Quantum AI Technology

Taking advantage of emerging quantum computing technologies for high-performance and parameter-efficient artificial intelligence (AI) and machine learning (ML).

MERL Researchers: Toshiaki Koike-Akino, Ye Wang, Pu Wang, Jing Liu, Kieran Parsons.

Overview

Natural Computing: A Sustainable Path for AI

The concept of natural computing is a promising avenue for fostering sustainable growth in AI, moving beyond the current reliance on traditional CPU (central processing unit), GPU (graphical processing unit), and TPU (tensor processing unit) modalities. Within the realm of natural computing, which includes molecular computing, liquid-state computing, and DNA (deoxyribonucleic acid) computing, quantum computing has garnered widespread global interest.

The Rise of Quantum Computing Applications

Quantum computing stands out for its immense potential in computational capacity, parameter efficiency, and rapid throughput. This has led to the emergence of diverse applications like quantum chemistry and quantum finance. This technology has also made inroads into the field of AI, giving rise to the exciting areas known as quantum machine learning (QML) and quantum AI (QAI). The surge in articles on QML over the past decade has been exponential, nearly doubling annually, with just a 6-year delay.

Rapid Advancements in Quantum Technology

The development of quantum technology has been exceptionally rapid, with significant progress on both the hardware and software fronts. Many manufacturing industries have successfully produced real quantum processing units (QPUs). QPUs exceeding 1,000 qubits were already released in 2023. Additionally, sophisticated quantum software platforms, such as Qiskit, Cirq, Pennylane, and TensorFlow Quantum, have become readily available to the community.

Embracing the Quantum AI Frontier

Given the rapid advancements in quantum technology, it is an opportune time to explore the new frontiers of quantum AI and to position ourselves to be "quantum ready ." QML is seen as a potential solution to drive the current "AI spring" and to prevent the possibility of a future "AI winter."

Harnessing the Power of Quantum Computing

The combination of powerful quantum hardware and sophisticated quantum software presents a unique opportunity to unlock new possibilities in AI and ML. By embracing the quantum computing paradigm, researchers and developers can push the boundaries of what's possible, potentially leading to breakthroughs that could revolutionize various industries and applications.

MERL's Initiatives on Quantum AI

Quantum AI: Unlocking Exponential Expressivity

Mitsubishi Electric Research Laboratories (MERL) has been exploring quantum computing and quantum AI for in recent years, resulting in numerous academic publications and invited tutorials, publicized with several press releases, and participation in competition and hackathons.

One of the most prominent advantages of QML over classical AI is its impressive capacity for exponential expressivity, enabling the realization of highly efficient parameterization of DNN architectures. Our research has demonstrated that many large-scale DNN models can be substantially downsized by applying quantum neural networks (QNNs), without sacrificing performance. To make this technology more accessible, we have developed an automated QML (AutoQML) framework that can search for compact QNN architectures without the need for extensive quantum expertise.

Compact AI Systems for Industrial Applications

We have made a press release on our AutoQML technology, Mitsubishi Electric's New Quantum Artificial Intelligence Technology Uses Automated Design to Realize Compact Inference Models, showcasing its ability to create compact AI systems for diverse industrial applications. Please refer our short promotional video on our QML research and the future of AI at:

MERL's Quantum AI Technology

Tutorials on Quantum Machine Learning

Following our contributions to various QML applications, we have been invited to share our expertise through tutorial talks and seminar presentations at prestigious IEEE conferences and events. In 2022, we were invited to deliver a QML tutorial at the IEEE GLOBECOM conference. This was followed by another QML tutorial presentation at the IEEE VCC in 2023, where we had the opportunity to engage with a global audience of researchers and industry professionals. Additionally, we were invited to give a seminar talk at the IEEE Boston Photonics event in 2022, further expanding our reach and sharing our insights with the broader scientific community. One of video recordings on our QML tutorial talks is available on our YouTube channel:

GLOBECOM 2022/VCC 2023 Tutorial] Post-Deep Learning Era: Emerging Quantum Machine Learning for Sensing and Communications (Session 2)

Showcasing Our Quantum Expertise

To further demonstrate our engagement in the field of quantum AI, we have actively participated in several quantum hackathon and code challenge events, achieving high rankings, awards and rewards as follows:

  • QHack 2022
  • Quantum Code Camp 2022
    • Code challenge ranking 5th place (over 300 teams participated).
    • Received swag pack prize.
  • QHack 2023
    • Code challenge ranking 19th place (over 700 teams participated)
    • Received Nvidia A200 power-up award.
    • Quantum XR project: GitHub qXR: Quantum & Mixed Reality
    • Received the 3rd prize in visualization award.

Pioneering Applications of Quantum Machine Learning

Our team has proactively embarked on initiatives to apply QML to a diverse range of cutting-edge fields. These include, but are not limited to, radio communications, fiber-optic communications, Internet of things (IoT), Wi-Fi sensing, compressed sensing, Terahertz (THz) imaging, brain-computer interface (BCI), mixed reality (XR), electronic design automation (EDA), green AI and generative AI. Several papers detailing our research themes and findings in these innovative areas are found on arXiv and OpenReview as follows:

These publications offer insights into our exploration of QML applications across various domains and highlight the potential impact of quantum computing on advancing technology and scientific frontiers. They are briefly introduced below one by one.

QML Applications: Wi-Fi Sensing

Transforming Wi-Fi Systems with Sensing Capability

Wi-Fi networks have become ubiquitous, serving not only as data communication channels but also as powerful tools for sensing applications like indoor human monitoring, vital sign checks, motion tracking, and object localization. To see further insights on our Wi-Fi sensing innovations visit MERL's research highlight page: mmWave Beam-SNR Fingerprinting (mmBSF) for Precise Indoor Localization using Commercial-Off-The-Shelf (COTS) Routers. Additionally, our research on Wi-Fi sensing and ISAC (integrated sensing and communications) can be reviewed in depth in our comprehensive 70-minute tutorial talk:

GLOBECOM 2022/VCC 2023 Tutorial] Post-Deep Learning Era: Emerging Quantum Machine Learning for Sensing and Communications (Session I)

Quantum-Enhanced Wi-Fi Sensing

Our team has developed quantum-enhanced Wi-Fi sensing systems, showcasing the power of QML in recognizing human poses and motions using standard Wi-Fi access points. Our QNN, with only 18 parameters, achieved performance comparable to a DNN with 35,000 parameters. Please see our technical report TR2022-044 Quantum Transfer Learning for Wi-Fi Sensing to learn more about quantum-enhanced human pose recognition.

Seamless Integration of Quantum AI

Integrating QNN models into existing AI systems typically requires extensive expertise in quantum AI methodologies. One needs to decide: how many qubits and ancilla bits are arranged; how to embed classical data into quantum systems; how to read quantum measurements back to classical systems; how to rotate and entangle qubits; how to update variational quantum parameters; how to integrated with DNNs. Indeed, there are many different quantum circuits (also known as ansatz): e.g., tree tensor network (TTN); matrix product state (MPS); multi-scale entanglement renormalization ansatz (MERA). Our AutoQML framework simplifies this process by automatically searching for suitable quantum ansatz configurations. This empowers AI engineers to seamlessly blend quantum AI with classical AI, harnessing quantum capabilities without the need of specialized quantum knowledge. Learn more about this framework in our technical report: TR2022-068 AutoQML: Automated Quantum Machine Learning for Wi-Fi Integrated Sensing and Communications.

QML Applications: THz Imaging

Unlocking the Potential of THz Sensing

The terahertz (THz) spectrum, situated between the infrared (IR) and microwave (MW) regions, offers unique advantages for sensing applications. With its ultra-wideband and short wavelength characteristics (300?m at 1THz), THz waves can achieve fine spatial resolution (unlike MW) and penetrate through various materials (unlike IR). Importantly, THz radiation is non-ionizing, making it a relatively safe option for numerous sensing applications, such as skin health monitoring, substance inspection, internal defect detection, factory automation, positioning, and imaging.

Quantum-Enhanced THz Sensing

Our team has developed quantum-enhanced THz sensing systems for non-destructive inspection. Through experimental validation, we have demonstrated the advantages of quantum AI in addressing complex challenges, such as inspecting multi-layered samples. Our experiments utilized THz time-domain spectroscopy (TDS) to reconstruct images printed on both sides of 3-layer cardboard samples. This task is particularly challenging due to shadow effects, which can lead to incorrect imaging of deeper layers. While conventional time-gated reflection techniques struggled to provide sufficient performance, our AI-driven approach proved to be highly effective.

Hybrid Quantum-Classical AI for Enhanced Performance

Traditional logistic regression offered an accuracy of 87.8% with 1,000 parameters, while a DNN achieved a good performance of 97.6% accuracy with 37,000 parameters. However, our QNN further improved the accuracy up to 99.6% by adding only 28 parameters. This remarkable performance was achieved through our hybrid quantum-classical AI model, which exploited the exponential expressivity of QNN by leveraging amplitude embedding to extract valuable features from the reflected time-domain samples. For more details on this work, please refer to our technical report: TR2022-110 Quantum Feature Extraction for THz Multi-Layer Imaging.

QML Applications: BCI and Biosensing

Advancements in Brain-Computer Interfaces

Brain-Computer Interfaces (BCIs) have experienced rapid advancements in the past decade, thanks to the development of new sensing devices and data processing methods. AI technology has played a crucial role in enabling practical BCI systems, allowing operators to control robots and systems without excessive stress or extensive training. Given the user-specific nature of biosignals, it has been of great importance to address user dependency to realize calibration-free BCI systems. Our team has a solid experience in investigating transfer-ready pre-shot learning methodologies to tackle this challenge. Explore more about our BCI and biosensing research themes on another research highlight page: Biosignal Processing for Human-Machine Interaction.

Quantum-Enhanced BCI Systems

We have developed quantum-enhanced BCI systems. Traditionally, space-time convolutional DNN architectures, such as EEGNet, have been widely used to analyze brainwave signals like electroencephalogram (EEG) and electrocorticography (ECoG) for BCI applications. In our research, we have proposed a quantum counterpart of EEGNet, called quEEGNet, which utilizes space-time quanvolutional architectures. We have demonstrated that our quEEGNet can improve the analysis of diverse biosignals, including electrocardiogram (ECG) and electromyography (EMG), as well as EEG and ECoG. For more detailed information on our quantum-enhanced BCI systems, please refer to our technical report: TR2022-121 quEEGNet: Quantum AI for Biosignal Processing.

QML Applications: Compressed Sensing

Compressed Sensing: Efficient Signal Reconstruction

Compressed sensing, also known as compressive sensing or sparse sampling, is a powerful signal processing technique that enables efficient reconstruction of sparse signals by finding solutions to underdetermined linear systems. This approach has found applications in diverse fields, including photography, holography, magnetic resonance imaging, network tomography, and microscopy. Various algorithms have been developed for compressed sensing, such as LASSO (Least Absolute Shrinkage and Selection Operator), sISTA (Iterative Shrinkage-Thresholding Algorithm), FISTA (Fast ISTA), OMP (Orthogonal Matching Pursuit), and OAMP (Orthogonal Approximate Message Passing).

Quantum-Enhanced Compressed Sensing

Our team has developed a quantum-enhanced compressed sensing algorithm, employing a variational quantum circuit (VQC) to perform the nonlinear denoising step. We have demonstrated that our quantum compressed sensing algorithm outperforms classical counterparts, particularly in IoT systems that allow grant-free access from a massive number of devices. For more details, please refer to our technical report: TR2022-052 Variational Quantum Compressed Sensing for Joint User and Channel State Acquisition in Grant-Free Device Access Systems.

Advancing Quantum Compressed Sensing

We have further extended our quantum compressed sensing approach by introducing meta-learning and turbo detection. When the signal to be reconstructed is encoded using error correction codes, our method integrates signal estimation, denoising, and decoding as three interconnected components. We have proposed the use of meta-learning based on the learning-to-learn (L2L) framework, which eliminates the need for gradient calculation for the VQC. Specifically, we have incorporated a long short-term memory (LSTM) module to control the turbo quantum approximate optimization algorithm (QAOA) for decoding within the compressed sensing loop. Please refer our work: Learning to Learn Quantum Turbo Detection.

QML Applications: EDA

Quantum-Enhanced Electronic Design Automation

Agile prototyping has become a crucial development strategy for proof-of-concept demonstrations in modern industries and businesses. The advancements in AI-based electronic design automation (EDA) systems have significantly accelerated the hardware prototyping over the past decade. We have developed a quantum-enhanced EDA system, called AutoHLS (Automated High-Level Synthesis). AutoHLS leverages a hybrid approach, combining DNNs and QNNs, to predict the quality of results, such as throughput, precision, and power consumption, after hardware synthesis.

We have validated the advantages of our AutoHLS system in the design of field-programmable gate arrays (FPGAs). By utilizing AutoHLS, we were able to implement energy-efficient AI models through optimized pruning and quantization techniques. For more details on our quantum-enhanced EDA systems, please refer to our published reports: TR2023-097 AutoHLS: Learning to Accelerate Design Space Exploration for HLS Designs and TR2023-096 Joint Software-Hardware Design for Green AI.

QML Applications: XR

Mixed Reality: Immersive Experiences

Mixed reality (XR), also known as extended realty, encompassing virtual reality (VR) and augmented reality (AR), has brought tremendous potential to consumer services and industrial solutions. With the proliferation of various XR devices from numerous manufacturers, and the emergence of innovative services and platforms like the metaverse, users can now immerse themselves in rich 3-dimensional (3D) scenes with unprecedented spatial and temporal freedom through their virtual avatars.

Quantum AI for 3D Scene Rendering

We have developed quantum-enhanced XR systems, leveraging the power of quantum AI to enhance the rendering of 3D scenes and models on XR devices. Our approach utilizes a quantum-based generative adversarial network (GAN) to predict depth information from RGB (red, green, blue) color plane images, enabling the synthesis of 3D models.

We have validated the benefits of our Quantum Conditional Wasserstein GAN with Gradient Penalty (QcWGAN-GP) in rendering high-quality 3D scenes on XR devices. For more details on our quantum-enhanced XR systems, please refer to our hackathon project: GitHub qXR: Quantum & Mixed Reality.

QML Applications: Digital Communications

Quantum AI: A Driving Force for 6G Communications and Beyond

Quantum technology is expected to be one of the driving forces behind the development of the sixth generation (6G) of digital communications systems. AI has already proven its efficiency in solving a wide range of problems in the communications domain, leading to the emergence of numerous AI-powered applications, such as resource allocation, interference mitigation, and end-to-end signal design. The integration of quantum computing into AI techniques holds the potential to further evolve and enhance communications technologies.

Quantum-Enhanced Decoding and Demodulation

We have developed quantum-enhanced decoding and demodulation techniques. Decoding error correction codes often depends on the specific coding structures, such as belief propagation decoding for low-density parity-check (LDPC) codes or successive cancellation list decoding for polar codes. To reduce the computational complexity of these decoding methods, we have introduced the use of quantum algorithms, specifically the Quantum Approximate Optimization Algorithm (QAOA). By reformulating the decoding problem into an irregular Ising problem, we were able to unify quantum decoding methods for any arbitrary linear codes. Furthermore, our research has revealed an empirical finding that low-density generator matrix (LDGM) codes tend to have better decoding probability when using QAOA-based quantum decoding. For more details, please refer to our technical report: TR2019-071 Channel Decoding with Quantum Approximate Optimization Algorithm.

Additionally, we have extended our quantum decoding approach to be used for quantum demodulation in high-order, high-dimensional constellations, which are employed in fiber-optic communications. You can find more information about this work in our technical report: TR2020-028 Variational Quantum Demodulation for Coherent Optical Multi-Dimensional QAM.

Media Coverage

MERL Software

MERL Videos

MERL's Quantum AI Technology
Quantum AI Tutorial Talk at VCC 2023
Quantum AI for Biosignal Processing at BHI 2022
Automated Quantum AI for ISAC at SAM 2022
Quantum AI for Wi-Fi Sensing at ICC 2022
Quantum AI for Compressed Sensing at ICC 2022
Quantum AI for THz Imaging at IRMMW-TH 2022
Applied AI and Quantum AI Seminar Talk at Boston Photonics 2022
Quantum AI for Fiber Communications at OFC 2020


MERL News & Events

  •  NEWS    Toshiaki Koike-Akino to give a seminar talk at EPFL on quantum AI
    Date: May 22, 2024
    MERL Contact: Toshiaki Koike-Akino
    Research Areas: Artificial Intelligence, Machine Learning
    Brief
    • Toshiaki Koike-Akino is invited to present a seminar talk at EPFL, Switzerland. The talk, entitled "Post-Deep Learning: Emerging Quantum AI Technology", will discuss the recent trends, challenges, and applications of quantum machine learning (QML) technologies. The seminar is organized by Prof. Volkan Cevher and Prof. Giovanni De Micheli. The event invites students, researchers, scholars and professors through EPFL departments including School of Engineering, Communication Science, Life Science, Machine Learning and AI Center.
  •  
  •  NEWS    MERL Researchers give a Tutorial Talk on Quantum Machine Learning for Sensing and Communications at IEEE VCC
    Date: November 28, 2023 - November 30, 2023
    Where: Virtual
    MERL Contacts: Toshiaki Koike-Akino; Pu (Perry) Wang
    Research Areas: Artificial Intelligence, Communications, Computational Sensing, Machine Learning, Signal Processing
    Brief
    • On November 28, 2023, MERL researchers Toshiaki Koike-Akino and Pu (Perry) Wang will give a 3-hour tutorial presentation at the first IEEE Virtual Conference on Communications (VCC). The talk, titled "Post-Deep Learning Era: Emerging Quantum Machine Learning for Sensing and Communications," addresses recent trends, challenges, and advances in sensing and communications. P. Wang presents use cases, industry trends, signal processing, and deep learning for Wi-Fi integrated sensing and communications (ISAC), while T. Koike-Akino discusses the future of deep learning, giving a comprehensive overview of artificial intelligence (AI) technologies, natural computing, emerging quantum AI, and their diverse applications. The tutorial is conducted virtually.

      IEEE VCC is a new fully virtual conference launched from the IEEE Communications Society, gathering researchers from academia and industry who are unable to travel but wish to present their recent scientific results and engage in conducive interactive discussions with fellow researchers working in their fields. It is designed to resolve potential hardship such as pandemic restrictions, visa issues, travel problems, or financial difficulties.
  •  
  •  NEWS    MERL Researchers gave a Tutorial Talk on Quantum Machine Learning for Sensing and Communications at IEEE GLOBECOM
    Date: December 8, 2022
    MERL Contacts: Toshiaki Koike-Akino; Pu (Perry) Wang
    Research Areas: Artificial Intelligence, Communications, Computational Sensing, Machine Learning, Signal Processing
    Brief
    • On December 8, 2022, MERL researchers Toshiaki Koike-Akino and Pu (Perry) Wang gave a 3.5-hour tutorial presentation at the IEEE Global Communications Conference (GLOBECOM). The talk, titled "Post-Deep Learning Era: Emerging Quantum Machine Learning for Sensing and Communications," addressed recent trends, challenges, and advances in sensing and communications. P. Wang presented on use cases, industry trends, signal processing, and deep learning for Wi-Fi integrated sensing and communications (ISAC), while T. Koike-Akino discussed the future of deep learning, giving a comprehensive overview of artificial intelligence (AI) technologies, natural computing, emerging quantum AI, and their diverse applications. The tutorial was conducted remotely. MERL's quantum AI technology was partly reported in the recent press release (https://us.mitsubishielectric.com/en/news/releases/global/2022/1202-a/index.html).

      The IEEE GLOBECOM is a highly anticipated event for researchers and industry professionals in the field of communications. Organized by the IEEE Communications Society, the flagship conference is known for its focus on driving innovation in all aspects of the field. Each year, over 3,000 scientific researchers submit proposals for program sessions at the annual conference. The theme of this year's conference was "Accelerating the Digital Transformation through Smart Communications," and featured a comprehensive technical program with 13 symposia, various tutorials and workshops.
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  •  NEWS    MERL's Quantum Machine Learning Technology Featured in Mitsubishi Electric Corporation Press Release
    Date: December 2, 2022
    MERL Contacts: Toshiaki Koike-Akino; Kieran Parsons; Pu (Perry) Wang; Ye Wang
    Research Areas: Artificial Intelligence, Computational Sensing, Machine Learning, Signal Processing, Human-Computer Interaction
    Brief
    • Mitsubishi Electric Corporation announced its development of a quantum artificial intelligence (AI) technology that automatically optimizes inference models to downsize the scale of computation with quantum neural networks. The new quantum AI technology can be integrated with classical machine learning frameworks for diverse solutions.

      Mitsubishi Electric has confirmed that the technology can be incorporated in the world's first applications for terahertz (THz) imaging, Wi-Fi indoor monitoring, compressed sensing, and brain-computer interfaces. The technology is based on recent research by MERL's Connectivity & Information Processing team and Computational Sensing team.

      Mitsubishi Electric's new quantum machine learning (QML) technology realizes compact inference models by fully exploiting the enormous capacity of quantum computers to express exponentially larger-state space with the number of quantum bits (qubits). In a hybrid combination of both quantum and classical AI, the technology can compensate for limitations of classical AI to achieve superior performance while significantly downsizing the scale of AI models, even when using limited data.
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MERL Publications

  •  Koike-Akino, T., Cevher, V., "Quantum-PEFT: Ultra Parameter-Efficient Fine-Tuning", International Conference on Machine Learning (ICML), July 2024.
    BibTeX TR2024-101 PDF Presentation
    • @inproceedings{Koike-Akino2024jul,
    • author = {{Koike-Akino, Toshiaki and Cevher, Volkan}},
    • title = {Quantum-PEFT: Ultra Parameter-Efficient Fine-Tuning},
    • booktitle = {International Conference on Machine Learning (ICML)},
    • year = 2024,
    • month = jul,
    • url = {https://www.merl.com/publications/TR2024-101}
    • }
  •  Kojima, K., Koike-Akino, T., "Universal Photonic Neural Networks with Quantum-Free Data Reuploading", SPIE Photonics for Quantum, DOI: 10.1117/​12.3023231, June 2024.
    BibTeX TR2024-074 PDF
    • @inproceedings{Kojima2024jun,
    • author = {Kojima, Keisuke and Koike-Akino, Toshiaki}},
    • title = {Universal Photonic Neural Networks with Quantum-Free Data Reuploading},
    • booktitle = {SPIE Photonics for Quantum},
    • year = 2024,
    • month = jun,
    • publisher = {SPIE},
    • doi = {10.1117/12.3023231},
    • url = {https://www.merl.com/publications/TR2024-074}
    • }
  •  Ahmed, M.R., Koike-Akino, T., Parsons, K., Wang, Y., "AutoHLS: Learning to Accelerate Design Space Exploration for HLS Designs", International Midwest Symposium on Circuits and Systems (MWSCAS), DOI: 10.1109/​MWSCAS57524.2023.10405914, August 2023.
    BibTeX TR2023-097 PDF
    • @inproceedings{Ahmed2023aug2,
    • author = {Ahmed, Md Rubel and Koike-Akino, Toshiaki and Parsons, Kieran and Wang, Ye},
    • title = {AutoHLS: Learning to Accelerate Design Space Exploration for HLS Designs},
    • booktitle = {International Midwest Symposium on Circuits and Systems (MWSCAS)},
    • year = 2023,
    • month = aug,
    • publisher = {IEEE},
    • doi = {10.1109/MWSCAS57524.2023.10405914},
    • issn = {1558-3899},
    • isbn = {979-8-3503-0210-3},
    • url = {https://www.merl.com/publications/TR2023-097}
    • }
  •  Koike-Akino, T., Wang, Y., "quEEGNet: Quantum AI for Biosignal Processing", IEEE Conference on Biomedical and Health Informatics (BHI), DOI: 10.1109/​BHI56158.2022.9926814, September 2022.
    BibTeX TR2022-121 PDF Video Presentation
    • @inproceedings{Koike-Akino2022sep,
    • author = {Koike-Akino, Toshiaki and Wang, Ye},
    • title = {quEEGNet: Quantum AI for Biosignal Processing},
    • booktitle = {IEEE Conference on Biomedical and Health Informatics (BHI)},
    • year = 2022,
    • month = sep,
    • publisher = {IEEE},
    • doi = {10.1109/BHI56158.2022.9926814},
    • issn = {2641-3604},
    • isbn = {978-1-6654-8791-7},
    • url = {https://www.merl.com/publications/TR2022-121}
    • }
  •  Koike-Akino, T., Wang, P., Yamashita, G., Tsujita, W., Nakajima, M., "Quantum Feature Extraction for THz Multi-Layer Imaging", International Conference on Infrared, Millimeter, and Terahertz Waves (IRMMW-THz), DOI: 10.1109/​IRMMW-THz50927.2022.9896037, August 2022.
    BibTeX TR2022-110 PDF Video Presentation
    • @inproceedings{Koike-Akino2022aug,
    • author = {Koike-Akino, Toshiaki and Wang, Pu and Yamashita, Genki and Tsujita, Wataru and Nakajima, M.},
    • title = {Quantum Feature Extraction for THz Multi-Layer Imaging},
    • booktitle = {International Conference on Infrared, Millimeter, and Terahertz Waves (IRMMW-THz)},
    • year = 2022,
    • month = aug,
    • publisher = {IEEE},
    • doi = {10.1109/IRMMW-THz50927.2022.9896037},
    • issn = {2162-2035},
    • isbn = {978-1-7281-9427-1},
    • url = {https://www.merl.com/publications/TR2022-110}
    • }
  •  Koike-Akino, T., Wang, P., Wang, Y., "AutoQML: Automated Quantum Machine Learning for Wi-Fi Integrated Sensing and Communications", IEEE Sensor Array and Multichannel Signal Processing Workshop (SAM), DOI: 10.1109/​SAM53842.2022.9827846, June 2022.
    BibTeX TR2022-068 PDF Video Presentation
    • @inproceedings{Koike-Akino2022jun,
    • author = {Koike-Akino, Toshiaki and Wang, Pu and Wang, Ye},
    • title = {AutoQML: Automated Quantum Machine Learning for Wi-Fi Integrated Sensing and Communications},
    • booktitle = {IEEE Sensor Array and Multichannel Signal Processing Workshop (SAM)},
    • year = 2022,
    • month = jun,
    • doi = {10.1109/SAM53842.2022.9827846},
    • url = {https://www.merl.com/publications/TR2022-068}
    • }
  •  Liu, B., Koike-Akino, T., Wang, Y., Parsons, K., "Variational Quantum Compressed Sensing for Joint User and Channel State Acquisition in Grant-Free Device Access Systems", IEEE International Conference on Communications (ICC), DOI: 10.1109/​ICC45855.2022.9838445, May 2022.
    BibTeX TR2022-052 PDF Video Presentation
    • @inproceedings{Liu2022may3,
    • author = {Liu, Bryan and Koike-Akino, Toshiaki and Wang, Ye and Parsons, Kieran},
    • title = {Variational Quantum Compressed Sensing for Joint User and Channel State Acquisition in Grant-Free Device Access Systems},
    • booktitle = {IEEE International Conference on Communications (ICC)},
    • year = 2022,
    • month = may,
    • publisher = {IEEE},
    • doi = {10.1109/ICC45855.2022.9838445},
    • issn = {1938-1883},
    • isbn = {978-1-5386-8347-7},
    • url = {https://www.merl.com/publications/TR2022-052}
    • }
  •  Koike-Akino, T., Wang, P., Wang, Y., "Quantum Transfer Learning for Wi-Fi Sensing", IEEE International Conference on Communications (ICC), DOI: 10.1109/​ICC45855.2022.9839011, May 2022.
    BibTeX TR2022-044 PDF Video Presentation
    • @inproceedings{Koike-Akino2022may2,
    • author = {Koike-Akino, Toshiaki and Wang, Pu and Wang, Ye},
    • title = {Quantum Transfer Learning for Wi-Fi Sensing},
    • booktitle = {IEEE International Conference on Communications (ICC)},
    • year = 2022,
    • month = may,
    • doi = {10.1109/ICC45855.2022.9839011},
    • issn = {1938-1883},
    • isbn = {978-1-5386-8347-7},
    • url = {https://www.merl.com/publications/TR2022-044}
    • }
  •  Koike-Akino, T., Matsumine, T., Wang, Y., Millar, D.S., Kojima, K., Parsons, K., "Variational Quantum Demodulation for Coherent Optical Multi-Dimensional QAM", Optical Fiber Communication Conference and Exposition and the National Fiber Optic Engineers Conference (OFC/NFOEC), DOI: 10.1364/​OFC.2020.T3D.6, March 2020, pp. T3D.6.
    BibTeX TR2020-028 PDF Video Presentation
    • @inproceedings{Koike-Akino2020mar2,
    • author = {Koike-Akino, Toshiaki and Matsumine, Toshiki and Wang, Ye and Millar, David S. and Kojima, Keisuke and Parsons, Kieran},
    • title = {Variational Quantum Demodulation for Coherent Optical Multi-Dimensional QAM},
    • booktitle = {Optical Fiber Communication Conference and Exposition and the National Fiber Optic Engineers Conference (OFC/NFOEC)},
    • year = 2020,
    • pages = {T3D.6},
    • month = mar,
    • publisher = {OSA},
    • doi = {10.1364/OFC.2020.T3D.6},
    • isbn = {978-1-943580-71-2},
    • url = {https://www.merl.com/publications/TR2020-028}
    • }
  •  Matsumine, T., Koike-Akino, T., Wang, Y., "Channel Decoding with Quantum Approximate Optimization Algorithm", IEEE International Symposium on Information Theory (ISIT), DOI: 10.1109/​ISIT.2019.8849710, July 2019.
    BibTeX TR2019-071 PDF Presentation
    • @inproceedings{Matsumine2019jul,
    • author = {Matsumine, Toshiki and Koike-Akino, Toshiaki and Wang, Ye},
    • title = {Channel Decoding with Quantum Approximate Optimization Algorithm},
    • booktitle = {IEEE International Symposium on Information Theory (ISIT)},
    • year = 2019,
    • month = jul,
    • doi = {10.1109/ISIT.2019.8849710},
    • issn = {2157-8117},
    • isbn = {978-1-5386-9291-2},
    • url = {https://www.merl.com/publications/TR2019-071}
    • }