Friday, October 4th, 2024

Enjoy a nice cup of freshly brewed quantum news ☕️ 

Today’s issue includes:

• Randomized SearchRank is a semiclassical quantum algorithm designed to improve the quantum SearchRank's functionality for searching and ranking nodes on large networks.

• Two quantum algorithms use analog quantum computing with neutral atom QPUs to predict solvent configurations in drug discovery.

• A new photonic parameter-shift rule for calculating gradients in photonic quantum computers enables efficient gradient-based optimization for variational quantum algorithms on linear optical platforms.

• Plus, the latest from quantum computing on SoundCloud, a new class of error correcting codes, quantum from myotonic dystrophy, and more.

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

🔍️ Randomized SearchRank: A Semiclassical Quantum Algorithm Offering Quadratic Speedup for Efficient Network Ranking Across Large-Scale Networks

QUICK BYTE: Scientists from the Complutense University of Madrid present the Randomized SearchRank, a semiclassical quantum algorithm designed to improve the quantum SearchRank's functionality for searching and ranking nodes on large networks, providing a significant quadratic speedup while maintaining a high probability of finding marked nodes in both small and large networks.

DETAILS

• The authors propose a new algorithm, Randomized SearchRank, which modifies the quantum SearchRank algorithm by incorporating a semiclassical Szegedy quantum walk, allowing efficient searching and ranking on networks while addressing the limitations of the original quantum SearchRank algorithm, particularly for large-scale networks.

• The Randomized SearchRank combines classical and quantum techniques, using a mixed quantum state as an initial condition to replace the Szegedy quantum walk. This simplifies the quantum evolution and reduces the number of classical steps required for convergence. The algorithm achieves a quadratic speedup similar to quantum PageRank and performs well across various network sizes.

• This method overcomes the degradation in search probability experienced by the quantum SearchRank algorithm when dealing with large networks (i.e., when the ratio between the number of marked nodes and total nodes becomes large). The Randomized SearchRank maintains high search probabilities (around 0.9) even in larger networks, ensuring reliable ranking performance comparable to classical PageRank but with quantum advantages.

💊 Analog Quantum Computing with Neutral Atom QPUs for Solvent Prediction in Drug Discovery

QUICK BYTE: Scientists from Pasqal, Qubit Pharmaceuticals, and Sorbonne University present two quantum algorithms that use analog quantum computing with neutral atom QPUs to predict solvent configurations in drug discovery.

DETAILS

• The authors develop and implement Quantum-3D-RISM algorithms, using neutral atom QPUs to predict solvent configurations (water molecules) within proteins. The two algorithms, one using quantum adiabatic evolution and the other a variational quantum algorithm, are used for sampling equilibrium solvent distributions and solving the problem as a combinatorial optimization task.

• The quantum adiabatic evolution model translates the water placement problem into an Ising model. The algorithm is tested on a real quantum device by coupling it with a classical 3D-RISM solvent density computation. The VQA version, which optimizes laser parameters using classical Bayesian minimization, allows for a quantum-classical hybrid approach that improves optimization and scalability.

• These methods open new pathways for applying analog quantum computing to life sciences, particularly drug discovery, where accurate modeling of protein hydration is critical for predicting molecular interactions. By avoiding manual constraints on molecule placement, the algorithms increase the precision and efficiency of solvent prediction, making them potentially more practical as compared to classical methods.

• The Quantum-3D-RISM algorithms successfully predicted solvent configurations in protein cavities, with simulation results demonstrating agreement with known crystallographic data. The VQA algorithm provides a scalable alternative, yielding accurate water molecule placement.

💡 New Parameter-Shift Rule for Photonic Quantum Computing Improves Gradient Optimization for Variational Quantum Algorithms

QUICK BYTE: Researchers from Quandela and Quantinuum introduce a new photonic parameter-shift rule for calculating gradients in photonic quantum computers, enabling efficient gradient-based optimization for variational quantum algorithms on linear optical platforms.

DETAILS

• A method for calculating gradients in quantum algorithms is implemented on linear optical quantum computing platforms by creating a photonic-specific parameter-shift rule. This rule addresses a critical limitation of previous methods due to the non-unitary nature of phase-shift operators in Fock space.

• The new photonic parameter-shift rule uses a parameterized photonic circuit with shifted parameters for each evaluation, making it highly efficient. The method overcomes challenges specific to photonics, such as phase-shift operator differentiation, and applies to various quantum tasks, including quantum chemistry and generative modeling.

• The photonic PSR also demonstrated superior optimization performance in numerical simulations, showing resilience to noise from finite sampling and photon distinguishability. The paper also highlights the method's practical application to VQAs and generative models, having greater resilience compared to traditional gradient-based and gradient-free methods.

Variational qraphical quantum error correction codes are a new class of quantum error-correcting codes that use graphical representations from the Quon 3D language. Developed by scientists from Tsinghua University, these codes incorporate adjustable parameters that can optimize error correction for specific noise environments, providing flexibility to adapt to various quantum noise models. The paper demonstrates the VGQEC's ability to outperform existing codes, such as the five-qubit [[5, 1, 3]] code, by numerically optimizing the performance under amplitude damping and thermal relaxation noise.

Researchers at the University of Nottingham, in collaboration with Phasecraft and QuEra Computing Inc., have received funding to explore quantum computing's potential for drug discovery targeting myotonic dystrophy. The project is part of the Wellcome Leap Quantum for Bio (Q4Bio) program and intends to seek out novel ways to use quantum computing to model complex biological systems that conventional computers cannot handle. By combining quantum and classical simulation methods, the team hopes to accelerate the discovery of treatments for this debilitating genetic disease.

Researchers at the Karlsruhe Institute of Technology have demonstrated precise control of tin-vacancy centers in diamonds using microwaves, improving qubit coherence times to 10 milliseconds. Achieved with dynamical decoupling and superconducting waveguides, this development improves the stability and manipulation of these qubits for use in quantum communication and computing.

At the Q2B24 Tokyo conference, Professor Naoki Yamamoto of Keio University discussed how quantum computing can address both near-term and long-term challenges. He highlighted the potential of reservoir computing, where quantum superconducting reservoirs emulate neural networks, and the use of 120 entangled qubits for more efficient calculations. Yamamoto also emphasized quantum computing's future role in solving partial differential equations for applications like weather forecasting and the importance of quantum sensing for improving measurement precision. He stressed the convergence of quantum computing, sensing, and communication as key to advancing the technology and transforming industries.

Scientists from Sapienza Università di Roma implement a modular quantum-to-quantum Bernoulli factory on an integrated photonic processor. They demonstrate a fully programmable photonic system capable of manipulating photonic qubits using interferometric schemes to perform fundamental operations such as inversion, product, and addition. These operations, which act on qubit states, form the building blocks of the QQBF, promote the manipulation of randomness in a modular fashion without requiring knowledge of the input bias. The system's experimental results show high fidelity and modularity, suggesting potential applications in quantum computation and communication.

Abdurrahman Keklik, head of Istanbul-based tech firm ComPro, emphasized the importance of developing Turkey’s own quantum computing technology, noting that rising protectionism, such as the EU's restrictions on quantum technology exports, makes this essential. Speaking at TEKNOFEST, Keklik highlighted ComPro's efforts to build a Turkish quantum computer prototype while aiming for local production.

LISTEN

ENJOY

A recent article from Forbes announces Eduardo Reck Miranda’s newest composition, "Qubism," which merges classical music with quantum computing, using an AI model run on a quantum computer to improvise in real-time alongside a live violinist. By integrating quantum technology, the piece creates dynamic, ever-changing musical interactions that explore new frontiers in creativity. Miranda's work is a testament to quantum computing's potential to alter the world as we know it, weaving its way into music composition and elevating the role of technology as a true partner in artistic expression, rather than just a tool.

WATCH

Dr. Taha Selim, co-founder of MolKet, explores quantum entanglement, explaining its intuitive, mathematical, and physical aspects, and demonstrates how it enables quantum computations that are impossible with classical computers: