Within the framework of this project, we are diligently crafting quantum-classical hybrid algorithms that seamlessly meld the prowess of quantum computation with the robust capabilities of classical machine learning. Our primary objective revolves around enhancing the performance of current noisy and intermediate-scale quantum (NISQ) computers, leveraging cutting-edge technology to achieve this.
This enhancement is achieved through sophisticated post-processing techniques employing machine learning paradigms, most notably neural networks.
At the core of our project lies a clear and compelling ambition: the practical application of these novel algorithms to address pivotal challenges in the realm of materials science. Specifically, our focus extends to fields such as quantum condensed-matter physics and quantum chemistry, where the potential for transformative breakthroughs is considerable.

Quantum Artificial Intelligence (QAI) is considered to be a promising quantum computing application in the present era of noisy-intermediate scale quantum computers. There are two aims in this research project: first, we focus on a QAI algorithm to coherently learn quantum data using quantum simulations of quantum field theory as benchmarks; second, we develop techniques to design quantum circuits and optimize quantum gates, tailored for specific high energy physics applications.

In our pursuit of advancing quantum information science and technology into the post-NISQ (Noisy Intermediate-Scale Quantum) era, we are pioneering innovative programming paradigms centered around the integration of higher-order functions into quantum computing, rooted in the framework of higher-order quantum operations. This pioneering approach lays the foundation for the development of cutting-edge quantum algorithms and applications, particularly geared towards quantum simulation and quantum sensor technology.
Furthermore, we are on an exploratory mission into the uncharted territory of quantum computation. We aim to deepen our comprehension of the spacetime structure inherent in quantum physics as it pertains to information processing.
This journey involves elucidating the distinctive properties of parallelizability and causal structures intrinsic to quantum programming. Our efforts are thus not confined to the development of technology alone but extend into the realm of fundamental quantum theory, marking a transformative leap in our understanding of quantum information processing.

Quantum chemistry's potential on quantum computers has garnered significant interest, particularly as a promising target for emerging quantum devices. The field traditionally addresses the solution of the time-independent Schrödinger equation for electrons. However, the exploration of the time-dependent Schrödinger equation (TDSE) on quantum computers has remained relatively underexplored. TDSE is indispensable for describing light-matter interactions, yet it presents the formidable challenge of combinatorial explosion inherent to quantum many-body systems.
In our research endeavor, we are embarking on a pioneering path. Our objective is to construct a hybrid quantum-classical simulator for multi-electron dynamics, utilizing IBM Quantum's resources. Going beyond this, we aspire to extend our hybrid simulator to encompass nonadiabatic dynamics, a domain where both electrons and nuclei are treated quantum mechanically.
This ambitious endeavor aims to showcase the first-ever successful utilization of a real quantum computer in tackling complex multi-electron and nonadiabatic dynamics, such as dissociative ionization in a hydrogen molecule.
This groundbreaking achievement will open the doors to a multitude of future possibilities, including the accurate simulation of biologically significant photoreactions—endeavors that would be immensely challenging to undertake using classical computers alone. Our research is charting a course towards transformative advancements at the intersection of quantum computing and quantum chemistry, promising to revolutionize our ability to model and understand complex molecular processes.

Quantum computation has emerged as a frontier technology with the potential to revolutionize a myriad of fields, from cryptography to materials science, offering the promise of computational capabilities beyond the reach of classical computers. However, despite the tremendous potential, several fundamental challenges must be addressed before quantum computing can be harnessed for practical applications on a large scale.
In this pioneering project, we are leveraging our extensive expertise in spintronics and AI physics, domains where we have made significant advancements, to tackle these critical challenges head-on. Our overarching goal is to propel the development and practicality of quantum computers by focusing on two key aspects: the development of q-bit drivers and the creation of efficient calibration methods.
The heart of any quantum computer lies in its q-bits, the quantum equivalent of classical bits. Our research endeavors are dedicated to the creation of q-bit drivers that are not only highly efficient but also possess the capability to manipulate and control these delicate quantum states with utmost precision. These drivers will serve as the backbone of quantum computation, enabling the seamless execution of complex algorithms and computations.
Moreover, calibration in quantum computing is a critical but often overlooked aspect. Quantum systems are inherently susceptible to environmental noise and fluctuations, making precise calibration a non-trivial task. We are committed to pioneering innovative calibration methods that ensure the stability and reliability of quantum computations, mitigating the impact of noise and environmental factors.
Through this project, we aspire to not only contribute to the field of quantum computation but also to bridge the gap between theoretical potential and practical utility. Our work will play a pivotal role in advancing the state-of-the-art in quantum technology, paving the way for its integration into diverse applications, ranging from optimizing supply chains to solving complex scientific problems.
By harnessing the synergistic potential of spintronics, AI physics, and quantum computing, we are poised to unlock new frontiers in technology and scientific discovery, catalyzing a transformative era where the power of quantum computation is harnessed to reshape our world in profound ways.

Superconducting quantum circuits are being actively investigated for quantum information processing. In a quantum transducer, electromagnetic waves in the microwave region handled by superconducting quantum circuits are converted into light and vice versa. The quantum transducer provides a quantum interconnect between superconducting quantum computers, and also to control them optically. Therefore, the optical interface using quantum transducers will be a core technology for the expansion of quantum technologies such as large scale quantum computers and a quantum internet. In this research, we will develop a quantum transducer that connects light and microwaves via acoustic quanta such as vibrations of objects and elastic waves. By combining high performance optical waveguide, superconducting technology and optomechanical technology, we aim to achieve highly efficient optical microwave conversion.

Quantum states formed in relatively simple circuit dynamics of a superconducting processor are subject to external noise sources and unwanted inter-qubit interactions. Quantum states in a multi-junction circuit possess symmetry which can be used to protect qubits from noise effects. In this research, we will leverage the added circuit degrees of freedom of the multi-junction qubit to enhance the qubit performance. We will also explore novel two-qubit gates and gate decompositions using higher energy levels of the qubit, through which we aim to extend the computational capacity of current noisy superconducting processors.