Welcome to the Quantum Realm. 

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

Forget all about quantum speed-ups. Quantum space-ups may be the real advantage — recent work shows quantum computers excel in memory efficiency in terms of max directed cut problem. Plus, quantum for eyes and ears: how it’s improving ophthalmology imaging and integrating with acoustic resonators for scalability.  

And — experimentally realized Fibonacci anyons using superconducting quantum processor

🗓️ UPCOMING

Sunday, July 7 | QTM-X Quantum Education Series 6 of 10

📰 NEWS QUICK BYTES

🧠 Quantum memory efficiency unlocked by quantum computers: Researchers at Sandia National Laboratories and Boston University discovered that while quantum computers may not excel not in speed for solving the advanced maximum directed cut problem, they do excel in memory efficiency — they use exponentially less as compared to classical computers. This revelation was recently presented at the Symposium on Theory of Computing and implies that focusing on memory efficiency could reveal more practical uses for quantum computing beyond speed.

👀 Quantum for ophthalmology diagnostics and treatment: In ophthalmology, quantum computing is being used in imaging for earlier and more accurate detection of diseases such as diabetic retinopathy and macular degeneration through improved image resolution and processing speed. Over the course of several studies, trends are emerging that show quantum’s high accuracy in classifying eye diseases using hybrid quantum-classical models. By combining quantum computing with AI and deep learning in future applications, researchers may develop predictive models and personalized treatments to improve patient care outcomes.

⚡ Will quantum cut data center energy demand? As AI's energy needs surge, quantum computing may be the sustainable alternative we need by reducing carbon emissions in data centers. Riverlane is actively working towards improving quantum computing's efficiency and reducing global power consumption. By joining the Quantum Energy Initiative, Riverlane aligns with global efforts to address the energy demands of scaling quantum technologies as well as mitigate climate change impacts.

🔊 Acoustic Resonators Propel Quantum Computing Scalability: Acoustic resonators coupled with superconducting qubits can produce squeezed mechanical states and enable a full set of quantum gates. This breakthrough overcomes the linearity limitations of resonators and implies efficient, long-lived quantum states essential for advanced quantum algorithms. By integrating these resonators with qubits, we may soon see an emergence of hybrid quantum computing platforms that lead in both scalability and coherence.

🤝 With IonQ’s support, South Korea becomes a key player in quantum: Dr. Martin Roetteler, associated with IonQ, recently highlighted South Korea's rapid ascent as a global quantum computing leader. This emergence is driven by substantial government investment, industry commitment, availability of educational participation, and innovative startups. IonQ's engagement in South Korea includes projects in optimization, machine learning, and quantum chemistry. With continued investment and strategic partnerships, South Korea may just be the next powerhouse in quantum computing.

☕️ FRESHLY BREWED RESEARCH

Integrating Quantum Algorithms Into Classical Frameworks: A Predictor-corrector Approach Using HHL: A predictor-corrector framework for the HHL quantum algorithm is used to overcome the readout problem as well as show polynomial advantage. Specifically, this approach integrates HHL into a broader computational framework and focuses on reducing the need for full classical solves at every time step. In two different proposed methods, the solution space is sampled at each time step and it is determined whether or not a full classical solve is needed. If the classical solve can be bypassed, the computational efficiency can effectively be optimized while still preserving accuracy. Breakdown here.

Non-Abelian braiding of Fibonacci anyons with a superconducting processor: Previous attempts to demonstrate non-Abelian braiding of anyons for quantum computation were purely theoretical or relied on simpler anyon models that did not achieve universal quantum computation. This research overcomes significant barriers to fault-tolerant quantum computation by experimentally realizing non-Abelian topologically ordered states with Fibonacci anyons using a superconducting quantum processor. This advancement opens new possibilities for quantum computers based on topological qubits. Breakdown here.

Dataflow-Based Optimization for Quantum Intermediate Representation Programs: A dataflow-based optimization technique for QIR programs is presented as a way to reduce redundant operations within quantum computations. Additionally QDFO integrates seamlessly with the LLVM compiler framework, providing specific optimizations for QIR that leverage existing LLVM infrastructure. Through experiments and case studies on real-world quantum programs, QDFO is shown to reduce the size and complexity of QIR code.

Efficient Quantum Algorithm for Filtering Product States: A quantum algorithm is used to prepare states with small energy variance at a target energy. Previous methods are costly or rely on classical post-processing while this algorithm directly prepares finite energy states using a Lorentzian filter applied to a product state, which can be implemented in polynomial time via adiabatic evolution. This provides a practical tool for probing finite energy physics on quantum devices along with the study of many-body systems and quantum thermalization.

Information encoding and encryption in acoustic analogues of qubits: The acoustic analogues of qubits, logical phi-bits, can be used for scalable and efficient data encryption, which is directly relevant to data security. The nonlinear properties of acoustic metamaterials can be used to encode and encrypt information as a potentially quantum-resistant solution. Logical phi-bits are shown to be a promising new avenue for secure data encryption as they are relatively scalable, but additional work will be needed to explore comprehensive security analyses against quantum threats.

UNTIL TOMORROW.

BREAKDOWN

Integrating Quantum Algorithms Into Classical Frameworks: A Predictor-corrector Approach Using HHL

🔍️ SIGNIFICANCE: 

• Hybrid quantum-classical algorithms are regularly touted as the most sensible solution in the NISQ era (and possibly beyond) for clear reasons. Instead of focusing solely on pure quantum computation, integrating a QPU as one feature within a larger process primarily run on classical hardware can provide the highest level of advantage by balancing both speedups and computational costs. This quote from the article provides a profound summary all its own:

• One of the standout features of this work is that it’s based on the acceptance that not all applications can benefit from quantum computation alone. In real-world scenarios, quantum does not encompass the entire ecosystem; it’s one part of a whole.

• The proposed solution is a predictor-corrector approach that can bypass complications from quantum readout and the costs of classical computation. This integration aims for a practical polynomial advantage, rather than purely theoretical exponential speedups, thus enhancing the practicality and utility of quantum computing in solving classical problems.

🧪 METHODOLOGY: 

• The HHL algorithm is modified to function as a predictor-corrector rather than a direct solver. The main idea is to evaluate the change in solutions between time steps rather than solving for the solution at each step — saving significantly on computational cost.

• Two main predictor-corrector schemes are explored for comparison: a hybrid predictor-corrector and a fully quantum predictor-corrector.

• In the H-PC method, the HHL algorithm is applied to obtain a sample distribution at each time step and a classical Chi-squared test is used to see if the distribution is significantly varied from the previous step. If it isn’t, the computationally expensive full classical solve can be bypassed.

• In the Q-PC method, solutions are stored as quantum states and a quantum swap test is used to determine the overlap between successive states. If the overlap is deemed sufficient, the classical solve can be skipped. This approach does reduce the number of samples required since it only requires a single ancilla bit measurement.

• To analyze in terms of real-world scenarios, the methods are applied to incompressible smoothed particle hydrodynamics for fluid dynamics simulations. This is extended to consider the application to plasma and reactive flow simulations as well to demonstrate generality.

📊 OUTCOMES & OUTLOOK: 

• Both the hybrid and fully quantum predictor-corrector methods demonstrate that they can skip updating the solution for a substantial proportion of time steps while still maintaining agreement with the global solution. They effectively mitigate data transfer bottlenecks by reducing the frequency of full classical solves and leveraging quantum sampling methods.

• Specifically, in simulations such as the Taylor-Green Vortex and the 2D dam break problem, the methods retained solution accuracy while reducing computational costs. For the Taylor-Green Vortex simulation, the results showed that both hybrid and quantum PCs skipped updating pressure for approximately 45-50% of the time steps without excessive deterioration of the global solution. For the 2D dam break simulation, the Q-PC algorithm could accurately model critical aspects of fluid dynamics while updating the pressure only 16% of the time and still maintaining minimal differences with the full classical solve.

• By reframing the way we think of incorporating algorithms into larger computational processes, we can more effectively find new ways to gain advantage by carefully considering when and how we perform computational tasks.

• The proposed methodologies demonstrate both practicality in terms of cost and resources as well as versatility in that they can be applied across fields like fluid dynamics, plasma simulations, and reactive flows. Additionally, the predictor-corrector methods improve upon computational efficiency by intelligently determining when a full classical solve is absolutely necessary, so that both the strengths of classical and quantum computing can be leveraged to their full potential.

BREAKDOWN

Non-Abelian braiding of Fibonacci anyons with a superconducting processor

🔍️ SIGNIFICANCE: 

• Arguably, one of the most significant barriers to fault-tolerant computation is the need to incorporate error correction. This has led to significant interest in toplogically ordered systems. These systems are less susceptible to noise and disturbances because the information is encoded globally instead of in individual qubits. As long as the topological order is preserved, the system itself is preserved.

• In topologically ordered states, excitations lead to quasiparticles called anyons. Since these excitations are guided by tolopological rules, they too are resistant to noise. Fibonacci anyons (number of possible states follows Fibonacci sequence) in particular are predicted to be considerably advantageous for quantum computations because they can perform universal quantum gates through braiding.

• While they may theoretically be prime candidates, they are unfortunately difficult to realize experimentally because they typically require exotic states of matter that are difficult to create and control.

• Previous efforts have been limited to theoretical exploration or quantum simulations with simpler Ising anyons that unfortunately do not support universal quantum computation. This research is outstanding for the field of quantum computing because it successfully demonstrates the experimental realization of non-Abelian topologically ordered states with Fibonacci anyons using a superconducting quantum processor.

🧪 METHODOLOGY: 

• A superconducting quantum processor with 27 transmon qubits arranged in a honeycomb lattice was used for the experiementation.

• A Fibonacci string-net model was implemented by preparing the ground state and manipulating Fibonacci anyons through digital quantum simulation techniques: the quantum processor was optimized to achieve high qubit coherence times and gate fidelities, variational unitary synthesis was used for quantum circuit design to prepare the non-Abelian ground state, and the ground state of the string-net model was prepared by pushing the system into the desired state through quantum gate sequence.

• The topological nature of the states was confirmed by measuring topological entanglement entropy and verifying the fusion and braiding statistics of Fibonacci anyons using unitary operations on the qubits.

📊 OUTCOMES & OUTLOOK: 

• The topological entanglement entropy of the ground states was consistent with Fibonacci topological order, verifying this aspect.

• The researchers successfully created, braided, and fused pairs of Fibonacci anyons. The measured braiding statistics matched the theoretical predictions, providing evidence for the non-Abelian properties of the anyons and their potential for universal quantum computation.

• The quantum dimension of the Fibonacci anyons was experimentally determined to be very close to the theoretical value, further validating the creation of true Fibonacci anyons.

• These results demonstrate that current noisy devices can simulate non-Abelian topological states and perform operations with Fibonacci anyons. With successful recreation of conditions and methods fine-tined for scalability, this has the potential to open up new avenues for fault tolerance through topological qubits.