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IN TODAY’S ISSUE:

Tags: COLLABORATION ALGORITHMS TRAPPED IONS SOFTWARE APPLICATION QUANTUM INSIGHTS ERROR CORRECTION SUPERCONDUCTING

• Researchers from JPMorgan Chase, Argonne National Laboratory, and Quantinuum have successfully demonstrated a quantum algorithmic speedup using QAOA

• IBM's beta release of the Qiskit Transpiler Service for Quantum Premium Plan users integrates AI-powered transpiler passes to improve quantum circuit optimization and performance

• Study applies quantum computing to automated guided vehicle scheduling, demonstrates reductions in computation time

• A deeper look at decoherence in quantum systems shows that different geometric configurations affect stability, challenging previous assumptions about multiqubit systems

• Research from Google using hybrid analog-digital quantum simulator with 69 superconducting qubits reveals new insights into thermalization dynamics and phase transitions

• Plus, Classiq and Hewlett Packard Labs have developed a method for solving large-scale combinatorial optimization problems, new entangled QMARL framework for distributed multi-agent reinforcement learning, Pasqal and Tech Mahindra are collaborating to advance global quantum computing applications, Alice & Bob and Riverlane are partnering to take on quantum error correction, and Quantum Machines’ OPX quantum control platform is improving quantum sensing

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BRIEF BYTES

NEWS FOR THOSE IN A HURRY

• Classiq and Hewlett Packard Labs have developed a rapid method for solving large-scale combinatorial optimization problems by combining HPE's Cray Supercomputing EX system and Classiq's QAOA synthesis engine. Their work will be presented at the IEEE IPDPS Conference on May 31.

• Collaboration in distributed multi-agent reinforcement learning is challenging due to the need for balancing coordination and communication costs. The proposed entangled QMARL framework in this study uses quantum entanglement to facilitate cooperation without sharing local observations. This reduces communication overhead and has better performance than classical methods.

• Pasqal and Tech Mahindra have announced a collaboration to advance quantum computing applications globally by focusing on training, developing high-impact use cases, and educating clients on quantum technology.

• At the France Quantum conference, Alice & Bob and Riverlane announced their collaboration to advance quantum error correction by integrating Riverlane’s quantum error correction stack with Alice & Bob’s cat qubit technology. This partnership will focus on addressing the challenge of bit-flip and phase-flip errors.

• Quantum Machines has advanced quantum sensing by integrating its OPX quantum control platform with a new non-adaptive phase estimation algorithm. This algorithm uses a binomial distribution method and improves the accuracy and sensitivity of quantum measurements without relying on single-shot readout.

TOP HEADLINES IN NEWS & RESEARCH

NEWS

Tags: COLLABORATION ALGORITHMS TRAPPED IONS

QUANTUM ALGORITHM SPEEDUP ACHIEVED

BRIEF BYTE: Researchers from JPMorgan Chase, Argonne National Laboratory, and Quantinuum have demonstrated a quantum algorithmic speedup using QAOA.

WHAT HAPPENED: 

• QAOA is known for its potential applications in logistics, telecommunications, financial modeling, and materials science. By applying QAOA to the low autocorrelation binary sequences problem, the collaboration was able to show that the algorithm’s performance improves as problem size increases, successfully outpacing classical solvers.

• To validate these results, a simulator was developed using the Polaris supercomputer at Argonne. This allowed the team to test QAOA’s performance in a noiseless environment.

• Additionally, a small-scale implementation was conducted on Quantinuum’s H1 and H2 trapped-ion quantum computers, where algorithm-specific error detection reduced errors by up to 65%.

WHY IS THIS IMPORTANT:

• This clear demonstration of a quantum speedup is a milestone towards achieving quantum advantage. The ability of QAOA to solve larger problems more efficiently than classical methods has implications for fields like logistics and cryptography.

• Also, the collaboration highlights the powerful potential in combining high-performance computing and quantum information science, as well as the combined efforts of academia, industry, and government labs.

NEWS

Tags: SOFTWARE

NEW AI-POWERED QISKIT TRANSPILER SERVICE

BRIEF BYTE: IBM has released the beta version of its Qiskit Transpiler Service to IBM Quantum Premium Plan users.

WHAT HAPPENED: 

• The Qiskit Transpiler Service provides the latest transpilation capabilities from the Qiskit SDK, including new AI-powered transpiler passes for circuit routing and synthesis.

• These passes were introduced at the IBM Quantum Summit 2023 and produced shorter, shallower circuits more quickly than current heuristic methods.

• The beta release expands on an earlier alpha version, improving stability, reliability, and features. It also offers more flexibility by allowing users to choose between AI, heuristic, or automatic optimization.

WHY IS THIS IMPORTANT:

• The beta release of the Qiskit Transpiler Service is a step towards integrating AI into quantum computing workflows. AI-powered optimization could lead to more efficient quantum algorithms and better utilization of quantum hardware.

RESEARCH

Tags: APPLICATION 

OVERVIEW OF QUANTUM COMPUTING FOR SEVERAL AGV SCHEDULING MODELS

BRIEF BYTE: This study explores the application of quantum computing to automated guided vehicle scheduling by introducing two types of quadratic unconstrained binary optimization models and solving them using an optical coherent Ising machine.

WHY: 

• The logistics and transportation industries have seen significant growth, with major companies like Amazon and Jingdong adopting advanced technologies, including AGVs, to improve efficiency and automation. The complexity of scheduling AGVs effectively has become a significant issue, as traditional methods are often too slow or fail to find optimal solutions. This research addresses the challenge of scheduling AGVs in large-scale applications, a problem where traditional computing methods struggle due to high computational demands.

• Previously, solutions have focused on classical approaches to AGV scheduling. These methods have limitations in scalability and computational efficiency. Traditional exact algorithms can find optimal solutions but are impractically slow while inexact algorithms are faster but often converge to local optima rather than the best possible solution.

HOW: 

• The AGV scheduling problem involves a set of AGVs and transportation tasks, each with a starting and ending point. The objective is to optimize either the total travel time of AGVs or the the time to complete all tasks.

• For classical comparison, a mixed integer programming model is used, incorporating binary variables to represent task assignments and arrival times.

• Two QUBO models (node and arc models) are proposed for quantum computing. The node model treats tasks as nodes and their execution order as the path through these nodes. The arc model considers the sequence of tasks as arcs between nodes. Both models use binary variables to represent task assignments.

• Experiments were conducted using the Gurobi solver on a traditional computer to solve the MIP model and the coherent Ising machine was used to solve the QUBO models.

RESULTS: 

• Numerical experiments demonstrated that the coherent Ising machine significantly outperforms traditional mixed integer programming models by achieving an average 92% reduction in computation time.

• The research suggests expanding the models to accommodate more complex and realistic AGV scheduling scenarios, such as varying start and end nodes and integrating path optimization. Combining the strengths of both quantum and classical computing could also lead to increased problem-solving efficiency.

RESEARCH

Tags: QUANTUM INSIGHTS ERROR CORRECTION

OVERVIEW OF DECOHERENCE IN EXCHANGE-COUPLED QUANTUM SPIN-QUBIT SYSTEMS: IMPACT OF MULTIQUBIT INTERACTIONS AND GEOMETRIC CONNECTIVITY

BRIEF BYTE: Different connectivity configurations affect decoherence time in quantum systems under quasistatic Heisenberg noise.

WHY: 

• Previous studies have demonstrated high-fidelity operations in single or two-qubit systems but struggled to maintain this fidelity in multiqubit systems due to decoherence.

• This research investigates the impact of geometric connectivity on decoherence time in quantum systems, versus previous studies which have focused on the common perception that higher connectivity leads to decreased stability.

HOW: 

• The model used in the research focuses on two-qubit interactions with qubits connected through Heisenberg interactions.

• Three types of units (node, stick, and triangle) are connected into larger structures using ring, chain, and tree configurations. These represent different basic arrangements of qubits, with the vertices as qubits and the connections between them as potential sites for quantum gates.

• The quasistatic Heisenberg noise is modeled using a Gaussian distribution for the interaction strengths between qubits and the Monte Carlo method is used to simulate the noise by averaging over many realizations of the Gaussian-distributed interaction strengths.

RESULTS: 

• Ring configurations exhibit greater stability compared to chain configurations which challenges the common expectation that higher average connectivity leads to decreased stability.

• The stick configuration is more stable than the triangle configuration which indicates the influence of geometric shape of qubit arrangements on decoherence time.

• As the system size increases (up to 10 qubits), the decoherence time generally decreases for all configurations. However, the rate of decrease is slower for rings compared to chains.

• Ring configurations with an odd number of qubits show shorter lifetimes due to effects from at least one spin being unable to align oppositely to both its neighbors.

PREPRINT

Tags: QUANTUM INSIGHTS SUPERCONDUCTING

OVERVIEW OF THERMALIZATION AND CRITICALITY ON AN ANALOG-DIGITAL QUANTUM SIMULATOR

BRIEF BYTE: This study demonstrates a quantum simulator with 69 superconducting qubits capable of emulating a 2D XY quantum magnet, revealing insights into thermalization dynamics and phase transitions through advanced state preparation and measurement techniques.

WHY: 

• Current analog quantum simulators often lack versatility while digital quantum simulators are limited by gate errors. This research introduces a hybrid analog-digital quantum simulator that combines both approaches. This is particularly relevant for studying thermalization dynamics, which requires precise state preparation and characterization capabilities not easily achievable in purely analog systems.

HOW: 

• A 2D lattice of 69 superconducting transmon qubits is connected by tunable couplers. This architecture allows for both universal entangling gates and high-fidelity analog simulation.

• A notable innovation is a scalable calibration scheme that accurately determines the bare qubit and coupler frequencies for precise analog simulation.

• The team used cross-entropy benchmarking to assess the simulator's performance.

• By preparing and characterizing states of a 2D XY-magnet with varying energy densities, the researchers investigated the interplay between quantum and classical in the system's phase diagram.

• Measurement techniques to gain insights into the system's behavior included entanglement entropy, multi-qubit vortex correlators, and energy fluctuations.

RESULTS: 

• The key findings of this research include the observation of finite-size signatures of the Kosterlitz-Thouless topological phase transition, a breakdown of the Kibble-Zurek mechanism due to critical coarsening, and the ability to prepare and study thermalized states with tunable energy densities.

• The hybrid simulator's ability to prepare entangled initial states allowed for the investigation of energy transport and the thermalization of spin currents and vorticity.

• These results demonstrate the potential of hybrid analog-digital quantum simulators to tackle complex many-body problems that are beyond the reach of classical computation.

EVENTS

• Now - May 31 | Register for Google/X-Prize Quantum Challenge

• 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

• Intel Corporation Quantum Error Correction Researcher | Remote $162K - $259.4K

• Jacobs Quantum Physicist | Washington DC

• SandboxAQ Senior Product Support Engineer | Remote

• Mesa Quantum Quantum R&D Engineer | Boulder, CO $110K - $140K

• Fermilab Quantum Optics Network Research Associate | Batavia, IL (Hybrid)

• Booz Allen Hamilton Quantum Optics & High Energy Physics Consultant | Arlington, VA $67.7K - $154K

• Carnegie Mellon Software Engineering Institute Research Scientist - Quantum | Pittsburgh, PA (Hybrid)

• AWS Research Scientist, Control Stack, AWS for Center for Quantum Computing | Pasadena, CA $124.1K - $212.8K

• AWS Quantum Research Scientist, AWS CQC Materials | Pasadena, CA $113K - $195.2K

UNTIL TOMORROW.

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