Welcome to the Quantum Realm. 

Enjoy today’s breakdown of news, research, & events within quantum.

😴 Just one of those days. Not a firehose.

🗓️ THIS WEEK

Wednesday, June 5 - Friday, June 14 | IBM Quantum Challenge 2024 — Register here!

Saturday, June 15 | Simulation of Quantum Chemistry Hamiltonians with Relativistic Effects w/ Washington DC Quantum Meetup

📰 NEWS QUICK BYTES

👑 Queen Mary University Enhances Quantum Research with Cryogenic Technology: Queen Mary University of London recently upgrades its quantum research with the ProteoxMX dilution refrigerator from Oxford Instruments NanoScience. This tech enables high-precision experiments and complements the Mol Lab's quantum computing research capabilities to attract top talent.

⚡️ Riverlane Joins Quantum Energy Initiative for Sustainable Quantum Computing: Riverlane has joined the Quantum Energy Initiative to focus on reducing quantum computing's environmental impact. They will be working alongside global leaders like Microsoft Azure Quantum and IBM Quantum to establish new standards for the energy use of quantum systems.

⚙️ Strategic Partnership in Semiconductor Integration: EV Group and Fraunhofer IZM-ASSID have partnered to develop bonding and debonding technologies for CMOS and heterogeneous integration, including quantum computing. The goal of this partnership is to advance the manufacturing processes for quantum systems and improve 3D device integration.

🛫 Quantum Computing Accelerates Aerospace Optimization: At the Quantum Computing Summit London, experts highlighted the use of quantum technologies in aerospace optimization. Salvatore Sinno of Unisys noted significant reductions in fuel consumption and emissions, with collaborations advancing these practical applications.

🌴 NBC News Visits Google's Quantum AI Lab: Gadi Schwartz of NBC News recently visited Google's Quantum AI lab in Santa Barbara, California. Experience the tour below:

⛰️ Alberto Prado Talks Quantum Computing and AI at AI Summit London: Alberto Prado, Unilever's global head of R&D digital & partnerships, speaks on the transformative impact of quantum computing on technology and emphasizes the critical need for proper data management to fully harness the potential of AI, at the AI Summit in London. Check out interview below:

☕️ FRESHLY BREWED RESEARCH

Recurrent quantum embedding neural network and its application in vulnerability detection: The recurrent quantum embedding neural network for vulnerability detection combines quantum computing with natural language processing to reduce memory consumption and improve accuracy. By using quantum embeddings and recurrent cells, RQENN achieves significantly lower space complexity and higher detection accuracy compared to both classical and existing quantum NLP methods. Breakdown here.

Experimental Quantum Advantage in the Odd-Cycle Game: The first experimental demonstration of the odd-cycle game achieves a quantum advantage over classical strategies by using entangled ions with high detection efficiency. The experiment achieved a winning probability of 97.8% of the theoretical quantum limit and measured the highest nonlocal content for physically separate devices free of the detection loophole. Breakdown here.

Efficient Quantum Algorithms for Stabilizer Entropies: Practical quantum algorithms to measure stabilizer entropies, which indicate nonstabilizerness or "magic" in quantum states, are developed. These algorithms utilize Bell measurements and require only a linear number of copies and classical computational time. The methods have been experimentally validated on the IonQ quantum computer.

Designing metasurface optical interfaces for solid-state qubits using many-body adjoint shape optimization: A new method for designing metasurface optical interfaces tailored for solid-state qubits is developed. By using many-body adjoint shape optimization, researchers created a structure that enhances photon collection from nitrogen-vacancy centers in diamond. This design optimizes photon coupling into optical fibers and eliminates the need for free-space collection optics. The approach improves photon capture efficiency, can be adapted to various solid-state qubit systems, and offers a practical alternative to traditional topology optimization by considering material and fabrication constraints.

Feedback-Based Quantum Algorithm for Constrained Optimization Problems: A feedback-based quantum algorithm to address quadratic constrained binary optimization problems expands on the existing FALQON algorithm. This new approach introduces an operator that encodes the solution as its ground state and employs Lyapunov control theory to optimize quantum control systems. This method reduces computational resources and improves performance as compared to previous techniques; validated by numerical simulations.

UNTIL TOMORROW.

BREAKDOWN

Recurrent quantum embedding neural network and its application in vulnerability detection

🔍️ SIGNIFICANCE: 

• In vulnerability detection systems, NLP models face high memory consumption and complexity. The recurrent quantum embedding neural network introduces a quantum computing approach to reduce memory usage while improving upon performance. Unlike previous methods that rely solely on classical deep learning techniques or traditional quantum NLP frameworks, RQENN uses quantum advantages to manage large datasets efficiently.

🧪 METHODOLOGY: 

• A trainable encoding method based on parameterized binary indices was developed to transform code sequences into quantum embeddings: source code is tokenized, tokens are mapped to numerical indices, indices are converted to binary, binary indices are encoded into quantum states using quantum circuits.

• The RQENN model integrates these embeddings within a recurrent cell structure to capture contextual dependencies in code. This model applies iterative quantum circuits to the input data and reduces the space complexity and memory usage compared to classical models.

📊 OUTCOMES & OUTLOOK: 

• The RQENN model significantly reduces space complexity at each execution stage and uses exponentially fewer resources compared to classical models.

• RQENN uses 0.21% the number of parameters as compared to classic RNN models.

• RQENN achieves a 15.7% higher accuracy in vulnerability detection tasks compared to other QNLP methods. It also demonstrates better performance for shorter code lengths compared to classical RNNs.

BREAKDOWN

Experimental Quantum Advantage in the Odd-Cycle Game

🔍️ SIGNIFICANCE: 

• This research presents the first experimental demonstration of the odd-cycle game. Unlike previous demonstrations of quantum advantage such as random circuit sampling and Gaussian boson sampling, the odd-cycle game can be explained in simpler terms and its classical solution is easily comprehensible without requiring complex mathematical proofs.

• This research differentiates itself by implementing an optimal quantum strategy that outperforms the best classical strategy and achieves a winning probability close to the theoretical quantum limit and much higher than classical limits. Additionally, this experiment is free of the detection loophole, which has been a limiting factor in many previous photonic system-based experiments.

🧪 METHODOLOGY: 

• Two physically separated ions, each controlled by independent apparatus, were entangled via a photonic Bell state measurement at a central station.

• The players (Alice and Bob) shared entanglement and received inputs (queries) from a referee. They performed qubit rotations based on optimal angles specific to the vertices of the odd-cycle and measured their qubits to determine the output bits.

• A sequence of laser pulses was used to implement the qubit rotations and involved the transmission of outputs to the referee electronically in real-time.

📊 OUTCOMES & OUTLOOK: 

• The quantum strategy used in the odd-cycle game achieved a winning probability of 97.8% of the theoretical limit imposed by quantum mechanics. This performance was significantly above the classical limit for cycles with up to 21 vertices.

• Additionally, the experiment measured a nonlocal content of 0.54, the highest value observed for physically separate devices free of the detection loophole.