Program with Abstracts > Tuesday PM + posters

Towards the Entanglement of a Superconducting qubit to Erbium defects

Kritika Mundeja et al. (National University of Singapore)

Superconducting circuits are promising candidates for building quantum processors due to their scalability, design flexibility, fast gate operations and high fidelity. However, their operation at microwave frequencies presents challenges for integration with existing telecommunication infrastructure which relies on optical fibres for long-distance signal transmission at room temperature. To bridge this frequency gap, we aim to achieve fast and reliable optical-microwave entanglement using rare-earth ions as an intermediary. 

Our initial focus is on establishing entanglement between an Erbium ion ensemble and a Transmon qubit through a cavity. We leverage the Zeeman splitting of Erbium ions to align with the microwave regime of our superconducting cavity. Additionally, we employ chirped pulses to efficiently retrieve the excitation stored within the ensemble. Through this poster, we present our entanglement protocol and results, demonstrating spin-resonator cooperativity, long spin coherence times and high Transmon qubit coherence in a magnetic field. Our work represents a significant step toward the seamless integration of superconducting circuits with optical quantum networks, paving the way for scalable quantum communication and hybrid quantum computing architectures.

SFFT-based Homogenization: Using Tensor Trains to Enhance FFT-based Homogenization

Sascha Hauck, Matthias Kabel, Ali Mazen, Nicolas Gauger

(Fraunhofer Institute ; Kaiserslautern University ; Multiverse Computing)

Homogenization is a key technique for approximating the macroscopic properties of materials with microscale heterogeneity. The FFT-based Homogenization method has gained widespread usage due to its computational efficiency and accuracy in handling complex microstructures. However, despite its advantages, the method is limited by speed and memory constraints, particularly when applied to high-resolution discretizations. These limitations affect its scalability and efficiency, especially in large-scale simulations or when dealing with highly detailed microstructures. These challenges arise from the fundamental reliance on the Fast Fourier Transform, which imposes inherent restrictions on further advancements. In this paper, we propose a novel SFFT-based Homogenization algorithm that utilizes a Quantized Tensor Train variant of the Quantum Fourier Transform. This method is tailored to the geometry under consideration and offers significant improvements in time complexity and memory efficiency compared to the traditional FFT-based approach while remaining executable on classical hardware. The method is applicable only if a suitable Quantized Tensor Train representation exists for the stiffness operator associated with the underlying geometry. 

Nonreciprocal Quantum Batteries

Borhan Ahmadi, Pawel Mazurek, Pawel Horodecki, Shabir Barznajeh

(Gdansk University; Institute of Science and Technology of Austria)

Nonreciprocity, arising from the breaking of time-reversal symmetry, has become a fundamental tool in diverse quantum technology applications. It enables directional flow of signals and efficient noise suppression, constituting a key element in the architecture of current quantum information and computing systems. Here we explore its potential in optimizing the charging dynamics of a quantum battery. By introducing nonreciprocity through reservoir engineering during the charging process, we induce a directed energy flow from the quantum charger to the battery, resulting in a substantial increase in energy accumulation. Despite local dissipation, the nonreciprocal approach demonstrates a fourfold increase in battery energy compared to conventional charger-battery systems. This effect is observed in the stationary limit and remains applicable even in overdamped coupling regimes, eliminating the need for precise temporal control over evolution parameters. Our result can be extended to a chiral network of quantum nodes, serving as a multi-cell quantum battery system to enhance storage capacity. The proposed approach is straightforward to implement using current state-of-the-art quantum circuits, both in photonics and superconducting quantum systems. In a broader context, the concept of nonreciprocal charging has significant implications for sensing, energy capture, and storage technologies or studying quantum thermodynamics.

Generative-Based Algorithm for Data Clustering on Hybrid Classical-Quantum NISQ Architecture

Julien Rauch (Université Paris Saclay)

Clustering is a well-established unsupervised machine-learning approach to classify data automatically. In large datasets, the classical version of such algorithms performs well only if significant computing resources are available (e.g., GPU). An alternative approach relies on integrating a quantum processing unit (QPU) to alleviate the computing cost. This is achieved through the QPU’s ability to exploit quantum effects, such as superposition and entanglement, to natively parallelize computation or approximate multidimensional distributions for probabilistic computing (Born rule). In this paper, we propose first a clustering algorithm adapted to a hybrid CPU-QPU architecture while considering the current limitations of noisy intermediate-scale quantum (NISQ) technology. Secondly, we propose a quantum algorithm that exploits the probabilistic nature of quantum physics to make the most of our QPU’s potential. Our approach leverage on ideas from generative machine-learning algorithm and variational quantum algorithms (VQA) to design an hybrid QPU-CPU algorithm based on a mixture of so-called quantum circuits Born machines (QCBM). We hope to achieve accurate data clustering and acceleration on the NISQ architectures scheduled to be available in the next few years. Finally, we analyse our results and summarize the lessons learned from exploiting a CPU-QPU architecture for data clustering.

Optimizing unitary coupled cluster wave functions on quantum hardware : error bound and resource-efficient optimizer

Martin Plazanet and Thomas Ayral  (Eviden)

Simulating quantum many-body physics is the prime motivation behind the inception of quantum computing. To this end, many methods for quantum chemistry calculations have been developed, some of which with noisy intermediate scale quantum (NISQ) devices in mind. In this work, we study the projective quantum eigensolver (PQE) method, recently introduced by [1] in order to overcome the shortcomings of the most widespread NISQ method, the variational

quantum eigensolver (VQE). We first show that one can derive a rather tight upper bound on the energy error. We then introduce a mathematical study of the classical optimization itself, and derive a novel residue-based optimizer. Using molecules LiH and BeH2 as benchmarks, we present numerical evidence of the superiority of our scheme over both VQE and the original optimization introduced in [1], thus establishing the viability of PQE as a very promising alternative to VQE.

[1] PRX Quantum 2 (2021), 10.1103/prxquantum.2.030301.

Silicon Spin Qubit Circuits: Modeling and Evaluation of Energetic Efficiency

Konstantina Koteva (Institut Néel ; University of Singapore)

As quantum devices scale, noise and rising cooling costs threaten performance. We present a full-stack model for silicon spin qubits (EDSR and ESR), connecting gate operations, measurement, and cryogenic power to computational fidelity. Using experimental data, we benchmark energy consumption for a 4-qubit VQE and a 20-qubit random circuit. Through the Metric-Noise-Resource (MNR) framework, we identify optimal qubit temperatures and driving frequencies to minimise power use without compromising success. Our results highlight practical strategies for energy-efficient, scalable quantum computing.

Efficient cross-device verification via Pauli sampling for highly-entangled, highly-doped states

Jose Carrasco (FU Berlin)

Cross-device verification (a.k.a. distributed inner product estimation) allows two remote parties to estimate inner products  tr(), with each having black-box access to copies of  and , respectively. When the states  and  exhibit low entanglement or can be prepared with few non-Clifford gates, this task can be reduced to independently learning efficient classical descriptions of each state using established techniques, and sharing this description in order to compute the overlap. In our work, we argue that efficient cross-device verification is also possible in more complex scenarios where tensor network and stabilizer-based methods are insufficient. Specifically, we introduce a class of highly entangled real states that cannot be approximated by circuits with log-many non-Clifford gates, and prove that Bell sampling enables efficient inner product estimation for these states. Notably, these findings are robust against preparation errors. We present possible applications of the results in quantum cryptography and verification.

Refs: arXiv:2405.06544, arXiv:2501.11688