Welcome to the Quantum Realm. 

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

Mondays will be Mondays. But this Monday is superior to the rest with quantum tech improving early diagnosis of Alzheimer’s and Standard Model predictions. 🌠

🗓️ THIS WEEK

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

Thursday, June 13 | A Survey of Quantum Resource Estimation Tools

Saturday, June 8 | Towards Practical Quantum Computing: Addressing Crosstalk and Circuit Optim w/ Washington DC Quantum Computing Meetup

Sunday, June 9 | QTM-X Weekly Quantum Education Session 3/10: Entanglement

📰 NEWS QUICK BYTES

✏️ Qiskit School Is In Session: Registration for the 2024 Qiskit Global Summer School, happening virtually from July 15 to July 26, is now open. Participants will gain skills to run utility-scale quantum experiments through lectures and Q&A sessions led by IBM Quantum experts, with a focus on the new Qiskit SDK capabilities.

💎 Black Opal for the EdTech Win: Q-CTRL's Black Opal for Enterprise has clinched the Digital Courseware Solution of the Year Award by EdTech Breakthrough. This platform has empowered over 16,000 learners with no prior quantum knowledge through comprehensive, self-paced courses and innovative visualizations.

➗ Mastering Quantum Dynamics: This detailed tutorial on quantum master equations is essential for modeling quantum system dynamics. It covers methods like the Lindblad master equation, Bloch-Redfield theory, and Floquet theory, with practical examples and code implementations to help researchers in quantum optics and computing.

🔋 Recharging Quantum Storage: Researchers at the University of Gdansk and the University of Calgary have developed nonreciprocal quantum batteries, showing a 4x improvement in energy storage efficiency compared to conventional ones. This breakthrough uses reservoir engineering to prevent energy backflow and optimize charging dynamics.

✨ Quantum Visionary: In Episode 4 of Crosstalk, Ismael Faro, VP of Quantum Services and Data at IBM, discusses IBM Quantum's software strategy for 2024 and beyond, emphasizing the role of classical AI in advancing utility-scale quantum experiments. Faro shares insights from his experience, including connecting Qiskit users with IBM Quantum hardware.

☕️ FRESHLY BREWED RESEARCH

Fault-tolerant connection of error-corrected qubits with noisy links: Fault-tolerant connections between error-corrected qubits can tolerate interface noise levels up to 10%, significantly higher than previously assumed, while maintaining overall fault tolerance. Since it relaxes the requirements for low-noise inter-module communication, this can imply more scalable quantum computing using existing hardware. Breakdown here.

Revolutionizing Alzheimer’s Diagnosis using Quantum Computing: Quantum sensors and quantum neural networks may be used to improve the early detection of Alzheimer's disease through retinal imaging. Specifically, both the accuracy and efficiency of diagnosing Alzheimer's in its early stages showed improvements using quantum technologies. Breakdown here.

Towards Quantum Computing Timelike Hadronic Vacuum Polarization and Light-by-Light Scattering: Schwinger Model Tests: The article explores the use of 1+1-dimensional quantum electrodynamics via the Schwinger Model to investigate hadronic vacuum polarization and hadronic light-by-light scattering relevant to the muon's anomalous magnetic moment. By using tensor network techniques and classical emulators of digital quantum computers, the research overcomes limitations of traditional lattice QCD methods. Breakdown here.

QuAS: Quantum Application Score for benchmarking the utility of quantum computers: The presented Quantum Application Score is a revised holistic scoring method for benchmarking the utility of quantum computers. QuAS incorporates strengths from previous metrics, such as QPack and Q-score, to create an application-level metric that better quantifies the practical utility of quantum computers.

Quantum-circuit refrigeration of a superconducting microwave resonator well below a single quantum: A single-junction quantum-circuit refrigerator is used for controlling the temperature of a superconducting 4.7 GHz resonator. Using a transmon qubit, the research measures resonator Fock states and shows effective dissipation control with both continuous and pulsed radiofrequency signals, which addresses previous limitations.

Simulation of a feedback-based algorithm for quantum optimization for a realistic neutral-atom system with an optimized small-angle controlled-phase gate: A feedback-based quantum optimization algorithm, FALQON, is applied to a realistic neutral-atom system. The study presents a method to implement small-angle controlled-phase gates with high fidelity and improve the performance of FALQON in solving combinatorial optimization problems like MaxCut.

Enhanced quantum state transfer by circumventing quantum chaotic behavior: A scalable protocol for high-fidelity quantum state transfer in a two-dimensional network of 36 superconducting qubits is presented. By overcoming quantum chaotic behavior using optimized couplings via a Monte Carlo annealing process, the study demonstrates the efficient transfer of single-qubit excitations, entangled states, and multiple excitations, while achieving significant fidelity even in the presence of imperfections.

UNTIL TOMORROW.

BREAKDOWN

Fault-tolerant connection of error-corrected qubits with noisy links

🔍️ SIGNIFICANCE: 

• Connecting modular quantum processors with high fidelity is a significant bottleneck in quantum computing. Unlike previous methods that required extremely low noise levels for inter-module communication, this research demonstrates that quantum systems can tolerate substantially higher noise levels at the interface (up to 10%) compared to the bulk (1%) while maintaining fault tolerance. This relaxation in the noise threshold allows current hardware, which already approaches these noise levels, to achieve scalable quantum computing without requiring further technological advancements in noise reduction.

🧪 METHODOLOGY: 

• Both analytical and numerical methods are used to quantify the combined effect of errors across the interface and within the bulk of surface code patches. The primary technique involves connecting distinct surface code patches via a noisy interface and analyzing how noise affects fault tolerance.

• The interface was modeled using a noise model where the contributions of bulk and interface noise combine to create logical failure modes.

• Analytical bounds on the tolerable error rates were established, demonstrating that interface noise can be up to 14 times higher than bulk noise.

• Monte Carlo simulations were conducted to validate the analytical results, ensuring that the theoretical findings hold true under practical conditions.

📊 OUTCOMES & OUTLOOK: 

• The system can tolerate interface noise levels up to 10% while maintaining fault tolerance, even with bulk noise levels around 1%.

• The introduction of higher noise levels at the interface results in only a minimal effect on the code’s threshold and subthreshold behavior.

• The results imply that existing quantum hardware platforms can achieve scalable fault-tolerant quantum computing with noisy interconnects. This significantly reduces the overheads in terms of time and space required for distillation or the development of better local gates.

BREAKDOWN

Revolutionizing Alzheimer’s Diagnosis using Quantum Computing

🔍 SIGNIFICANCE: 

• Alzheimer's disease is a global health concern due to its complex onset and lack of definitive early diagnostic markers. Traditional methods like CT and PET scans often fail to detect the disease in its early stages. By using the advanced computational power of quantum computing, the precision and efficacy of early diagnosis may be improved.

• This approach differentiates itself from previous methods by integrating quantum sensing and quantum neural networks which offer unparalleled sensitivity and accuracy compared to classical imaging and AI techniques.

🧪 METHODOLOGY: 

• The research uses several methodologies. One method involves optically pumped magnetometers and quantum dots to increase the precision of retinal imaging. OPMs detect the magnetic fields generated by electrical activity in the retina, providing high spatial resolution without requiring cryogenic temperatures. In contrast, QDs are used for their optical properties to identify biomarkers indicative of Alzheimer's disease.

• Another approach is using quantum neural networks to process retinal images by encoding features as qubits. The training of QNNs involves using quantum algorithms that adjust the parameters of quantum gates, thereby improving the model’s accuracy in detecting Alzheimer’s disease from retinal images.

• Optical coherence tomography and optical coherence tomography angiography are used to obtain high-resolution images of retinal layers and vascular structures without the need for invasive dye injections. Specifically, OCTA monitors blood flow dynamics in the retina, providing detailed insights into microvascular abnormalities associated with Alzheimer’s disease.

📊 OUTCOMES & OUTLOOK: 

• The integration of quantum technologies allows for the early detection of Alzheimer’s disease by identifying subtle changes in retinal biomarkers that are not discernible through traditional methods. This can significantly improve the management and treatment outcomes for patients.

• Quantum sensors, such as OPMs and QDs, offer higher sensitivity and accuracy in detecting the minute details of retinal structure and blood flow patterns. This enables more precise identification of Alzheimer’s-related changes in the retina.

• QNNs demonstrate the ability to process large datasets more efficiently than classical neural networks and this scalability allows for comprehensive analysis of retinal images.

• The successful application of quantum computing in this research shows its potential to transform healthcare by providing tools that are both powerful and precise.

BREAKDOWN

Towards Quantum Computing Timelike Hadronic Vacuum Polarization and Light-by-Light Scattering: Schwinger Model Tests

🔍 SIGNIFICANCE: 

• The relevance of this research lies in its potential to improve the precision of Standard Model predictions concerning the muon's anomalous magnetic moment which is a key parameter in particle physics. This parameter involves quantum corrections from all sectors of the SM, including quantum electrodynamics, weak, and quantum chromodynamics. Discrepancies between experimental measurements and theoretical predictions indicate potential new physics beyond the SM. Specifically, this research targets the QCD contributions, which are the most complex and least controlled.

• Traditional methods face limitations in the timelike region, and this research introduces a methodology using 1+1-dimensional quantum electrodynamics through the Schwinger Model. By bringing together tensor network techniques and classical emulators of digital quantum computers, this approach circumvents challenges in lattice QCD calculations.

🧪 METHODOLOGY: 

• The Schwinger Model, a 1+1-dimensional QED, is used as a testbed for exploring hadronic vacuum polarization and hadronic light-by-light scattering.

• The researchers utilized the ITensor library in Julia to perform simulations. The matrix product states form represented quantum states, and the time evolution was achieved using the time-dependent variational principle algorithm. The density matrix renormalization group algorithm prepared the initial state.

• Classical emulators of digital quantum computers were used to provide insights into the implementation of quantum computations on actual quantum hardware.

📊 OUTCOMES & OUTLOOK: 

• The research successfully extracted the HVP tensor using tensor network simulations. The results showed the expected features, with a decay in both spacelike and timelike directions and a peak around the origin. The numerical Fourier transform's quality was crucial for accurate results.

• The evaluation of the HLBL contribution demonstrated the feasibility of extracting relevant quantities using tensor networks. The extraction required a finer spatial and time grid for better precision.

• The research successfully demonstrated a new approach to tackling the challenges of calculating QCD contributions to the muon's anomalous magnetic moment. By using quantum computing techniques, specifically tensor networks and classical emulators, the study provides a way to more precise and direct computations in the timelike region.