Conference Schedule

Opening session by QTRic and SQ2

Quantum Computing: Promise, Peril and Responsible Sharing -- Efficiently uploading data into quantum states is essential for many quantum algorithms to achieve advantage across a wide range of applications. In this paper, we address this challenge by developing a method to upload a polynomial function f(x) defined on the interval x in [-1, 1] into a pure qubit-based quantum state, where a discretized version of f(x) appears as the state amplitudes. The preparation cost scales as O(n log n) in the number of qubits n, and scales linearly with the polynomial degree Q. This efficiency enables the preparation of states whose amplitudes correspond to high-degree polynomials (up to 10^4), allowing accurate approximation of functions that admit efficient polynomial series representations and whose amplitude profiles are not extremely localized. We provide a fully explicit circuit realization, based on a decomposition into four real polynomials satisfying specific parity and boundedness constraints. We further extend this construction to piecewise polynomial functions, a case not previously treated explicitly in the literature, with complexity scaling linearly in the number of pieces. Finally, we present detailed gate counts and resource analysis, demonstrating efficient and resource-transparent quantum circuit

• Invited Talk

Higher-order quantum learning algorithms for quantum channels -- Efficiently learning the properties of quantum objects is one of the key anticipated applications of quantum computers. We develop higher-order quantum algorithms for learning quantum channels, based on higher-order quantum computation, which transform a black-box quantum channel into either another quantum channel or a numerical quantity characterizing its properties. In this framework, a quantum computer acquires “quantum data” about the channel through black-box queries and then outputs the target channel or evaluates the desired properties coherently, without reconstructing a full classical description of the black box. We present two such fully quantum algorithms for learning black-box quantum channels: one for universal unitary inversion, and another for learning the singular-value moments of an unknown quantum channel.

• Invited Talk

Analog Quantum Simulation of Dirac Hamiltonian in Circuit QED -- Analog quantum simulation has emerged as a powerful tool for emulating complex quantum phenomena through tunable Hamiltonians. In this presentation, we demonstrate a versatile circuit QED platform that realizes the Dirac Hamiltonian by coupling a superconducting qubit to one or two cavity modes. This architecture allows for the precise simulation of both one- and two-dimensional relativistic dynamics, such as Zitterbewegung, within a highly controllable environment. By varying system parameters, we reproduce Dirac-like motion, supported by a close agreement between experimental observations and numerical calculations. Furthermore, we provide a detailed analysis of physical imperfections—including the breakdown of the rotating-wave approximation (RWA), sideband asymmetries, and Rabi-frequency instabilities—and their impact on simulation fidelity. Our results establish a robust approach to exploring relativistic effects, offering a scalable roadmap for future analog quantum simulations in superconducting circuits.

• Invited Talk

Speaker: Giacomo Franceschetto

• Oral Session

Speaker: V Vijendran

• Oral Session

Speaker: Michael Ragone, Dario Poletti, Apimuk Sornsaeng

• Oral Session

Speaker: Chattamas Manoworakul

• Oral Session

Electrometry of Extremely-Low Frequencies from kHz to Sub-Hz with a Rydberg-Atom Sensor -- Rydberg-atom electric-field sensors provide compact, broadband quantum receivers, but extending them to ultra-low frequencies has been hindered by electric-field screening in conventional vapor cells. We report an electrode-free strategy for low-frequency electrometry based on Rydberg-EIT in a paraffin-coated cesium vapor cell, combined with auxiliary field modulation and lock-in detection. The paraffin coating slows charge redistribution at the cell wall, creating a time window in which externally applied fields can be sensed before screening dominates. Using this approach, we detect electric fields from 0.5 Hz to 10 kHz within a unified operating scheme and measure sensitivities of 819 μV/cm/√Hz at 1 Hz, 33 μV/cm/√Hz at 10 Hz, 10 μV/cm/√Hz at 100 Hz, and 2 μV/cm/√Hz at 1 kHz. At the lowest frequencies, the sensor outperforms a same-size classical dipole receiver by one to two orders of magnitude, highlighting the potential of atomic receivers where compact classical antennas become increasingly ineffective. These results establish Rydberg vapor cells as a promising platform for compact low-frequency field sensing, with potential impact in underwater communication, geophysical and atmospheric measurements, low-frequency radio science, and multimodal quantum sensing.

• Invited Talk

Problem-informed graphical quantum generative learning -- Leveraging the intrinsic probabilistic nature of quantum systems, generative quantum machine learning (QML) offers the potential to outperform classical learning models. Current generative QML algorithms mostly rely on general-purpose models that, while being very expressive, face several training challenges. One potential way to address these setbacks is by constructing problem-informed models that are capable of more efficient training on structured problems. In particular, probabilistic graphical models provide a flexible framework for representing structure in generative learning problems and can thus be exploited to incorporate inductive bias into QML algorithms. In this work, we propose a problem-informed quantum circuit Born machine Ansatz for learning the joint probability distribution of random variables, with independence relations efficiently represented by a Markov network (MN). We further demonstrate the applicability of the MN framework in constructing generative learning benchmarks and compare our model's performance to previous designs, showing that it outperforms problem-agnostic circuits. Based on a preliminary analysis of trainability, we narrow down the class of MNs to those exhibiting favourable trainability properties. Finally, we discuss the potential of our model to offer quantum advantage in the context of generative learning.

• Invited Talk

Single-Photon-Interference-Mediated Remote Correlation for Long-Distance QKD and Long-Lived Entanglement Generation -- We report advances in quantum communication leveraging single-photon interference for efficient nodes interconnection. For prepare-and-measure quantum key distribution (QKD), we demonstrate end-to-end twin-field QKD transitioning from proof-of-principle experiments to complex field environments, repeatedly setting transmission distance records. For ion-trap quantum repeaters, we achieve high-fidelity remote ion-ion entanglement and subsequently realize device-independent QKD over 100 km. We further validate that the entanglement survival time exceeds the average waiting time for entanglement generation, a critical condition for repeater-based operation.

• Invited Talk

Speaker: Mario Herrero González, Sreeraj Rajindran Nair

• Oral Session

• QTRic

Occam's Quantum Razor: On the potentials of quantum machines to do more with less

• Invited Talk

Speaker: Muhammad Taufiqi

• Oral Session

Quantum Technology Foundation (Thailand) Co., Ltd. (QTFT)

• Sponsor

Speaker: Josep Lumbreras, Xiangjing Liu

• Oral Session

Quantum Algorithms and Use Cases: Chemistry and CFD solvers -- Technical Leader / QAMD team’s director, Qunova Computing -- As quantum hardware matures, the central challenge shifts from theoretical promise to practical utility. Qunova Computing presents two hybrid quantum-classical algorithms delivering demonstrated Industrial Quantum Advantage on today's NISQ hardware. HI-VQE (Handover Iterative VQE) is a quantum chemistry solver that achieves chemical accuracy for molecular ground and excited state energy calculations, especially for strong correlation molecules (such as FeMoCo, 2Fe-2S, and iron-porphyrin) beyond 40 qubits. Commercially available via IBM Qiskit Functions and AWS Marketplace, HI-VQE finds applications in OLED design, catalyst development, and carbon capture reaction analysis. With the same hybrid variational philosophy, we developed the technique called HI-VQNS, that extends the hybrid variational framework to computational fluid dynamics (CFD), solving Navier-Stokes equations for single- and two-phase thermofluid problems. Targeting electronics cooling, automotive thermal management, and aerospace CFD, HI-VQNS could achieve up to 1 billion grid points with polylog qubits scale while maintaining accuracy. Therefore, both algorithms are compelling candidates for near-term quantum utility across industry.

• Sponsor

• Poster Session