which makes it an especially interesting task to consider in the search for practical quantum advantage in near term quantum computers.
In this work we view classical Ising models (on certain graphs) as quantum imaginary time evolution, which is enabled by the use of the transfer matrix mapping.
We study this mapping from two points of view: (1) following Onsager and Kaufman's original solution of the 2D Ising model, which serves as a starting point, we consider more general models and the possibility of a similar Lie-theoretic solution; (2) we consider quantum algorithms for the computation of partition functions and thermal averages via transfer matrices, which can be implemented either with block encodings inside larger unitaries or by approximating the state trajectories with unitary operators.
Classical information loading is a crucial task for many quantum algorithms, playing a fundamental role in the field of quantum machine learning. Consequently, the inefficiency of this loading process becomes a significant bottleneck for the application of these algorithms. In this context, we present and compare algorithms for the amplitude classical data into a quantum computer.
We introduce two approximate quantum-state preparation methods for the NISQ era, drawing inspiration from the Grover-Rudolph algorithm. The first method reduces the number of gates required when no ancillary qubits are used, while the second proposes a variational algorithm capable of loading real functions beyond the Grover-Rudolph algorithm. We also examine the encoding of polynomial functions, either through their matrix product state representation or a scheme that involves the block encoding of the linear function using the Walsh-Hadamard transform and a polynomial transformation of the amplitudes, achieved through the quantum singular value transformation (QSVT).
In digital communication, authentication, integrity, and confidentiality are fundamental security requirements. These properties enable robust security controls such as secure boot, secure update, secure access, and secure TLS across connected devices. However, existing cryptographic algorithms, while effective against classical threats, vulnerable to powerful quantum attacks. In this paper, we propose quantum-resilient implementantations for the aforementioned security controls. Leveraging NIST-selected algorithms— CRYSTALS-Dilithium, CRYSTALS-Kyber, FALCON, XMSS, and SPHINCS+ our solutions not only withstand quantum attacks but also outperform their classical counterparts in efficiency for various use cases.
A circuit design interface for simulation of quantum circuits is presented. This simple but powerful tool aims to enable and improve experimentation in the field of quantum computing, either for professional or amateur usage. It provides an intuitive, automated, easy-to-use environment for building algorithms using the circuit model of quantum computation, while freeing the experimenter from technical procedures, such as coding or program installations. It provides a wide range of tools to the user, from a universal gate-set, to custom gate definitions, post-selected measurements, complex compositions of sub-circuits in a recursive way, good modularity and nice output results. Moreover, it is combined with popular backend simulators due to its circuit-parsing capabilities.