Psiket Webinars

For decades, photosynthesis was considered a purely classical phenomenon. However, a series of experiments in the early 2000s suggested that quantum effects may play a role in the highly efficient energy transfer processes occurring during the initial stages of photosynthesis in plants and bacteria. These findings sparked the emergence of a new research field known today as quantum biology. In this webinar, we will explore the quantum effects that have been observed, as well as the key questions and controversies that remain under debate in this fascinating field.

Fiber optic sensors, as one of the most significant achievements of photonics technology, have played a remarkable role in the development of measurement, monitoring, and diagnostic systems in recent years. Due to their outstanding characteristics, including high sensitivity and accuracy, immunity to electromagnetic interference, distributed sensing capability, compact size, flexibility, and the ability to operate under harsh environmental conditions, these sensors have become an ideal solution for industrial and medical applications. In this presentation, the fundamental operating principles and classification of fiber optic sensors will be briefly introduced. Subsequently, the most important applications of this technology in industry, including structural health monitoring, oil and gas industries, power plants, aerospace, transportation, and smart manufacturing, will be reviewed. Furthermore, emerging medical applications of these sensors, including vital sign monitoring, minimally invasive instruments, medical imaging, disease diagnosis, and intelligent therapeutic devices, will be discussed.

In the previous webinar, we discussed quantum machine learning—an emerging field that has attracted significant attention in recent years. On the one hand, interest in quantum technologies is growing, and these technologies are gradually moving beyond the purely scientific sphere toward practical applications. On the other hand, machine learning and artificial intelligence have become among the most important technologies of the future, and many companies need to invest in these areas in order to remain competitive. The convergence of these two fields—quantum technology and machine learning—has brought quantum machine learning into the spotlight in the IT industry, although this attention does not always align with the scientific and research challenges of the field. In this talk, we will continue the discussions from the previous webinar and examine the path quantum machine learning has taken in recent years: which methods have received less attention, and which approaches have been taken more seriously. We will also revisit the comparison between classical and quantum machine learning and explore the prospects for quantum machine learning entering industry.

What if someone tried to read your message while it was being transmitted—and you could detect it immediately? This is exactly the idea behind “quantum secure communication,” which we will explore in this webinar.

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.

Reducing the number of assumptions in an axiomatic theory is a natural route toward constructing more general frameworks. Historically, the discovery of non-Euclidean geometries provides a well-known example of this approach. Recently, we have applied this strategy to quantum mechanics by abandoning the assumption that the position of a particle must be described by a strictly self-adjoint operator. The remaining principles of quantum theory, together with Galilean symmetry, lead to a generalized quantum framework characterized by a single free parameter with the dimension of length. This mathematical structure preserves Galilean invariance and gives rise to a generalized uncertainty relation that guarantees the existence of a minimal length—an expectation shared by several approaches to quantum gravity. Remarkably, this non-Heisenberg quantum theory yields, without assuming any *a priori* commutation relations, a modified Heisenberg-type uncertainty relation of the form [ \Delta x , \Delta p \geq \sqrt{\frac{\hbar^2}{4} + l_0^2 (\Delta p)^2}, ] which explicitly implies a minimum position uncertainty equal to ( l_0 ). In the limit ( l_0 \to 0 ), the theory smoothly reduces to standard quantum mechanics. By comparing the predictions of this framework with observational data—including the first longitudinal normal modes of the resonant-bar gravitational wave detector **AURIGA** and the (1S!-!2S) transition in hydrogen—we derive upper bounds on the value of the length parameter ( l_0 ).

Thermodynamics holds a very unique and complex position among the branches of physics. In this lecture, after exploring some historical and conceptual aspects, we will discuss how fundamental research in thermodynamics may lead to significant advancements in physics. These advancements could range from theoretical aspects of physics, such as quantum gravity, to complex quantum technologies in the modern era.

In this talk, we begin by introducing a general framework for quantifying the intrinsic quantum randomness. This framework allows us to compute the maximum guessing power of an eavesdropper, while fully accounting for the possibility that they may share classical or quantum correlations with the input state or even with the measurement itself. We then compute the intrinsic randomness of a mixed state and identify the measurements that optimize this quantity. Next, we derive the randomness generated by noisy measurements, along with the corresponding optimal states. Finally, we show that the simultaneous presence of noise in both the state and the measurement can significantly enhance the eavesdropper’s ability to guess the outcomes.

A quantum computer serves as a platform for executing quantum algorithms that exhibit significant advantages over classical computational methods in solving certain problems. Among the various proposed physical implementations, photonic technology has emerged as a strategic pathway toward achieving universal quantum processors, thanks to its inherent compatibility with quantum communication and sensing systems. In the field of photonic quantum computing, two promising approaches have attracted particular attention: the continuous-variable (CV) approach, which operates based on the measurement-based computational model, and the single-photon approach, which utilizes the fusion-based computational model. This talk will explore the physical and computational principles underlying each of these approaches, highlighting their strengths, implementation challenges, and scalability potential within photonic platforms. The goal of this presentation is to provide a comprehensive overview of the diverse development pathways for photonic quantum computing systems and to outline the future prospects of this transformative technology.