Welcome to the Quantum Realm. 

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

⌛️ Only time will tell if previous technological advances have taught us the importance of collaboration and accessibility, but today we celebrate the educators around the world advocating for inclusive practices and the many passionate individuals dedicating their time and effort towards providing resources. Plus, entropy as it relates to quantifying coherence and the computing paradigm taking on optimization.

🗓️ UPCOMING

Tuesday, July 9th | Quantum Formalism Research Detours Seminar Series

Tuesday, July 9th | TQN Q-CTRL Black Opal Quantum Programming Learning Sessions

Sunday, July 14 | QTM-X Quantum Education Series 6 of 10: Quantum Hardware

📰 NEWS QUICK BYTES

🤝 Zapata AI and D-Wave expand their partnership for AI solutions: Zapata AI and D-Wave have announced an expanded partnership to accelerate the development of integrated quantum and generative AI solutions within D-Wave's Leap cloud platform. This collaboration will combine Zapata’s Universal Generative AI software and D-Wave’s real-time quantum cloud service to improve model training efficiency and performance for the purpose of streamlining business processes and driving sustainability.

🌟 Women for Quantum are calling for systemic changes to promote equality and inclusivity in the quantum community: The "Women for Quantum" manifesto, authored by a group of tenured professors in quantum physics, highlights the underrepresentation of women in the field and the inefficacy of current initiatives geared at improving gender balance. The document outlines a set of values and goals to encourage a more inclusive, supportive, and collaborative scientific community. Key values include diversity, respect, integrity, and the empowerment of underrepresented groups. The manifesto also calls for a transformation in scientific leadership, funding distribution, and decision-making processes, emphasizing the importance of shared power and transparent practices.

🧩 Institute for Quantum Computing team develops modular, open-source software package for modeling QKD protocols: Dr. Norbert Lütkenhaus and his team at the Institute for Quantum Computing have created a modular, open-source software package for modeling quantum key distribution protocols that allows users to calculate secure quantum key generation rates with user-submitted variables for real-world scenarios. By breaking down complex coding challenges into smaller modules, the software facilitates easier integration of experimental data. The modular design also benefits training and teaching new researchers as they can focus on specific aspects of the QKD model. The team is actively working on bridging the gap between theoretical and experimental quantum research through collaboration with other researchers in addition to the open-source release which encourages global participation.

📚 Top five quantum algorithms from Q2 2024: Xanadu shares their top five quantum computing papers of Q2 2024 (excluding their own). The featured papers include significant advancements such as a 69-qubit study on thermalization and criticality, an efficient method for matrix product state preparation, a breakthrough in error correction for ion-trap quantum computers, a new state-of-the-art method for quantum integer multiplication, and a landmark paper in quantum chemistry simulations. Honorable mentions include improvements in quantum amplitude estimation, an optimal quantum adder, and innovative approaches in spin coupling and dynamic programming for quantum algorithms.

🌌 Bell Prize Recipient Dr. John Preskill discusses his journey into quantum physics: In this exclusive interview, John Preskill, a pioneering physicist and director of Caltech's Institute for Quantum Information and Matter, shares his journey into quantum physics and the current state of quantum computing. He describes how his interest in quantum information science began with contemplating black holes and Peter Shor's algorithm. Preskill highlights the profound potential of quantum computing, as well as the need for interdisciplinary collaboration between theory and experiment. Preskill also discusses the future impact of quantum simulations on understanding quantum gravity and emergent space-time. He advocates for international cooperation and public sector support to achieve long-term advancements in quantum information science.

🖥️ QTM-X Quantum Community Sunday quantum sessions: QTM-X is supporting the transition to a quantum future by hosting online Sunday evening sessions to discuss quantum technologies, global challenges, and the impact on business and cybersecurity. With the growing demand for quantum skills as well as the need for accessible resources, these sessions are a great opportunity to meet others in the field as well learn more about the state of quantum technology. Link for this coming Sunday’s session top of the newsletter!

🎓 HQS Quantum Simulations webinar series: HQS Quantum Simulations has launched a comprehensive webinar series to introduce their new HQStage software toolkit for quantum simulation and computing. The series starts with introductory sessions on installation, feature exploration, and practical use cases, followed by in-depth webinars on advanced topics like non-unitary gates in quantum algorithms and AI-assisted model building. The webinars are free, with some requiring registration for advanced sessions. The program targets quantum computing researchers, materials scientists, and quantum chemists. More information and registration details are available in the link!

🎤 Quantum Formalism community's seminar series: The Quantum Formalism community is launching a seminar series with its first edition on July 9th. This "Research Detours" seminar provides a platform for researchers to present and discuss their projects that haven't yet had the chance to be shared. The first speakers include Mehdi Chehimi, who will discuss quantum smart cities; Abderrahim Adrabi, who will present on a virtual laboratory for visualizing mathematical concepts and topological quantum computing components; and George Zipperlen, who will explore arithmetic with quantum harmonic oscillators. The goal is to provide an environment for encouraging discussions and collaborations within the community.

☕️ FRESHLY BREWED RESEARCH

Relative Entropy of Coherence Quantifies Performance in Bayesian Metrology: Quantum metrology relies on quantum mechanical properties like superposition and entanglement to achieve high-precision measurements, surpassing classical methods. This research uses the relative entropy of coherence, generalized to positive operator-valued measures, to offer a comprehensive tool for Bayesian metrology, applicable to both unitary and dissipative dynamics, and discrete settings. The results highlight the role of coherence in improving quantum sensing and information processing technologies. Breakdown here.

Entropy Computing: A Paradigm for Optimization in an Open Quantum System: Entropy computing offers a scalable solution for NP-hard optimization problems through a hybrid photonic-electronic system, addressing both the limitations of traditional quantum annealers and coherent Ising machines. This paradigm uses measurement-based feedback to effectively manage a broad range of NP-hard problems. The Dirac-3 machine demonstrates superior performance in solving non-convex and combinatorial optimization problems compared to classical methods. Breakdown here.

Superselection rules and bosonic quantum computational resources: A framework is introducted for identifying and classifying quantum optical non-classical resources based on their computational power in a bosonic quantum computer. By establishing a correspondence between continuous variable states and single photons in distinct modes, the study demonstrates how superselection rules and mode entanglement contribute to quantum computational advantage. It also highlights the role of non-Gaussian operations in creating mode entanglement.

Universal terminal for cloud quantum computing: A proposed universal terminal for cloud quantum computing enables personal edge devices to offload computational tasks to scalable quantum computers via edge servers with cryogenic components and fault-tolerant schemes. The setup uses Rydberg cavity-QED technology to facilitate entanglement of logical qubits encoded across different encoding protocols. By using a universal photonic interface, the system is meant to connect processors and quantum memories within a quantum cloud infrastructure and effectively improve the scalability and accessibility of quantum computing for mobile users.

Lagrangian Relaxation Based Parallelized Quantum Annealing and its Application in Network Function Virtualization: The Lagrangian relaxation based parallelized quantum annealing algorithm is designed to address large-scale optimization problems in network function virtualization. By decomposing complex problems into smaller subproblems, which are then solved using multiple quantum computers, quantum annealing and Lagrangian relaxation can be used to achieve efficient solutions. The proposed method is specifically applied to the VNFs scheduling problem in NFV networks and shows superior performance and scalability as compared to classical algorithms.

Function Smoothing Regularization for Precision Factorization Machine Annealing in Continuous Variable Optimization Problems: A new function smoothing regularization method is used to improve precision factorization machine quantum annealing for continuous variable optimization problems. The Hamiltonian function surface, when using traditional factorization machine approaches, becomes very noisy, and hampers the efficiency of quantum annealing. The proposed FSR method reduces this noise, improves the generalization performance of FMQA and successfully applies it to practical problems like predicting the nanophysical properties of contrast agents.

UNTIL TOMORROW.

BREAKDOWN

Relative Entropy of Coherence Quantifies Performance in Bayesian Metrology

🔍️ SIGNIFICANCE: 

• Quantum metrology is the science of using quantum mechanical systems for high-precision measurements. The quantum mechanical properties of superposition and entanglement are what enable quantum metrology to achieve greater accuracy as compared to classical methods.

• Coherence is a fundamental property of quantum systems that defines their ability to exhibit superposition. It is fundamental to technologies such as quantum computing, as well as important to quantum metrology as it is linked to the precision of measurements. Understanding how to quantify coherence assists us in designing better performing quantum technology.

• Quantum resource theories provide frameworks for quantifying quantum properties such as coherence. Two approaches within quantum metrology are the Fisher information approach and Bayesian metrology. Fisher information measures how much information an observable variable provides about an unknown parameter. However, this approach often assumes continuous parameter evolution and known operating points, which are not always practical. Bayesian metrology, on the other hand, uses Bayesian probability theory for measurement and parameter estimation. It quantifies the uncertainty in parameter estimates and provides a framework for decision-making based on the updated probability distribution. This makes it particularly helpful for scenarios where parameters are unknown or complex.

• The significance of using Bayesian metrology to quantify coherence is that it offers a more comprehensive view of how coherence impacts measurement. This research differentiates itself by using the relative entropy of coherence and its generalization to positive operator-valued measures to provide a more versatile and comprehensive tool for Bayesian metrology. Since this method applies to both unitary and dissipative dynamics and accommodates discrete settings, it expands the scope of practical applications in quantum technologies.

🧪 METHODOLOGY: 

• First, the theoretical foundation was built out to link the relative entropy of coherence with Bayesian metrology. The authors define the relative entropy of coherence and introduce the CXI equality, which states that this coherence measure equals the difference between the optimal Holevo information and the mutual information obtained from measurements.

• They extend their framework to include positive operator-valued measures, which are more general than projective measurements.

• Naimark’s dilation is used to prove the validity of their coherence measure in this broader context.

📊 OUTCOMES & OUTLOOK: 

• The CXI equality is arguably the most profound finding from this paper as it quantifies the information gain from measurements using the relative entropy of coherence. It’s also shown that the ensemble coherence measure can effectively indicate the amount of information in superpositions that are inaccessible with a given measurement scheme. This provides insights into how much additional advantage joint measurements on multiple states can offer.

• This approach allows for more precise and general quantification of measurement performance, which could lead to improved quantum sensing and information processing technologies.

• Research inspiration: discovering how it applies to multiparameter estimation; using insights from coherence to understand quantum effects on a large scale

BREAKDOWN

Entropy Computing: A Paradigm for Optimization in an Open Quantum System

🔍️ SIGNIFICANCE: 

• NP-hard problems are the most difficult computational problems to solve and where we most expect to see quantum advantage. Quantum annealers use continuous time evolution to solve certain optimization problems, including some NP-hard problems, often resulting in speedup. However, the realization of quantum annealers on superconducting platforms remains a challenge due to limitations on connectivity and scalability.

• For quantum computers that use matter over light (for example, superconducting qubits over photons as qubits), we must take great strides to ensure that the machine itself is isolated from environmental triggers which might introduce noise into the system and compromise the computation — where our problems with scalability arise.

• While the coherent Ising machine is a popular quantum analog due to its superior time-to-convergence compared to annealers, its main drawback is the need to avoid external disturbances. Additionally, many NP-hard problems do not directly map to the Ising framework.

• To address these challenges, this research introduces the entropy computing paradigm for optimization within open quantum systems, addressing limitations of traditional closed quantum systems that constrain scalability and practical implementation. A hybrid photonic-electronic system uses measurement-based feedback to solve non-convex optimization problems. Unlike conventional quantum annealers and Ising machines, entropy computing effectively manages a broad range of NP-hard problems.

🧪 METHODOLOGY: 

• A hybrid photonic-electronic computer was designed to implement entropy computing through a feedback loop that stabilizes a ground state.

• The system encodes qudits using temporal photonic modes in the time-frequency degree of freedom, which are then processed through a mixer comprising beamsplitters, optical delay lines, and switches. This setup conditions the quantum reservoir, promoting the evolution of qudits representing lower energy states of a target Hamiltonian.

• The hybrid system integrates an electro-optical modulator, a nonlinear optical circuit, and single-photon detectors, with feedback loops managed by a field-programmable gate array to iteratively evolve quantum states and apply loss mechanisms to suppress unwanted states and promote convergence.

📊 OUTCOMES & OUTLOOK: 

• The entropy computing machine, Dirac-3, is shown to successfully solve non-convex and combinatorial optimization problems, while outperforming classical gradient descent algorithms and semi-definite programming in solution quality. For instance, Dirac-3 achieved high success rates in finding ground states for a non-convex quadratic problem and provided superior results in max-cut, max-3-cut, and max-4-cut problems compared to SDP.

• The hybrid system's ability to operate near the single-photon regime improves optimization performance by avoiding local minima. Entropy computing could become a powerful tool for optimization tasks in quantum computing, relevant to applications like portfolio optimization, resource allocation, and machine learning.

• Research inspiration: entropy computing with photonic integrated circuits for energy consumption reduction; applying entropy computing to optimization problems realted to machine learning and large-scale resource allocation