Welcome to the Quantum Realm. 

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

❄️ It’s chilling here in Flatland. Between QuTech’s 2D Majorana qubits, China’s advanced dilution refrigerator for quantum chips, and Diraq’s high-fidelity single quantum dots, there’s something for all.

🗓️ THIS WEEK

Wednesday, June 5 - Friday, June 14 | IBM Quantum Challenge 2024 — Register here!

Thursday, June 13 | A Survey of Quantum Resource Estimation Tools

Saturday, June 15 | Simulation of Quantum Chemistry Hamiltonians with Relativistic Effects w/ Washington DC Quantum Meetup

📰 NEWS QUICK BYTES

⏩️ Fast-Tracking Quantum Computing with DDS Firmware: Researchers at University Kaiserslautern have sped up quantum computing development with new DDS firmware from Spectrum Instrumentation. This innovation simplifies the control of lasers trapping single atoms, key for building quantum circuits, and upgrades the precision and flexibility of their experiments.

🧇 Record 99.9% Single Qubit Control Accuracy Achieved by Diraq Using CMOS Technology: Diraq, in collaboration with imec, achieved a record 99.9% control accuracy for a single silicon quantum dot using industry-standard CMOS materials on a 300mm wafer. Notably, this technology leverages standard semiconductor manufacturing processes.

🧊 China Unveils Advanced Origin SL1000 Dilution Refrigerator: The Anhui Quantum Computing Engineering Research Center introduced the Origin SL1000 dilution refrigerator in Hefei City. This device enhances cooling capacity and spatial efficiency, supporting the development of 100+ bit superconducting quantum chips.

📐 QuTech Achieves 2D Majorana Particles for Quantum Computing: QuTech researchers created Majorana particles in a two-dimensional plane using superconductors and semiconductors. This advancement improves the flexibility and scalability of Majorana qubit experiments which allows for more extensive studies and integration into quantum computing networks.

🛩️ Quantum’s Practical Applications and Industry Integrations: Quantum computing is now actively used in sectors like defense, supply chain management, and aerospace. Companies like D-Wave, IBM, and NTT lead with hybrid quantum-classical systems, emphasizing quantum error correction and AI integration to solve complex problems and drive business efficiencies.

🧲 Light-Induced Magnetism for Quantum Circuits: Graz University of Technology researchers found that infrared light can generate small magnetic fields in certain molecules which helps with constructing quantum computing circuits. This study shows metal phthalocyanines producing localized magnetic fields, potentially serving as high-precision optical switches in quantum computers.

☁️ UK's Cloud Computing Leadership: Carmen Palacios-Berraquero, CEO of Nu Quantum, highlights the UK's emergence as a global leader in cloud computing and emphasizes the need for ongoing government support to help the industry scale up. Watch interview below:

☕️ FRESHLY BREWED RESEARCH

Retrieving Nonlinear Features from Noisy Quantum States: The proposed "observable shift method" accurately retrieves high-order moments from noisy quantum states. This method uses entanglement and requires only one quantum operation which leads to lower overhead and improved accuracy compared to conventional techniques; numerical experiments confirm its effectiveness in mitigating noise in quantum systems. Breakdown here.

Quantum Positional Encodings for Graph Neural Networks: Quantum positional encodings for graph neural networks use the long-range correlations of quantum systems and improve model performance on benchmarks and large-scale datasets. By integrating quantum features derived from Hamiltonian evolutions, the approach provides richer graph representations and demonstrates better expressiveness and potential advantages over classical methods. Breakdown here.

Quantum state preparation for multivariate functions: By encoding multivariate functions using truncated Fourier and Chebyshev series, protocols for quantum state preparation can avoid complex arithmetic circuits and quantum Fourier transforms. The methods are both numerically validated and experimentally demonstrated and show improvements in resource efficiency for state preparation in quantum algorithms. Breakdown here.

Studying phonon coherence with a quantum sensor: A superconducting qubit is used as a quantum sensor to perform phonon number-resolved measurements on a piezoelectrically coupled phononic crystal cavity. This method reveals nonexponential relaxation and state size-dependent dephasing rates, attributed to interactions with two-level system defects. This is a contribution towards our understanding of TLS-induced phonon decoherence in quantum, which is important for the development of quantum technologies involving mechanical coherent states.

The SpinBus architecture for scaling spin qubits with electron shuttling: This SpinBus architecture uses electron shuttling to connect qubits in a scalable, two-dimensional array and addresses limitations in wiring fan-out and inter-qubit crosstalk. This method, as validated through simulations, achieves high operation fidelities exceeding 99.9% and supports at least 144 qubits with room temperature control, with potential for much larger systems using cryogenic control circuits. The architecture is based on established semiconductor technology could be integral in reaching scalability requirements for practical quantum computing.

UNTIL TOMORROW.

BREAKDOWN

Retrieving Nonlinear Features from Noisy Quantum States

🔍️ SIGNIFICANCE: 

• Quantum noise complicates estimation of high-order moments of quantum states by distorting the quantum states. The study introduces a method to retrieve high-order moments from noisy quantum states using a technique called the "observable shift method." This method induces lower overheads and avoids the complexity of sampling different quantum operations.

🧪 METHODOLOGY: 

• The researchers established that the retrieval of high-order moments from noisy quantum states is doable only if the noise channel is invertible, a condition that is both necessary and sufficient for a quantum protocol to achieve this goal.

• They developed a technique called the "observable shift method" which requires only one quantum operation. This reduces the need for complex sampling procedures and uses a retriever quantum channel and classical postprocessing to recover the desired moments.

• The study demonstrated that using entangled protocols are more effective in retrieving high-order information compared to conventional product protocols.

• They constructed a scalable protocol that addresses depolarizing channels in large quantum systems and makes the method applicable to practical quantum computing environments.

• Numerical experiments showed the effectiveness of the proposed method in mitigating depolarizing noise on the ground state of the Fermi-Hubbard model.

📊 OUTCOMES & OUTLOOK: 

• The study confirms that high-order moments can be accurately retrieved from noisy quantum states if the noise channel is invertible.

• The proposed observable shift method achieves optimal sampling complexity and lowers the overhead compared to existing methods.

• The method's simplicity and reduced resource requirements make it a strong candidate for practical implementation on current quantum devices. The research provides step-by-step protocols for common noise types, such as depolarizing and amplitude damping channels.

• The results highlight the effectiveness of entangled protocols in retrieving high-order information while contrasting the limited utility of entanglement in existing quasiprobability decomposition methods.

• These results are important pieces of understanding quantum noise's impact on high-order information extraction while providing practical guidance for mitigating its effects.

BREAKDOWN

Quantum Positional Encodings for Graph Neural Networks

🔍️ SIGNIFICANCE: 

• Traditional methods involved in building GNNs, such as message passing neural networks, have limitations including oversmoothing, oversquashing, and difficulty handling heterophilic data. The proposed quantum positional encodings address these limitations and can compute complex topological features of graphs that are intractable with classical algorithms.

🧪 METHODOLOGY: 

• Graphs are mapped to quantum states using Hamiltonians, such as the Ising Hamiltonian and XY Hamiltonian, which encode the graph's topology.

• Correlations between qubits are measured to obtain positional encodings. These correlations can be used similarly to Laplacian eigenvectors in traditional graph learning models.

• Continuous-time quantum random walks and discrete quantum-inspired random walks are used to capture graph features.

• The positional encodings obtained from quantum states are integrated into classical GNN architectures, allowing the use of well-established classical optimization techniques while benefiting from quantum features.

📊 OUTCOMES & OUTLOOK: 

• Models incorporating quantum features outperformed classical counterparts on various benchmarks, including ZINC, MNIST, CIFAR10, PATTERN, and CLUSTER datasets.

• Quantum features were shown to be more expressive for certain graph instances than classical methods, particularly on strongly regular graphs, which are challenging to distinguish using traditional approaches.

• The study provides strong indications that quantum hardware can lead to high-performance architectures for graph learning tasks. While the research focused on classically tractable quantum features, it suggests that further improvements could be realized with full quantum hardware capabilities.

BREAKDOWN

Quantum state preparation for multivariate functions

🔍 SIGNIFICANCE: 

• This research addresses the critical challenge of preparing quantum states for quantum algorithms, particularly when qubit registers represent a discretization of continuous variables defined by multivariate functions. Traditional methods for quantum state preparation often rely on complex arithmetic circuits, quantum Fourier transforms, or multivariate quantum signal processing, which can be resource-intensive and complex. This paper introduces protocols that simplify state preparation by linearly combining block-encodings of Fourier and Chebyshev basis functions without the complex techniques.

🧪 METHODOLOGY: 

• The target function is approximated using truncated Fourier or Chebyshev series.

• The basis functions of the series are block-encoded into quantum circuits. For Fourier series, this involves using phase shift gates to encode the Fourier basis functions. For Chebyshev series, block-encoding is more complex and involves constructing circuits that use qubitized block-encoding techniques to represent the Chebyshev polynomials.

• The encoded basis functions are combined linearly using a method called linear combination of unitaries for efficient construction of the target quantum state.

• The protocols avoid using expensive arithmetic circuits by using the properties of the Fourier and Chebyshev series.

📊 OUTCOMES & OUTLOOK: 

• The proposed protocols reduce the number of required two-qubit gates and ancilla qubits compared to traditional methods.

• The methods were numerically validated by preparing bivariate Student’s t-distributions, 2D Ricker wavelets, and electron wavefunctions in a 3D Coulomb potential. These demonstrations show the practical applicability of the protocols to various fields such as finance, image processing, and quantum chemistry.