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

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IN TODAY’S ISSUE:

Tags: TRAPPED IONS HARDWARE PHOTONIC QML ERROR CORRECTION NOVEL FRAMEWORK SOFTWARE APPLICATION

• Tsinghua University researchers stabilized a 2D crystal of 512 ions, achieve quantum simulation with 300 ion qubits

• Recurrent Guassian quantum network tested on Xanadu's Borealis processor improves performance in processing quantum time series

• Bosonic qubits in a concatenated architecture with quantum low-density parity-check codes shown to be more viable for error correction

• An abstraction hierarchy for quantum software engineering proposed to address current complexities and support scalable software development

• IBM Quantum and Moderna use quantum computing to predict mRNA secondary structures with accuracy comparable to classical solvers

• Plus, DARPA awards HRL Labs $7.1 million for quantum nanoelectronic devices, Jay Gambetta of IBM Research speaks on quantum-centric supercomputers, and CEO of Photonic Inc. speaks on distributed quantum network.

Check out NEW POLLS!

BRIEF BYTES

NEWS FOR THOSE IN A HURRY

• U.S. military researchers, through DARPA's SynQuaNon program, awarded HRL Laboratories a $7.1 million contract to develop advanced quantum nanoelectronic devices with improved size, weight, and power characteristics. HRL will focus on creating and benchmarking synthetic quantum materials for applications like superconducting qubits, high-sensitivity photon detectors, and quantum-limited signal processing technologies. The goal is to advance superconducting technologies by developing materials that operate at higher temperatures and frequencies.

• In this week’s edition of Politico’s Future in Five Questions, Jay Gambetta from IBM Research discusses the concept of “quantum-centric” supercomputers, the crucial role of the number zero, and the federal government's potential to boost the quantum research ecosystem.

• Quantum computers are advancing in power and accuracy and showing promise in applications like chemistry, drug discovery, and materials science. Despite current limitations, hybrid approaches combining classical and quantum computing are making strides which suggests a future where quantum technology accelerates breakthroughs in various scientific fields. For a more detailed overview of the most recent advancements, check out this full article.

• Watch this interview with Stephanie Simmons, CEO of Photonic Inc., on the distributed quantum network:

TOP HEADLINES IN NEWS & RESEARCH

NEWS

Tags: TRAPPED IONS HARDWARE 

LARGE-SCALE ION TRAP QUANTUM SIMULATION

BRIEF BYTE: Researchers at Tsinghua University have demonstrated the stabilizing and cooling of a two-dimensional crystal of 512 ions and conducting a quantum simulation with 300 ion qubits. 

WHAT HAPPENED: 

• Professor Luming Duan, along with his research team, demonstrated the creation and stabilization of a two-dimensional quantum simulator with over 500 trapped ions using a monolithic three-dimensional ion trap at cryogenic temperatures to minimize interference from background gas molecules and maintain stability.

• This advancement is significant in that it merges two essential features: a large qubit capacity and individual qubit readout capability. These capabilities are necessary for practical quantum simulations and computation tasks, especially when we are still working with NISQ devices.

• Previous methods have either achieved large qubit numbers without individual detection, or had individual detection but with a smaller number of qubits. This study integrates both features in a single system.

HOW: 

• They performed quantum simulations of long-range quantum Ising models with tunable coupling strengths and patterns using 300 ions. This allows the study of various spatial correlation patterns and the verification of quantum simulation results through two-spin correlations and classical simulated annealing comparisons.

• The site-resolved readout in a single-shot measurement means more detailed and accurate quantum simulations, and the sideband cooling to a very low phonon number across all relevant modes means better precision for quantum simulations.

• The study also explored quantum sampling tasks, such as probing the quench dynamics of the Ising model in a transverse field. This demonstrates the potential of the setup for exploring classically intractable quantum dynamics.

WHY IS THIS IMPORTANT:

• This new approach to trapped ion quantum computing improves the scalability and precision of ion-based quantum simulations.

• The study suggests that this setup is scalable to thousands of ions through sympathetic cooling and advanced laser techniques — time will tell.

RESEARCH

Tags: PHOTONIC QML

OVERVIEW OF A RECURRENT GAUSSIAN QUANTUM NETWORK FOR ONLINE PROCESSING OF QUANTUM TIME SERIES

BRIEF BYTE: A recurrent Gaussian quantum network is proposed and tested on Xanadu’s Borealis photonic quantum processor for processing quantum time series.

WHY: 

• The disturbance and destruction of quantum states during measurements hinders real-time processing of temporal data. This issue is especially relevant to quantum communication, where online processing is important. The recurrent Gaussian quantum network is introduced as a solution. It’s an entirely trainable model for processing quantum time series.

• Recent models inspired by reservoir computing partially address this by fixing some internal parameters and only training a subset. But, this limited flexibility and performance when, in contrast, the RGQN model allows all internal interactions to be trained. This approach not only improves benchmark task performance but also eliminates the need for redundant encoding and enables real-time processing.

HOW: 

• The RGQN is constructed using a generic m-mode symplectic circuit. Bloch-Messiah decomposition is used to structure the circuit as a combination of linear interferometers and single-mode squeezers. What’s important to note here is that the symplectic circuit can be trained entirely which provides the model’s flexibility and improved performance.

• The RGQN processes temporal data by sending quantum states through the input modes and memory modes recall past states. Training involves optimizing the parameters of the circuit to handle specific temporal tasks.

• Benchmark tasks include the short-term quantum memory task and the entangler task. The STQM task recalls quantum states after a specific number of iterations. The entangler task entangles different states in a timer series that we initially uncorrelated.

• Quantum communication tasks include the superaddidtivity task and the quantum channel equalization task. The superadditivity task generates entangled input states. The QCE task compensates for unwanted memory effects in quantum channels by using a second RGQN instance as a decoder.

RESULTS: 

• The RGQN can be implemented using readily available optical components as demonstrated by its performance on Borealis.

• The RGQN improved performance in benchmark tasks compared to reservoir computing-inspired models, especially when it comes to processing quantum time series with higher fidelity and greater flexibility.

• The model is more resource-efficient because it removes certain hardware restrictions present in existing methods and counteracts unwanted memory effects in quantum channels.

• The RGQN successfully performed the QCE task without requiring prior knowledge of input states or redundantly encoded signals which makes it more practically applicable for tasks like quantum key distribution.

RESEARCH

Tags: ERROR CORRECTION

OVERVIEW OF ANALOG INFORMATION DECODING OF BOSONIC QUANTUM LOW-DENSITY PARITY-CHECK CODES

BRIEF BYTE: This study proposes decoding methods that exploit analog syndrome information from bosonic qubits in a concatenated architecture with quantum low-density parity-check codes.

WHY: 

• Quantum error correction is necessary for scalable quantum computing, but traditional discrete-variable (ion-trap, superconducting, spin) qubits are challenging to isolate from external errors. Bosonic codes use the infinite-dimensional Hilbert space of harmonic oscillators and offer an alternative with intrinsic error correction capabilities.

• Previous studies have mostly been focused on concatenating bosonic codes with repetition codes or 2D topological codes. This study uses quantum low-density parity-check codes, because they are more information-efficient and maintain a constant encoding density as code distance scales.

HOW: 

• Analog Tanner graph decoding and single-stage decoding protocols are introduced to incorporate analog syndrome information from bosonic qubits for improved error correction.

• Designed to minimize the number of repeated syndrome measurements required under noise, quasi-single shot protocols are used to improve decoding without multiple rounds of syndrome extraction.

• These methods are combined with QLDPC codes to reduce computational overhead. The study's implementation and simulations demonstrate significant progress in efficient quantum error correction, supported by open-source tools for broader applicability.

• Numerical simulations were conducted to test the efficacy of the proposed methods In addition, open-source software tools were developed so that the methodology can be applied and verified by others in the field.

RESULTS: 

• The study successfully integrates analog information from bosonic syndrome measurements into belief propagation and matching decoders and shows how analog syndrome data can improve error correction protocols.

• The introduction of analog Tanner graph decoding methods shows an improvement in sustainable single-shot thresholds for the three-dimensional surface code.

• The methods developed contribute to the realization of fault-tolerant quantum computation by improving the efficiency and effectiveness of error correction protocols using analog information. The reduction in time-domain decoding overhead and the improved sustainable thresholds make the proposed methods more viable for practical implementations in quantum computing systems.

• By providing open-source tools, the study encourages further exploration in quantum error correction protocols and bosonic codes.

PREPRINT

Tags: NOVEL FRAMEWORK SOFTWARE

OVERVIEW OF AN ABSTRACTION HIERARCHY TOWARDS PRODUCTIVE QUANTUM COMPUTING

BRIEF BYTE: Based on learnings from classical computer science, this study analyzes the current state of the quantum software stack and proposes an abstraction hierarchy to support quantum software engineering.

WHY: 

• The diversity in quantum hardware architectures, qubit technologies, and the presence of noise complicates software development and a growing community of researchers and developers seek productive ways to solve real-world problems using quantum computers. Effective quantum software is important for addressing end-user needs and achieving both performance and utility.

• A well-defined abstraction hierarchy can help separate concerns between different layers of quantum software development and enable more productive and scalable software engineering practices. This paper proposes that abstraction hierarchy tailored to quantum software engineering and inspired by lessons learned from the evolution of classical parallel computing.

THE HIERARCHY: 

• The quantum programming model facilitates the translation of high-level algorithms into executable code and balances the need for abstraction with the need for practical implementation.

• The quantum execution model is an abstract representation of how quantum programs execute and includes logical circuits, error mitigation, and correction processes.

• The quantum hardware model handles the physical implementation of qubits and their operations, considering factors like connectivity and dynamic control to extend quantum state lifetimes.

RESULTS: 

• The pace of quantum hardware and software development is a rapid one, but this rapid evolution is unsustainable as hardware becomes more complex. This complexity tends to trickle up from hardware to software and creates challenges for developers.

• Current quantum computing lacks a well-defined abstraction hierarchy which leads to blurred boundaries between hardware, execution, and programming models.

• Implementing formal software engineering practices is necessary for quantum computing to help develop testing and analysis tools, reduce resource waste, and support principles like division of labor and resource rationalization.

PREPRINT

Tags: APPLICATION

OVERVIEW OF mRNA SECONDARY STRUCTURE PREDICTION USING UTILITY-SCALE QUANTUM COMPUTERS

BRIEF BYTE: Researchers from IBM Quantum and Moderna explore the use of quantum computing to predict mRNA secondary structures and achieve accurate results comparable to classical solvers.

WHY: 

• Predicting the secondary structure of mRNA is applicable to gene regulation, translation, degradation, and the development of RNA-based therapeutics. However, this is challenging due to the complex combinatorial nature of the problem. Classical methods, including machine learning, have been used but face limitations due to the combinatorial explosion of possible configurations.

• This study uses advancements in quantum computing to address these limitations, particularly through variational quantum algorithms like the conditional value at risk-based variational quantum eigensolver. This method offers a promising approach as it allows for the examination of relatively large mRNA sequences on universal quantum processors.

HOW: 

• The problem of RNA secondary structure prediction is transformed into a QUBO problem, so it may be solved using quantum algorithms. The CVaR-based VQE algorithm is then used to solve the QUBO problem by optimizing the lower tail of the energy distribution to improve convergence.

• The NFT algorithm is the classical gradient-free optimizer used to optimize the quantum circuit parameters sequentially.

• A "two-local" ansatz with single-qubit Pauli-Y rotations and two-qubit control-Z gates is used to prepare the quantum state.

• Noise-free simulations and hardware experiments on IBM Eagle and Heron processors are both run to benchmark the effectiveness of CVaR-VQE.

RESULTS: 

• The CVaR-VQE algorithm achieved an average success probability of 0.4 to 1.0 for problem sizes ranging from 10 to 40 qubits, indicating strong performance in noise-free simulations. The average optimality gap, which measures the difference between the quantum and classical solutions, was less than 20% which demonstrates the algorithm's accuracy.

• Experiments on IBM Eagle and Heron processors demonstrated success probabilities above 0.38 for sequences involving 26, 40, and 50 qubits. The average optimality gap for hardware runs was below 34%, indicating reasonable accuracy despite the presence of hardware noise.

• These results demonstrate that quantum computing, specifically using the CVaR-VQE algorithm, can effectively solve complex optimization problems such as mRNA secondary structure prediction.

EVENTS

• Sunday, June 2 | QTM-X Education Series 2 of 10: Superposition

• Tuesday, June 4 | Quantum Supremacy w/ D-Wave

• Wednesday, June 5 | Quant Insights Conference: Quantum Computing in Quant Finance 

• Wednesday, June 5| Quantum Computing Workshop w/ Classiq

• Thursday, June 6 | QaaS w/ Quantonix

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

JOBS POSTED WITHIN LAST 24 HOURS

• SandboxAQ AI Backend Software Engineer Intern | Remote

• SandboxAQ Senior Scientific Leader: Computational Chemistry | Remote

• Dice Research Intern - Quantum Information and Computation | Redmond, WA $61K - $158.8K

• Applied Materials Photonic Quantum Technology Development | Santa Clara, CA $140K - $192.5K

• Nokia Quantum Communication International Coop | New Providence, NJ

UNTIL TOMORROW.

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