Welcome to the Quantum Realm.

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

🌴 Looks like quantum news took one of those Friday half-days — PsiQuantum & the Chicago quantum park retain #1 spot in number of headlines, the UK government invests in five hubs to encourage quantum advancement in healthcare, cybersecutiry, and quantum-based positioning systems, plus the sunburst quantum Ising model can optimize ergotropy and charging power simultaneously in quantum batteries.

🗓️UPCOMING

Sunday, July 28 | QTM-X Quantum Education Series 7 of 10

📰QUANTUM QUICK BYTES

💡 The UK government announced a £106 million investment in five university-based hubs to advance quantum tech: The UK government will invest £106 million in five hubs to develop quantum technology applications. These hubs, located at universities in Glasgow, Edinburgh, Birmingham, Oxford, and London, will collaborate with industry partners to use quantum technology for R&D. Specific focuses include ultra-sensitive disease diagnosis, quantum sensors for healthcare, quantum internet technologies for cybersecurity, quantum computing advancements, and quantum-based positioning systems for critical infrastructure and autonomous vehicles. The hubs will be managed by UK Research and Innovation and the Engineering and Physical Sciences Research Council, with contributions from various UKRI councils and the National Institute for Health and Care Research.

🔁 PsiQuantum and Chicago are still the headlines of the day: After scouring the web, delaying the post in hope of breaking news to ignite the weekend, yet again headlines are interested in one thing: the soon-to-be Illinois Quantum and Microelectronics Park, soon to host PsiQuantum. However, two new aspects I can add to yesterday’s news include: 1. the quantum campus will feature a cryogenics plant, specially designed for cooling quantum computing infrastructure and 2. a rendering of the future campus 👇️ 

📈 New D-Wave webinar: Bob Sorensen, Hyperion’s Senior Vice President of Research and Chief Analyst for Quantum Computing, discusses a recent study that reveals organizations expect up to 20x ROI from quantum optimization and 21% plan to use quantum computing in production within the next 12-18 months. 👇️ 

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☕️FRESHLY BREWED RESEARCH

SUNBURST QUANTUM ISING BATTERY

QUICK BYTE: The sunburst quantum Ising model describes a quantum battery system composed of a transverse Ising chain with a very small transverse field (serving as an extended charger) and a finite number of isolated qubits (acting as batteries). This model optimizes both ergotropy (extractable work) and charging power with increasing coupling strength, independent of the initial state of the charger, making it experimentally viable and efficient for practical applications.

PRE-REQS:

• A spin model is a simplified representation used to study magnetic systems where spins represent the magnetic moment of particles. Spins interact with their neighbors and the nature of these interactions determines the properties of the model.

• The Ising model is a spin model in which spins on a lattice (of any size) can be in one of two states and interact with their nearest neighbors. The quantum Ising model is one in which a transverse magnetic field is added to the classical Ising Hamiltonian which introduces quantum fluctuations and allows spins to flip between up and down states.

• A transverse Ising chain is a specific use case of the quantum Ising model where spins are arranged in a one-dimensional line and a transverse field is applied; this model is especially useful for understanding how quantum fluctuations affect a system.

• Ergotropy quantifies the usable energy in a quantum system that can be converted to work as opposed to energy that remains as heat or is unavailable.

SIGNIFICANCE: With the emergence of quantum technology has also emerged a deeper interest in the study of quantum thermodynamics. In studying quantum thermodynamics, scientists coined the term "quantum batteries" to describe quantum systems that can store and deliver energy. These batteries have since been studied with great interest due to their potential abilities in storing energy more efficiently, transferring energy nearly instantaneously, powering quantum computers more effectively, finding relevant applications in renewable energy, and serving as energy storage for nanodevices.

While quantum batteries are still largely in their theoretical and experiemental phase, work has already begun in uncovering the effects of different charging schemes, the amount of work that can be extracted, and how entanglement and coherence affect work extraction. Key discoveries coherence as a significant factor in the charging process, collective charging as advantageous over parallel charging, a greater ratio of chargers to batteries is linked to an increase in power stored per battery, and a greater ratio of batteries as compared to chargers is linked to a decrease in power stored per battery. Additionally, if the system has a fixed initial state, charging power can increase while ergotropy decreases.

From this starting point, researchers considered the possibility of implementing a charging scheme where maxmum work extraction and charging power can both be optimized and made idependent of initial state of the charger. To explore this further, the researchers propose doing so through the suburst quantum Ising model.

The sunburst quantum Ising model is a quantum spin model that consists of two interacting integrable systems: a transverse Ising chain with a very small transverse field (acting as an extended charger) and a finite number of external isolated qubits (acting as batteries). By analyzing through the lens of this model, the researchers could investigate further how coupling strength between the charger and batteries affects ergotropy and charging power, and whether these quantities could be optimized simultaneously which would make it more practical for real-world applications.

RESULTS: 

• The sunburst quantum Ising model was studied as a quantum battery, consisting of a transverse Ising chain coupled to external qubits. Unlike other quantum battery models, both maximum ergotropy and charging power increase with coupling strength and optimal work extraction is possible in the strong-coupling limit.

• Ergotropy and charging power are independent of the charger's initial state, making it more experimentally viable

• Linear scaling of ergotropy and maximum power with battery number was observed numerically, but an analytical understanding remains an open question

HONORABLE RESEARCH MENTIONS:

A method to identify quantum entanglement is proposed using Siamese convolutional neural networks and semisupervised learning. By training on synthetically generated datasets, the model can accurately identify entanglement, including challenging positive-under-partial-transposition entangled states. The model's generalization capabilities are improved through symmetry operations and ensemble methods. —> link to Identification of quantum entanglement with Siamese convolutional neural networks and semisupervised learning

Quantum convolutional neural networks are designed for recognizing symmetry-protected topological phases, with a focus on error tolerance. These QCNNs can identify different phases, even in the presence of incoherent and symmetry-breaking errors, by using constant-depth circuits and classical postprocessing. This specific approach reduces the sample complexity required for detecting nonlocal quantum correlations compared to traditional methods. —> link to Error-tolerant quantum convolutional neural networks for symmetry-protected topological phases

ArtA is an automated tool created for Design Space Exploration of quantum-dot spin-qubit architectures in order to optimize performance across various quantum algorithms. Using the SpinQ compilation framework, ArtA explores 29,312 architectures using seventeen optimization methods, and reduces exploration times by up to 99.1%. The study highlights the importance of maximizing quantum gate parallelization and suggests universal architectural features for optimal performance in large-scale spin-qubit devices. —> link to ArtA: Automating Design Space Exploration of Spin Qubit Architectures

UNTIL TOMORROW.