Friday, August 30th, 2024

Enjoy a nice cup of freshly brewed quantum news ☕️ 

Today’s issue includes:

• Researchers from Princeton University, IBM Quantum, and RTX BBN Technologies developed a quantum machine learning algorithm, NISQRC, that overcomes the coherence time limitations of current quantum hardware and processes temporal data.

• Researchers from Cornell University, the University of Maryland, NTT Research Inc., and MIT demonstrated a quantum reservoir computing approach using a superconducting microwave circuit to process and classify analog microwave signals with high accuracy.

• GraphiQ, is an open-source framework designed by Quantum Bridge Technologies, the University of Toronto, the University of Waterloo, and Ki3 Photonics for the efficient generation of photonic graph states.

And even more research, news, & events within quantum.

🧠 IBM Quantum & Princeton Quantum Algorithm NISQRC Overcomes Coherence Limits

QUICK BYTE: Researchers from Princeton University, IBM Quantum, and RTX BBN Technologies developed a quantum machine learning algorithm, NISQRC, that overcomes the coherence time limitations of current quantum hardware and processes temporal data.

DETAILS: 

• The NISQRC algorithm addresses the coherence time barrier, a major limitation in quantum computing, to process long temporal data streams. Unlike previous quantum machine learning methods constrained by coherence time, NISQRC uses mid-circuit measurements and deterministic reset operations to maintain memory over long periods.

• The team developed and validated the NISQRC algorithm on a 7-qubit quantum processor, showing it can recover signals well beyond the system's coherence time. This algorithm reliably processed temporal data without needing quantum error correction.

• NISQRC enables more advanced quantum computing applications, including real-time data processing in communication systems and machine learning tasks with weak quantum signals.

♨️ Quantum Reservoir Computing For Real-Time Signal Processing with Superconducting Circuits

QUICK BYTE: Researchers from Cornell University, the University of Maryland, NTT Research Inc., and MIT demonstrated a quantum reservoir computing approach using a superconducting microwave circuit to process and classify analog microwave signals with high accuracy.

DETAILS: 

• The QRC approach enables highly accurate classification of microwave signals, effectively addressing challenges in processing continuous-time data, which is crucial for advancing practical quantum computing applications.

• Unlike traditional quantum machine learning methods that rely on discretized input, this analog method processes signals in real-time, offering a significant advantage in handling fast temporal variations.

• Researchers used a superconducting microwave circuit as a quantum reservoir to classify time-independent signals, RF modulation schemes, and filtered noise signals with over 90% accuracy, highlighting its potential for quantum sensing and real-time signal analysis.

🛠️ GraphiQ: A New Tool for Optimizing Photonic Graph States in Quantum Computing

QUICK BYTE: GraphiQ, is an open-source framework designed by Quantum Bridge Technologies, the University of Toronto, the University of Waterloo, and Ki3 Photonics for the efficient generation of photonic graph states.

DETAILS: 

• GraphiQ provides a practical and resource-efficient solution for generating photonic graph states, which are crucial for quantum computing and communication technologies.

• Unlike existing tools, GraphiQ is specifically designed for photonic graph state generation, supporting hybrid photon-emitter platforms and accounting for real-world experimental constraints.

• Developed as a versatile Python-based software, GraphiQ simulates, evaluates, and optimizes quantum circuits, improving the design process and enabling the generation of large, entangled graph states under realistic conditions.

🧊 Oxford Instruments NanoScience has joined Rigetti’s Novera QPU Partner Program. As a cryogenics partner, Oxford Instruments will contribute its expertise in cryogenic systems to maintain the ultra-low temperatures required for quantum computing. This collaboration will upon the companies' long-standing partnership, in order to advance on-premises quantum computing by integrating Oxford's Proteox cryogenic systems with Rigetti's Novera QPU.

⏰ Researchers at NIST, in collaboration with the University of Colorado and Pennsylvania State University, have developed a new sub-recoil Sisyphus cooling technique that improves the precision of atomic clocks for quantum metrology. This method, initially applied to ytterbium optical lattice clocks, involves strategically engineering energy shifts to cool atoms more effectively, improving the accuracy of high-precision clock spectroscopy. The technique is also relevant to quantum information processing and quantum computing, where precise control and cooling of atomic ensembles is essential.

🖥️ The Novo Nordisk Foundation, in collaboration with the University of Copenhagen, has launched an initiative to develop Denmark's first fully functional quantum computer by 2034. CEO Mads Krogsgaard Thomsen highlights in a recent video quantum computing's potential role in drug discovery and in providing insights into human and planetary health. The initiative will build on Copenhagen's quantum mechanics heritage and international collaboration.

🧪 Silicon carbide is emerging as a promising material platform for quantum photonic integrated circuits due to its unique properties, including both second-order and third-order nonlinearity. The material has demonstrated potential in generating high-performance quantum light sources, relevant for applications in quantum communication, sensing, and computing. Recent research has shown that SiC can produce strongly correlated photon pairs with high fidelity, making it a viable candidate for future quantum information systems. As material quality and nanofabrication technology advance, SiC is expected to become a leading platform for integrated quantum technologies.

🕸️ Research from Duke University and the University of Maryland demonstrates fast photon-mediated entanglement between trapped barium ion qubits, achieving a high entanglement rate of 250 entangled states per second with a fidelity exceeding 93%. By integrating an ytterbium ion for sympathetic cooling, the researchers eliminated the need for recooling interruptions, improving the continuous entanglement rate. This may be applied to scalable quantum networks by generating high-fidelity entanglement between quantum processing nodes.

LISTEN

Because it’s Friday, some inspirational, save-the-world-with-science tunes to carry you into the weekend:

ENJOY

Optical illusions, quantum mechanics, and neural networks seem like an odd trio, yet a new study combines them in intriguing ways. By leveraging quantum tunneling, a neural network was designed to "see" optical illusions much like humans do, even outperforming larger conventional networks in some cases. This research raises questions about whether AI can ever truly mimic human cognition and hints at the potential of quantum physics to deepen our understanding of perception, behavior, and even consciousness.

WATCH

This documentary on how technological innovation is shaping our future, featuring Phasecraft co-founders Toby Cubitt and Ashley Montanaro:

• On Monday, September 2nd, Washington DC Quantum Computing Meetup is hosting Quantum Computing in Finance—virtual

• On Monday, September 2nd, TQN Quantum Safe Transition Working Group is hosting Zero Trust by QryptoCyber—virtual

• On Thursday, September 5th, paper submissions are due for the 23rd International Conference on Machine Learning and Applications.