Welcome to the Quantum Realm. 

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

🔬🧪📚 There’s nothing quite like the beauty of the interconnectedness of STEM domains. If you needed a dose of inspiration for your next innovation, today is certainly that.  

🗓️ THIS WEEK

Monday, June 16 - Tuesday June 17 | D-Wave Qubits Conference (Boston, MA & Livestream)

Sunday, June 23 | QTM-X Quantum Education Series 5 of 10: Quantum Hardware

📰 NEWS QUICK BYTES

👩‍🔬 A powerhouse trio of quantum, chemistry, and AI: Microsoft’s Azure Quantum Elements combines the power of generative AI with quantum-classical hybrid computing to revolutionize (not an overstatement) chemistry and materials science. The new capabilities, Generative Chemistry and Accelerated DFT, will compress centuries of chemical research into decades and fast-track scientific discovery and innovation. Potential applications include the accelerated creation of new molecules and materials which would quickly progress fields such as consumer goods, medicine, and sustainability. Register for an upcoming webinar here. 

🧶 Genon braiding coming to QEC near you: Quantinuum's latest research on quantum error correction introduces "genon braiding" to execute fault-tolerant gates using high-rate error correcting codes. This innovation may require less physical qubits per logical qubit. It’s been said too many times to count, but this is truly a significant step toward scalable quantum computing.

➰ New harmonic oscillator overcomes quantum computing trade-off: Chalmers University researchers have developed a system that addresses quantum computing's trade-off problem, enabling both complex operations and improved fault tolerance by embedding a control device within harmonic oscillators.

💡 Google will pay for your modular quantum computing ideas: Google Quantum AI is inviting research proposals to enhance modular and distributed quantum computing, focusing on superconducting qubit platforms. Applications open on June 27, 2024, with awards up to $150K USD for innovative projects..

💰️ Quantum computing is expensive, but so is lost opportunity: D-Wave's study reveals that businesses using quantum computing expect significant ROI, with a potential combined financial impact of up to $51.5 billion. The survey highlights growing adoption and benefits in efficiency, revenue, and innovation. While promising, the key word is “potential.” (The survey was conducted by Hyperion Research on behalf of D-Wave)

🧩 Started from childhood puzzles and now he’s here: Vadim Lyubashevsky, inspired by childhood math puzzles with his grandfather, is now a leading cryptographer at IBM Research. He works on lattice-based cryptography to develop quantum-safe algorithms that will replace current encryption standards vulnerable to quantum computers. His work has significantly contributed to IBM's successful proposals for new cryptographic standards selected by NIST.

💼 Quantum Insights: Sonali Mohapatra, the quantum innovation sector lead at the National Quantum Computing Center, discusses quantum’s role in modeling nature, security implications, and diverse career opportunities. Watch below:

☕️ FRESHLY BREWED RESEARCH

Attention-Based Deep Reinforcement Learning for Qubit Allocation in Modular Quantum Architectures: A deep reinforcement learning approach consisting of transformer encoders and GNNs is used to determine optimal qubit allocation in multi-core quantum architectures. This method focuses on reducing inter-core communications and compilation times and performs better as compared to traditional heuristic and optimization algorithms. Breakdown here.

Lifetime of Infrared Quantum Information in Qubits: The investigation of long-term decoherence of qubits in the presence of infrared quantum information shows that in moderately sub-Ohmic dissipation, quantum information remains intact over the universe's current age. Using a strictly perturbative approach, the study demonstrates that qubits can sustain coherence significantly longer than previously believed, providing new insights into quantum information preservation as well as its implications for black hole information loss. Breakdown here.

Quantification of entanglement and coherence with purity detection: Entanglement and coherence in quantum systems are quantified using purity detection methods. By establishing quantitative bounds that are analytically computable and experimentally friendly, the study confirms these methods through optical experiments. This provides an efficient means to verify large-scale quantum information processing which is relevant for benchmarking and optimizing quantum technologies.

Optimal key forwarding strategy in QKD behaviours: A linear programming-based algorithm is presented for optimizing key forwarding and redistribution in quantum key distribution networks. The goal of the algorithm is to maximize key distribution efficiency across different network scenarios by converting QKD network graphs into complete graphs with logical links and optimizing key rates for different user requirements.

Development of optimization method for truss structure by quantum annealing: By representing real numbers with binary variables and using elastic strain energy and position energy of a truss structure, the research demonstrates that quantum annealing can efficiently achieve optimal truss designs. The approach may achieve faster global optimization compared to conventional methods, and be effectively used in structural optimization.

UNTIL TOMORROW.

BREAKDOWN

Attention-Based Deep Reinforcement Learning for Qubit Allocation in Modular Quantum Architectures

🔍️ SIGNIFICANCE: 

• This research explores modular qubit architectures as a solution to addressing the scalability of quantum computing. In order to successfully realize multiple cores in a modular architectures, quantum systems will need efficient communication while minimizing decoherence when transferring quantum states between cores.

• The researchers propose deep reinforcement learning to optimize qubit allocation while reducing the extensive search times typical of NP-hard problems inherent in quantum circuit compilation. Traditional methods often rely on heuristic mappers or derivative-free optimization algorithms, which can be time-consuming and less effective for complex, multi-core systems.

🧪 METHODOLOGY: 

• The DRL framework that incorporates advanced neural network architectures to encode quantum circuits and optimize qubit allocation.

• Quantum circuits are represented using self-attention mechanisms within a transformer encoder to capture the complex relationships between qubits.

• GNNs are used to encode the state representations while taking into account the connectivity and interactions between qubits. This mechanism outputs the probabilities of matching logical qubits with physical cores.

• The DRL agent uses an autoregressive policy to decode the qubit allocation step-by-step while maintaining consideration of the constraints and minimizing inter-core communications.

📊 OUTCOMES & OUTLOOK: 

• The DRL-based method outperforms traditional approaches in reducing inter-core communications and minimizing the online time to find a solution. It also shows improvements in minimizing state transfers, which means enhanced fidelity and reduced decoherence in quantum state transfers.

BREAKDOWN

Lifetime of Infrared Quantum Information in Qubits

🔍️ SIGNIFICANCE: 

• This is an exploration of infrared quantum information within the context of qubit dynamics as it relates to gauge theories and black hole formation. IQI refers to the entanglement between particles and low-frequency photons or gravitons produced during scattering events. This research is important as it addresses the long-standing black hole information loss paradox by showing that infrared particles do not completely destroy quantum information over the universe's current age.

• Unlike previous methods, strictly perturbative quantum master equations are used to compute the decoherence rates. This provides a more detailed and theoretically consistent understanding of long-term qubit coherence.

🧪 METHODOLOGY: 

• A two-level qubit system coupled to a bath of linear harmonic oscillators (known as the spin-boson model) is modelled.

• The fourth-order time convolutionless master equation for reduced dynamics is used to investigate the long-term behavior of qubit decoherence in the presence of infrared divergences. Since this approach excludes resummation or open-system methods, qubit decoherence due to infrared boson production can be examined.

• The study considers the qubit-bath coupling, bath dispersion parameter, and varying coupling strengths to analyze the coherence dynamics and decoherence rates across Ohmic, sub-Ohmic, and super-Ohmic dissipation.

📊 OUTCOMES & OUTLOOK: 

• In moderately sub-Ohmic dissipation, the decoherence rate exceeds the Hubble constant, suggesting that quantum information remains intact over the universe's current age.

• In deep sub-Ohmic dissipation, qubits exhibit infrared-divergent recovery of coherence, indicating potential long-term preservation of quantum information.

• The research also highlights an essential singularity in the decoherence time as bath dispersion approaches Ohmic dissipation, dramatically slowing down the decoherence process. These results imply that qubits in certain environments can maintain coherence far longer than previously thought, which is insightful (and exciting) in terms of studying IQI phenomena in current quantum computers and further understanding quantum information preservation in black hole scenarios.