Scientists Unleash Quantum Computing Potential Beyond Fundamental Physics

In pursuit of quantum innovation, the Superconducting Quantum Materials and Systems Center (SQMS) is pushing the boundaries of machine learning optimization, sensing beyond fundamental physics applications, and exploring the potential of quantum computing. Led by Davide Venturelli and Adrian Lupascu, the SQMS team comprises experts from various institutions collaborating with the US Department of Energy Office of Science National Quantum Information Science Research Centers.

SQMS is at the forefront of developing hybrid systems formed by levitated superconducting spheres and qubits, which have shown great promise in quantum sensing applications. The team is also exploring the intersection of quantum computing and machine learning, designing trainable quantum neural network protocols for real-world applications such as healthcare and sustainability.

With a focus on collaborative projects and partnerships, SQMS aims to develop capabilities that can impact societal advances beyond its core domains of High-Energy Physics and condensed matter physics. Its research has the potential to unlock new possibilities for quantum solutions in various domains, including healthcare, aerospace, and sustainability.

What is SQMS and its Research Focus?

SQMS, or Superconducting Quantum Materials and Systems Center, is a research center focused on developing quantum technologies. The center’s primary goal is to create capabilities that can be applied in various domains beyond the core areas of High-Energy Physics (HEP) and condensed matter physics. SQMS aims to leverage its expertise and research advances from other projects to develop new sensing and machine-learning optimization platforms.

SQMS has a team of researchers, including Davide Venturelli, Adrian Lupascu, Maxime Dupont, Tanay Roy, Doga Murat Kurkcuoglu, Alessandro Berti, Giuseppe Clemente, Gianna Del Corso, Giacomo Antonioli, Alessandro Poggiali, Nishchay Suri, Nicholas Bornman, Riccardo Lattanzi, José Cruz Serrallés, Lorenzo Maccone, Zhen Liu, Mustafa Bal, and Si lvia Zorzetti. These researchers are working on various projects, including sensing with hybrid systems formed of superconducting levitated spheres and qubits.

The SQMS team is led by experts in quantum physics and materials science. They have published numerous papers on their research, including studies on the use of levitated superconducting spheres for gravimetry and dark matter detection. These studies demonstrate the potential of SQMS’s research to impact societal advances beyond the core domain of HEP and condensed matter physics.

Sensing with Hybrid Systems: A New Platform

SQMS researchers have been exploring the possibility of using hybrid systems formed of superconducting levitated spheres and qubits for sensing applications. These systems have shown great promise in detecting subtle changes in their environment, making them ideal for applications such as gravimetry and dark matter detection.

The key to this new platform lies in the unique properties of levitated superconducting spheres at ultralow temperatures. These spheres can exhibit very long relaxation times, which can be leveraged to create susceptible measurement protocols. By coupling a levitated sphere to a superconducting qubit, researchers can enable new measurement protocols that are impossible with traditional sensing methods.

The SQMS team has been working on advanced design and material characterization for this new platform. They have developed expertise in leveraging their research advances from other projects to create novel sensing capabilities. This work has the potential to impact various domains beyond HEP and condensed matter physics, making it a critical area of research for SQMS.

Quantum Sensing: A New Frontier

Quantum sensing is an emerging field that leverages the unique properties of quantum systems to detect subtle changes in their environment. SQMS researchers have been at the forefront of this field, exploring the potential of hybrid systems formed of superconducting levitated spheres and qubits for sensing applications.

The use of levitated superconducting spheres has shown great promise in detecting subtle changes in their environment, making them ideal for applications such as gravimetry and dark matter detection. These systems have been demonstrated to be highly sensitive, potentially detecting impossible changes with traditional sensing methods.

SQMS researchers have published numerous papers in this area, including studies on using levitated superconducting spheres for dark matter detection. These studies demonstrate the potential of SQMS’s research to impact societal advances beyond the core domain of HEP and condensed matter physics.

Machine Learning Optimization: A New Capability

SQMS researchers have been exploring the possibility of using machine learning optimization techniques to improve the performance of quantum systems. This work can potentially create new capabilities that can be applied in various domains beyond HEP and condensed matter physics.

The SQMS team has developed expertise in leveraging their research advances from other projects to create novel machine-learning optimization capabilities. They have published numerous papers on this topic, including studies using trainable quantum neural networks for time series and inverse PDE solving.

This work has the potential to impact various domains beyond HEP and condensed matter physics, making it a critical area of research for SQMS. The team’s expertise in machine learning optimization can be applied to various places, including healthcare, aerospace, and sustainability.

Collaborative Projects: A Key Area of Research

SQMS researchers have been working on collaborative projects that leverage the center’s underdeveloped hardware, such as QPUs and sensors, to demonstrate utility in the journey towards quantum advantage. These projects aim to develop capabilities beyond quantum simulation, machine learning optimization, and sensing.

The SQMS team has identified external partners and projects with similar interests and complementary subject-domain expertise. They have been working on integrating these external partners and projects into the center’s research agenda, creating a collaborative ecosystem that can drive innovation and impact societal advances.

This work can potentially create new capabilities that can be applied in various domains beyond HEP and condensed matter physics. The SQMS team’s expertise in collaborative research can be applied to multiple areas, including healthcare, aerospace, and sustainability.

Training Quantum Neural Networks: A New Capability

SQMS researchers have been exploring the possibility of using trainable quantum neural networks for time series and inverse PDE solving. This work can potentially create new capabilities that can be applied in various domains beyond HEP and condensed matter physics.

The SQMS team has developed expertise in leveraging research advances from other projects to create novel trainable quantum neural network design, simulation, and testing capabilities. They have published numerous papers on this topic, including studies on the use of transmon arrays and multi cavity SRF processors for time series and inverse PDE solving.

This work has the potential to impact various domains beyond HEP and condensed matter physics, making it a critical area of research for SQMS. The team’s expertise in trainable quantum neural networks can be applied to various areas, including healthcare, aerospace, and sustainability.