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Welcome to the Quantum Realm.

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

🌇 The University of Illinois Urbana-Champaign announces their presence in the now-infamous Chicago Manhattan Project alongside PsiQuantum. Plus, neural network quantum states get an efficiency boost through symmetrical subspaces, and fine-tuned quantum sensors inspired by classical signal processing.

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🗓️UPCOMING

📰QUANTUM QUICK BYTES

🔐 QED-C's report on quantum technology in finance emphasizes the need for PQC: The Quantum Economic Development Consortium’s recently released Quantum Technology for Securing Financial Messaging report provides a thorough analysis of the impact of quantum computing and communications on the financial sector. An assessment of quantum-resistant technologies recommends federal support for the post-quantum cryptography transition through the support of grants and developing quantum talent as well as advocates for the dual incorporation of quantum key distribution and post-quantum cryptography for the most resilient security approach. The report is the product of a workshop hosted earlier this year to identify high-impact use cases such as secure cross-border transactions and post-quantum transport layer security.

🧪 Quantistry and IQM Quantum Computers partner in the name of hybrid classical-quantum solutions: Quantistry and IQM Quantum Computers have signed an MoU to explore hybrid classical-quantum solutions for R&D challenges in the chemical and materials industry. This collaboration will integrate IQM's advanced quantum systems, including their recently launched cloud service for algorithm development, into Quantistry's chemical simulation platform (QuantistryLab). Quantistry's Chief Growth Officer, Dr. Arturo Robertazzi, emphasizes that the partnership will also focus on sustainability and renewable energy.

🔍 A protocol to fine-tune quantum sensors improves their sensitivity: Researchers from North Carolina State University and MIT have designed a protocol to improve the sensitivity of quantum sensors' by fine-tuning quantum systems to detect specific signals. Inspired by classical signal processing, this protocol couples a qubit with a bosonic oscillator, allowing the sensor to focus on targets of interest. The ability to efficiently extract information from infinite-dimensional systems without requiring repeated measurements serves as a general framework for quantum sensing using readily available quantum resources.

🏙️ The University of Illinois Urbana-Champaign leads the new Illinois Quantum and Microelectronics Park in Chicago: The Grainger College of Engineering at the University of Illinois Urbana-Champaign will lead the Illinois Quantum and Microelectronics Park in Chicago, the state-funded $500 million endeavor previously compared to the Manhattan Project. By aligning the goals of industry alongside academia in a central location, the project is expected to drive the development of a quantum market projected to be worth $2 trillion in the next decade. The University of Illinois will collaborate with public and private stakeholders, supported by recent federal and state investments such as the DARPA-Illinois Quantum Proving Ground project. PsiQuantum has also announced plans to build a $1 billion utility-scale fault-tolerant quantum computer at the park.

🧠 IEEE Quantum Week 2024 is approaching: IEEE Quantum Week 2024 will host the global quantum community, industry and academia alike, in Montréal from September 15-20. The event will feature keynote speakers, tutorials, workshops, the latest in research, panel discussions, and a career fair. Special pricing is available for prospective attendees who sign up before August 5th!

☕️FRESHLY BREWED RESEARCH

LEARNING EIGENSTATES OF QUANTUM MANY-BODY HAMILTONIANS WITHIN THE SYMMETRIC SUBSPACES USING NEURAL NETWORK QUANTUM STATES

QUICK BYTE: Neural network quantum states are proposed for efficiently representing quantum states as they address the challenges of the exponential growth of the Hilbert space by confining the representation to a symmetric subspace. By integrating lattice symmetries and using Markov chain Metropolis sampling, the method reduces the number of parameters and improves accuracy.

PRE-REQS:

• The symmetric subspace refers to a subspace of the Hilbert space where the state of the wavefunction does not change even if the permutations of the particles do — the state of the system does not change when any two particles are swapped.

SIGNIFICANCE: Neural network quantum states have been studied as a promising alternative to traditional wavefunction representations for quantum states as they can handle the vast expanse of the Hilbert space more effectively. Common models include Restricted Boltzmann Machines, feedforward neural networks, variational autoencoders, and generative adversarial networks. However, despite the efficiency demonstrated in neural network quantum states, the exponential growth of the Hilbert space is still a challenge when it comes to achieving high precision in representing quantum states.

By confining NQS within a symmetric subspace using symmetries such as global spin inversion and spin conservation, the method presented reduces the number of parameters required and significantly improves the accuracy of the representation. The symmetric subspace, being much smaller, allows the use of more economical models and requires fewer Monte Carlo samples.

The method was validated using the frustrated spin-1/2 J1-J2 antiferromagnetic Heisenberg chain, and the results demonstrated that symmetrized neural network states achieved a two-order magnitude reduction in energy errors compared to conventional NQS methods. Additionally, the study examined low-energy dispersion and showed that the smaller variational space greatly improved the accuracy of variational energy calculations. Overall, this research presents a significant advancement in the application of NQS to complex quantum systems as it offers a practical solution to the quantum simulation challenges of scalability and precision.

RESULTS: 

• NQS confined to a smaller symmetric subspace reduces the number of parameters and improves accuracy, as compared to conventional methods

• Using lattice symmetries, including global spin inversion and spin conservation, resulted in a larger gap between the ground state and the lowest excited state within the subspace

• Validation using the frustrated spin-1/2 J1-J2 antiferromagnetic Heisenberg chain demonstrated that the symmetrized neural network states outperform the standard library of neural network states in both accuracy and convergence properties

HONORABLE RESEARCH MENTIONS:

The use of a quantum spin chain for energy storage and release demonstrates that introducing topological frustration in the spin chain, through antiferromagnetic interactions with an odd number of sites and periodic boundary conditions, significantly improves the battery's performance, and makes it more resilient to decoherence, faster to charge, and capable of more efficient energy transfer. This improvement is attributed to the unique energy spectrum and quantum correlations induced by topological frustration. —> link to Frustrating Quantum Batteries

The effect of parallel gating on gate fidelities in different configurations of flip-flop qubits is explored under realistic noise conditions. The study simulates gate fidelities in linear, square, and star arrays of four flip-flop qubits, finding that while linear arrays generally yield the lowest infidelities for single and multiple parallel operations, the infidelities increase with the number of parallel operations. Particularly, parallel two-qubit gates show very high infidelities, suggesting that they should be minimized in small arrays to maintain computational accuracy. —> link to Impact of Parallel Gating on Gate Fidelities in Linear, Square, and Star Arrays of Noisy Flip-Flop Qubits

A general algorithm for simultaneously characterizing quantum states and state preparation and measurement noise is proposed. This algorithm allows for the complete characterization of SPAM errors in any quantum system by analyzing the properties of the linear operator space induced by unitary operations. It can output both the quantum state and the noise matrix up to a single gauge degree of freedom, which can be broken using prior knowledge about the state or noise properties. —> link to Universal framework for simultaneous tomography of quantum states and SPAM noise

UNTIL TOMORROW.