AI Applications in Quantum Sensor Technology

❓ Ever wondered how Neural Networks (NNs) could revolutionize #quantum research? #NeuralNetworks aren't just transforming #AI —they're also pivotal in the quantum realm! In the work entitled "Parameter Estimation by Learning Quantum Correlations in Continuous Photon-Counting Data Using Neural Networks." Quantinuum proudly collaborated with global partners, such as the Universidad Autónoma de Madrid, Chalmers University of Technology, and the University of Michigan, uniting expertise from every corner of the world. 🌍 https://lnkd.in/gj8qttdN 🔍 Key Findings: 1️⃣ The study introduces a novel inference method employing artificial neural networks for quantum probe parameter estimation. 2️⃣ This method leverages quantum correlations in discrete photon-counting data, offering a fresh perspective compared to existing techniques focusing on diffusive signals. 3️⃣ The approach achieves performance on par with Bayesian inference - renowned for its optimal information retrieval capability - yet does so at a fraction of the computational cost. 4️⃣ Beyond efficiency, the method stands robust against imperfections in measurement and training data. 5️⃣ Potential applications span from quantum sensing and imaging to precise calibration tasks in laboratory setups. 🤔 Curious About the Unknowns? The authors are sharing EVERYTHING on Zenodo! 🎉 The codes used to generate these results, including the proposed NN architectures as TensorFlow models, are available here https://lnkd.in/gVdzJycM as well as all the data necessary to reproduce the results openly available here: https://lnkd.in/gVdzJycM Enrico Rinaldi, Manuel González Lastre, Sergio Garcia Herreros, Shahnawaz Ahmed, Maryam Khanahmadi, Franco Nori, and Carlos Sánchez Muñoz

*How can you use quantum neural networks (QNNs) to gain a quantum advantage on classical data?* We propose to use QNNs (and other quantum algorithms, including quantum signal processing) to process data in quantum sensors. Attempts over the past 7+ years to find near-term practical applications of quantum neural networks on classical data have faced a variety of challenges, including: if the classical data is small enough to be able to load into a quantum computer, then it has (empirically) always been possible to address the same problem with a classical neural network - and without the downsides of quantum computing with current (noisy) hardware. Rather than trying to tackle problems in the setting where the classical data originates from a classical computer's memory, we switch the framing of the problem slightly, but in a way that makes a huge difference: what if we use QNNs to perform classification on classical but a priori _unknown_ data? What do we mean by _unknown_ data? A quantum sensor senses a classical signal that is unknown to us, but is ultimately classical. We can use a QNN to help reveal a _trained nonlinear function_ of the unknown classical signal. One of the examples we have explored shows how you can gain an advantage where both the quantum sensing and quantum computing are performed by a single qubit! If you already knew the classical signal, there would be no hope for a quantum advantage (simulating a single qubit is of course trivial), but in the sensing setting we don't know the signal a priori. We have been able to show it is possible to gain a quantum computational-sensing advantage using quantum signal processing (QSP) treated as a QNN, versus first using a conventional quantum sensor and then postprocessing to compute the nonlinear classification function classically. By performing an approximation of the nonlinear classification function in the quantum system before measurement, the quantum sampling noise is greatly reduced: measurements of the system yield 0 or 1 with high probability depending on which of two classes the signal was in. We have a preprint on the arXiv showing various schemes for quantum computational sensing with a small number of qubits and/or bosonic modes, tested on a variety of binary and multiclass classification problems: https://lnkd.in/enQxFDNt I am optimistic about the prospects for experimental proof-of-concept demonstrations given the modest quantum resources required (down to just a single qubit and a not-particularly-deep circuit). Congratulations to Saeed Khan and Sridhar Prabhu, as well as Logan Wright!

💡AI Can Now Feel Surfaces -- via Quantum Mechanics💡 AI technologies have already advanced in seeing, conversing, calculating, and creating. However, one area that AI hasn't mastered yet is touch—the ability to "feel" and discern surface textures. That's changing thanks to the research from the Center for Quantum Science and Engineering (CQSE) at Stevens Institute of Technology. 🔬 Marrying Quantum Mechanics and AI Physics professor Yong Meng Sua, along with CQSE Director Yuping Huang and doctoral candidates Daniel Tafone and Luke McEvoy, have developed a quantum-lab setup that combines photon-firing scanning lasers with advanced AI algorithms. This system allows AI to accurately detect and measure surface topography by interpreting speckle noise—normally considered detrimental in imaging—as valuable data. "This is a marriage of AI and quantum," explains Tafone. ⚙️ How It Works Photon Firing Scanning Laser: Pulses a specially created beam of light at a surface. Speckle Noise Utilization: Reflected photons carry speckle noise, which the AI interprets to discern surface texture. High Precision: Achieved an accuracy within 4 microns—comparable to the best industrial profilometers. 🚀 Potential Applications 1️⃣Medical Diagnostics: Enhances the detection of skin cancers by measuring tiny differences in mole roughness, aiding in distinguishing benign conditions from malignant melanomas. "Tiny differences in mole roughness, too small to see with the human eye but measurable with our proposed quantum system, could differentiate between those conditions," explains Huang. 2️⃣ Manufacturing Quality Control: Detects minuscule defects in components that could lead to mechanical failures, ensuring product reliability and safety. 3️⃣ Enhanced LiDAR Technology: Improves devices like autonomous cars, smartphones, and robots by adding precise surface property measurements at very small scales. 🌐 Enriching AI's Sensory Capabilities Since LiDAR technology is already widely used, this method could significantly enhance its functionality. "Our method enriches their capabilities with surface property measurement at very small scales," Huang concludes. 📄 Original Paper: https://lnkd.in/gSxfYaK3 Thomas J. White IV #AI #QuantumTechnology #MachineLearning #SurfaceMeasurement #Innovation #Research #LiDAR #MedicalDiagnostics