Psiket Webinars

This talk explores the major frontiers of the Second Quantum Revolution from a quantum hardware perspective, focusing on how the future of quantum technologies will be shaped. I present a forward-looking view of how the three foundational pillars—quantum computation, quantum communication, and quantum sensing—are co-evolving toward scalable and practically deployable quantum infrastructures. Within this framework, I highlight selected examples from my own work, including spin–photon interfaces in silicon for scalable quantum computing architectures and quantum networking, erbium-doped nanoparticle single-photon sources for telecom-band quantum communication, and a levitated superconducting particle coupled to a superconducting qubit as a platform for macroscopic quantum control and ultra-sensitive quantum sensing.

Although quantum cryptography offers extremely high security, it still faces two major challenges: transmission distance and quantum key rate. To improve this technology, we must employ powerful theoretical and computational tools—one of which is tensor networks, particularly the Multi-scale Entanglement Renormalization Ansatz (MERA) architecture. MERA provides an efficient structure for the optimal encoding of quantum states of matter, especially in critical states. In this seminar, we will demonstrate how MERA can serve as a novel mathematical and algorithmic language, opening new horizons in quantum cryptography. Finally, we will discuss the role of trapped-ion systems as one of the leading experimental platforms for implementing such networks.

Quantum dynamical simulations are computationally demanding, particularly for large systems and long-time evolutions. While quantum computing holds the potential to revolutionize this field, current quantum computers remain limited in scale, and algorithm development continues to present significant challenges. Artificial Intelligence (AI) has demonstrated remarkable success in diverse applications, including complex problem-solving and large-scale data analysis. Recent advances in AI, such as NVIDIA's universal function approximators, have enabled the development of AI applications capable of learning and replicating complex algorithmic behaviors. We propose leveraging AI to accelerate chemical design and quantum dynamical simulations. These AI applications can be trained on the inputs and outputs of quantum chemistry and dynamics algorithms, effectively replacing computationally expensive components. Furthermore, they can learn the scaling behavior of these algorithms, enabling extrapolation to larger systems. We present a proof-of-concept which demonstrates the potential of this approach. We successfully developed an AI application capable of generating vibrational wavefunctions for small molecules, such as CO₂, using only frequency and potential parameters as input.  This method bypasses the need for computationally intensive variational algorithms. This work highlights the promising synergy between quantum computing and AI in advancing the field of quantum dynamical simulations.

Among several approaches for building quantum bits, superconducting qubit technology has made remarkable progress in recent years. Thanks to investments from governments and the private sector, this technology has reached a level of maturity where several producers around the world are now offering their products in the market. In this presentation, after reviewing the fundamentals of this technology, we will examine its current state, along with the challenges and prospects ahead.

Quantum machine learning is an emerging field that has raised high expectations. On the one hand, there is a growing commercial interest in quantum technologies that are at a turning point, moving towards practical tools for implementing quantum algorithms and moving beyond the purely scientific realm. On the other hand, machine learning, along with artificial intelligence, has been introduced as a central technology of the future that companies are forced to invest in to remain competitive. The combination of these two worlds inevitably attracts significant interest in quantum machine learning in the IT industry, an interest that is not necessarily aligned with the scientific challenges that researchers are only just beginning to explore.

Quantum computing is a new branch of computer science that uses the principles of quantum physics to process information. This field has attracted a lot of attention due to its unique capabilities in solving complex problems. The two main models in quantum computing are orbital models and adiabatic models. Orbital models are themselves divided into two main branches: the gate-based orbital model, in which information is processed using quantum gates, and the measurement-based orbital model, in which calculations are performed through quantum measurements. On the other hand, the adiabatic model is more often used for optimization problems, and the quantum system gradually reaches its optimal state. In this presentation, we will examine these models, their differences, and their applications in quantum computing.

Photonic quantum computers are single-purpose quantum computers that have recently attracted the attention of some start-up companies in the world. These computers are able to solve a very difficult problem, which researchers generally believe is beyond the reach of classical computers. Computing with such computers is different from computing with all-purpose, programmable quantum computers. In this presentation, we will first discuss in detail the basis of computing with photonic quantum computers, and then we will briefly mention the state of existing hardware of photonic quantum computers in the world. At the end, we examine the value of investing in such quantum computers for the country of Iran.

In this presentation, after explaining the basics of quantum computers, we will focus on superconducting quantum computers. First, we introduce the principles and main elements of circuit quantum electrodynamics (cQED) as a basis for understanding superconducting quantum computer technology. Then, we discuss the concept of resonant waveguides and artificial atoms, which are the cornerstones of cQED, and show how these structures can exhibit quantum behavior and revolutionize quantum computing and information processing. Also, we compare cQED with quantum cavity electrodynamics (CQED) and highlight its advantages. Next, we will examine superconducting quantum computers. This platform provides the possibility of engineering qubits by changing the geometry and topology of superconducting circuits and ease of control, reading and adjustability. After introducing the basics of gate-based quantum computing, we will examine the quantum layer and confirm the DiVincenzo criteria in the context of superconducting quantum computers, and we will introduce the types of superconducting qubits, the methods of applying single and double qubit gates and their reading. By following this presentation, you will gain a comprehensive understanding of superconducting qubits and the main ideas in their control and readout.

Based on the laws of quantum mechanics, future quantum computers will perform dramatically better than their conventional classical counterparts. However, large-scale universal quantum computers have yet to be built. Boson sampling, which is designed based on a linear optical network, has been considered as a fast way to demonstrate quantum superiority. While it is difficult to efficiently simulate boson sampling without noise using a classical computer, current boson sampling experiments are accompanied by loss and noise. Classical algorithms are presented for simulating Gaussian boson sampling experiments, where the sampling complexity can be significantly reduced by increasing the photon loss rate, and by simulating the true-basis distribution, they challenge claims of experimental quantum superiority.