Time Table

[Aug. 12 (Tuesday)]

Talk 1: Minsoo Kim (KAIST): Distinct Contributions of Entanglement and Ancilla Qubits to Exponential Quantum Learning Advantage

Quantum channel learning has emerged as a promising application of quantum technology, offering the potential for a quantum advantage. Achieving such an advantage requires quantum resources, such as ancilla qubits and resulting entangled input states of channels. However, how these two resources contribute differently to the quantum advantage is not fully resolved. In this work, we show that even with vanishingly small entanglement in the input states, the channel learning task can be accomplished by using only polynomial sample complexity. In contrast, if the number of ancilla qubits is restricted, even when learning a sparse channel, an exponential sample complexity can be unavoidable. Therefore, our work reveals that the entanglement of the input state and the ancilla qubit number contribute in fundamentally different ways to enabling the exponential quantum advantage.

Lecture 1: Minki Hhan (University of Texas at Austin): Cryptography in Quantum World

The most compelling application of quantum computing lies in cryptography: Shor's algorithm suggests replacing the currently deployed cryptosystems like RSA, and quantum key distribution introduces a new pathway in cryptography using quantum computers. In this lecture series, I survey the old and new applications of quantum computing in the field of cryptography and applications of cryptographic perspectives in quantum computing.

[Aug. 13 (Wednesday)]

Talk 2: Guedong Park (Seoul National University): Deconvolution-Based Noise Detection and Recovery of Quantum Resources

In this talk, we introduce our recent works of deconvolution method for learning and correcting noise in quantum circuits. The main content is divided into two sections. In the first section, we present our recent preprint [arXiv:2503.12870] on noise tailoring and the detection of hypergraph states. We propose an efficient Clifford-circuit-based scheme for tailoring and detecting noise in third-ordered hypergraph states, a representative non-Clifford resource generated by CCZ, CZ, and Z gates. The core idea of our method is to transform the noisy input state into a diagonal form and derive a convolution equation for the noise rate using Clifford circuits. This equation is then decoded using the fast Hadamard-Walsh transform or an approximation method. The approximation concerning the $l_2$-norm can be performed efficiently with respect to the number of qubits while maintaining sufficient accuracy. Moreover, under the common experimental assumption of sparse noise, an $l_1$-norm approximation becomes viable. Our approach offers a novel way to characterize noise properties of non-Clifford resources using only Clifford resources, and its various applications. In the second section, as an ongoing work, we discuss how to recover a pure measurement distribution with samplable distributions—such as a noise rate or noisy measurement distribution. This framework allows the construction of the pure distribution as a perturbative series involving multiple convolutions with samplable distributions. We apply this formalism to random Clifford measurements, and demonstrate how this approach can be used to mitigate estimation errors in shadow tomography.

Talk 3: Kyungmin Cho (Seoul National University): Entanglement-enhanced randomized measurement in noisy quantum devices

Quantum hardware is advancing rapidly across various platforms, yet implementing large-scale quantum error correction (QEC) remains challenging. As hardware continues to improve, there is a growing need to identify potential applications on noisy quantum devices that can leverage these enhancements. With this motivation, we explore the advantages of shallow measurements over (non-entangling) single-qubit measurements for learning various properties of a quantum state. While previous studies have examined this subject, they have primarily focused on specific problems. Here, by developing a new theoretical framework, we demonstrate how shallow measurements can benefit in diverse scenarios. Despite the additional errors from two-qubit gates in shallow measurements, we experimentally validated improvements compared to single-qubit measurements in applications like derandomization, common randomized measurements, and machine learning up to 40 qubits and 46 layers of two-qubit gates, respectively. As a result, we show that hardware improvements, even before QEC, could broaden the range of feasible applications.

Lecture 2: Jaewoo Joo (University of Portsmouth): Quantum simulation challenges ahead

The field of quantum information science, which utilises quantum bits (qubits), is considered to have potential applications across a wide range of disciplines — including physics, mathematics, chemistry, biology, engineering, and computing — and has seen significant advancement over the past few decades. It is now actively pursuing practical industrial applications. In particular, quantum simulation, traditionally regarded as a subfield of quantum computing, has expanded its scope and is now incorporating the strengths of classical computation (e.g., machine learning-based optimisation) in an effort to achieve quantum advantage beyond the limits of classical methods. However, despite these remarkable recent developments, there remain substantial challenges to be addressed before widespread commercialisation can be realised. In this context, we seek to explore a number of key topics related to the central question: Can quantum simulation truly be of practical use in the future?

양자 비트(큐비트)를 활용하는 양자 정보 과학 분야는 물리학, 수학, 화학, 생물학, 공학, 컴퓨팅 등을 포괄하는 전분야에 응용이 가능하다고 여겨지며, 지난 수십 년간 크게 발전해 왔습니다. 현재 이 분야는 실질적인 산업 응용을 적극적으로 모색하고 있습니다. 특히, 전통적으로 양자 계산 (quantum computing) 의 한 분야로 간주되던 양자 시뮬레이션은 (quantum simulation) 그 응용 범위를 확장하였으며, 이제는 머신러닝 기반 최적화와 같은 고전 계산의 강점을 통합하여 고전 계산의 한계를 넘는 양자 우위를 실현하고자 연구중에 있습니다. 그러나, 이러한 현재의 비약적인 발전에도 불구하고, 실질적 상용화까지는 여전히 해결해야하는 중요한 과제들이 존재합니다. 이러한 맥락에서 우리는 “양자 시뮬레이션이 미래에 실제로 실용적인 도움이 될 수 있는가?”라는 질문에 관련된 몇가지 주제들을 탐구하고자 합니다.

Ref 1: Yonghae Lee et. al., Hybrid quantum linear equation algorithm and its experimental test on IBM Quantum Experience, - Scientific Reports 9, 4778 (2019)

Ref 2: Jaewoo Joo, Hyungil Moon, Quantum variational PDE solver with machine learning - arXiv:2109.09216

Ref 3: Michael Lubasch et. al., Variational quantum algorithms for nonlinear problems - Phys. Rev. A 101, 010301(R) (2020)

[Aug. 14 (Thursday)]

Talk 4: Gwangil Ahn (Hanyang University): Filtering-based Bayesian updating on variational quantum eigensolver

Variational quantum eigensolver is one of the studies that is currently being actively conducted because of the relatively short circuit depth. One of the problems in variational quantum eigensolver is the number of types of measurements increases exponentially with the number of qubits. There exists an algorithm to try to solve this problem with adopting the structure of iterative phase estimation algorithm with Bayesian updating, but it needs a number of calculations in performing that. In this work, we suggest a filtering-based Bayesian updating algorithm of variational quantum eigensolver and how to determine related parameters. Finally, we show some results on several Hamiltonian examples.

Talk 5: Sung Bin Lee (Seoul National University): Scalable projected entangled-pair state representation of random quantum circuit states

Classical simulation of a programmable quantum processor is crucial in identifying the threshold of a quantum advantage. We demonstrate the simple update of projected entangled-pair states (PEPSs) in the Vidal gauge that represents random quantum circuit (RQC) states, which center around recent quantum advantage claims. Applied to square lattices of qubits akin to state-of-the-art superconducting processors, the PEPS representation is exact for circuit depths less than Dtr = β log2 χ, where χ is the maximum bond dimension and 2 ≲ β ≲ 4 depends on the choice of two-qubit gates, independent of the qubit number n. We find the universal scaling behaviors of the state fidelity by treating large-scale circuits of n ≤ 104, using χ ≤ 128 on a conventional CPU. We also estimated the total variation distance and confirmed it satisfies the Fuchs–van de Graaf inequality, with an extra error contribution from approximate PEPS contraction. Our method has computational cost scaling polynomially with n for circuit depth D = O( log n) and is more advantageous than matrix product state (MPS) approaches if n is large. This work underscores PEPSs as a scalable tool for benchmarking quantum algorithms, with future potential for sampling applications using advanced contraction techniques.

Lecture 3: Chae-Yeun Park (Yonsei University): Quantum and classical algorithms for many-body problems

Understanding the properties of quantum many-body systems is a fundamental objective of physics. In this lecture, we review various quantum and classical algorithms for solving dynamics and the ground state of quantum many-body Hamiltonians.

Part 1: Classical algorithms for many-body physics

We introduce tensor networks and quantum Monte Carlo algorithms. We discuss the class of Hamiltonians for which they are well-suited and identify their limitations from the computational complexity perspective.

Part 2: Quantum algorithms for many-body physics

We introduce variational and fault-tolerant quantum algorithms. We discuss the computational resources to run those algorithms using quantum computers.