The Week in Quantum Computing - January 20th - USA order and nominations, QBird, IonQ and Maryland, Open Source Quantum
Issue #218
Quick Recap
This week the US takes the center of the stage. The State of Maryland launches a $1 billion initiative with the support of IonQ. Biden proposes a Cybersecurity order with very strict timelines to be PQC prepared and Trump taps into IBM’s Dario Gil. IonQ also clases a meaty contract with the AFRL for quantum networking. Quantum Brilliance has secured a $20M Series A round. Microsoft launched their “Quantum Ready Program” to get more companies into the field. A great piece on the Zapata AI's abrupt closure, that despite securing key partnerships and reporting $2 million in Q2 revenue, underscores the volatility in transitioning tech companies. All while speculation around Honeywell's potential Quantinuum IPO, valued at $10 billion. And a beautiful one. Would you like an open source quantum device? You may have one (in a few years). Open Quantum Design has been announced with four partners: Xanadu, University of Waterloo, Unitary Foundation, and Haiqu.
What does the cybersecurity executive order entail? That from release, federal contractors would have to:
Within 180 days: CISA to list and update PQC-supported product categories.
Within 90 days of category listing: Agencies to include PQC requirements in solicitations.
As soon as practicable: Agencies to implement PQC or hybrid key algorithms.
Within 90 days: NIST and International Trade to engage globally on NIST PQC standards.
Within 180 days: Defense and OMB to require TLS 1.3 or successor adoption by January 2, 2030.
Diego Emilio has written one of the best summaries on the state of the art in QML. Not only the papers he links are must ready but the rich conversation is key for anyone who wants to merge ML and Quantum.
At the same time the recent paper Comprehensive Survey of QML: From Data Analysis to Algorithmic Advancements provides a fantastic entry point for a lot of the work done. Hope you have a lot of free time this week, but if not here’s an AI generated summary:
1. Introduction and Context
The document begins by highlighting the limitations of classical computing, particularly the slowing pace of Moore's Law, and introduces quantum computing (QC) as a potential solution. QC leverages quantum phenomena like superposition and entanglement to perform computations beyond the capabilities of classical computers. The development of Shor's algorithm in the 2090s, which demonstrated the exponential speedup of factoring large numbers on a quantum computer, marked a significant breakthrough in the field.
2. Fundamentals of Quantum Computing
The source delves into the core concepts of QC, including:
Qubits: Unlike classical bits, qubits can exist in superposition, enabling them to represent multiple states simultaneously. This property grants quantum computers significant computational advantages.
Quantum Gates: These are fundamental operations applied to qubits, enabling manipulations and transformations crucial for quantum algorithms. The Hadamard, CNOT, and Pauli gates are examples of essential quantum gates.
Quantum Circuits: These are sequences of quantum gates applied to qubits, forming the building blocks of quantum algorithms.
Quantum Correlations: Entanglement is a crucial quantum phenomenon where qubits become linked, sharing a correlated state. This interconnectedness is vital for quantum algorithms and data processing.
Quantum Noise and Error Mitigation: Quantum systems are highly susceptible to noise from environmental interference, posing a significant challenge. Current quantum computers operate in the Noisy Intermediate-Scale Quantum (NISQ) era, characterized by limited qubit coherence and vulnerability to errors.
3. Quantum Machine Learning
The survey then focuses on QML, exploring its potential to revolutionize various fields, including healthcare, finance, and scientific research. It categorizes ML into supervised, unsupervised, and reinforcement learning, and discusses the recent trends in QML:
Hybrid Quantum-Classical Algorithms: This approach integrates quantum and classical techniques, leveraging the strengths of both. Quantum algorithms can accelerate specific computations within classical ML workflows, improving performance.
Quantum Data Analysis (QDA): QDA encompasses encoding classical data into quantum states for efficient processing and analysis, enabling potential speedups for tasks like feature selection and data dimensionality reduction.
Variational Quantum Algorithms (VQAs): These algorithms, prominent in hybrid QML, are specifically designed for optimization and simulation tasks on NISQ devices. They iteratively optimize parameters on classical computers while utilizing quantum processors for specific computations.
4. Survey of QML Algorithms
The document provides a detailed analysis of various QML algorithms, including their strengths, weaknesses, and potential applications:
Quantum Logistic Regression: This algorithm utilizes quantum circuits to process logistic regression functions efficiently, offering potential advantages in classification tasks.
Quantum Decision Trees: These are quantum counterparts of classical decision trees, leveraging quantum properties to enhance decision-making processes, especially in scenarios with high-dimensional data or uncertainty.
Quantum Support Vector Machines (QSVMs): QSVMs leverage quantum kernels to classify data in high-dimensional spaces more efficiently than their classical counterparts, demonstrating potential in fields like medical imaging and fraud detection.
Quantum K-Nearest Neighbour (QKNN): QKNN employs quantum techniques to compute distances between data points efficiently, offering potential advantages in classification tasks for image processing, medical diagnosis, and NLP.
Quantum Naive Bayes: This algorithm utilizes quantum circuits to represent Bayesian networks and perform probabilistic calculations, offering potential speedups in classification tasks.
Quantum Neural Networks (QNNs): QNNs encode data into quantum states, enabling parallel processing and potentially exponential speedups for tasks like pattern recognition and data analysis.
Quantum Convolutional Neural Networks (QCNNs): QCNNs, a specialized type of QNN, utilize quantum circuits inspired by convolutional operations, demonstrating advantages in image recognition and classification.
Quantum Recurrent Neural Networks (QRNNs): These networks leverage quantum principles to enhance the capabilities of classical RNNs, offering potential benefits in processing sequential data for tasks like speech recognition and time-series forecasting.
Quantum Clustering: This category encompasses algorithms like quantum k-means and hierarchical clustering, which utilize quantum properties to optimize cluster assignments and analyze data relationships more efficiently.
Quantum Generative Adversarial Networks (QGANs): These networks utilize quantum circuits to represent the generator and discriminator components, demonstrating potential for generating complex data distributions and simulating quantum systems.
5. Quantum Computing Frameworks
The document highlights several quantum computing frameworks and platforms crucial for QML research and development:
TensorFlow Quantum (TFQ): This framework integrates quantum computing with TensorFlow, providing tools for building and training hybrid quantum-classical ML models.
PennyLane: PennyLane is a cross-platform Python library that facilitates the development and optimization of quantum algorithms for various applications, including ML.
Qiskit: Developed by IBM, Qiskit is an open-source framework for QC, providing tools for building quantum circuits, simulating quantum systems, and running experiments on real quantum devices.
Amazon Braket: This service offers access to various quantum hardware platforms and simulators, facilitating the development and testing of quantum algorithms for various applications, including ML.
Microsoft Azure Quantum: Azure Quantum provides a cloud-based platform for accessing diverse quantum computing resources and services, supporting research, development, and exploration of quantum applications.
D-Wave: This company specializes in quantum annealing technology, which is particularly suited for solving optimization problems efficiently.
Google Cirq: Cirq is an open-source framework for QC, focusing on building and running quantum circuits on NISQ devices. It provides tools for designing, simulating, and executing quantum algorithms.
6. Applications of QML
The survey explores several promising applications of QML across diverse fields:
Finance: QML algorithms offer potential advantages in tasks like portfolio optimization, risk management, and derivative pricing.
Natural Language Processing (NLP): QNLP leverages quantum principles to enhance language understanding and processing, enabling improved sentiment analysis, text classification, and machine translation.
Optimization for Supply Chain and Logistics Improvement: QML contributes to efficient route optimization, resource allocation, and inventory management, leading to significant cost reductions and improved performance.
Drug Discovery and Materials Science: Quantum simulations and ML techniques accelerate the discovery of new drugs and materials by efficiently predicting their properties and behaviours.
Environmental Science and Sustainability: QML techniques analyze climate data, optimize resource management, and model environmental systems to address climate change and sustainability challenges.
Cybersecurity: QC presents both opportunities and threats to cybersecurity. While QKD offers enhanced encryption methods, the potential for breaking existing cryptographic techniques necessitates the development of PQC.
Robotics: Quantum algorithms are applied to enhance robot control, navigation, and coordination, particularly in swarm robotics, where quantum planning optimizes the collective behaviour of multiple robots.
Time Series Analysis: Quantum algorithms like QLSTMs and quantum dynamic mode decomposition offer potential advantages in forecasting, anomaly detection, and understanding complex temporal patterns.
7. Challenges and Research Horizons
The document acknowledges the challenges hindering the widespread adoption of QML:
Quantum Noise and Error Correction: The sensitivity of quantum systems to noise necessitates robust error correction mechanisms. Advances in fault-tolerant QC and hybrid error mitigation strategies are crucial for reliable QML operations.
Scalability: Scaling quantum systems to handle large datasets and complex computations remains a bottleneck. Developments in modular quantum architectures and quantum-inspired algorithms are crucial for addressing scalability issues.
Algorithm Development and Optimization: Designing efficient quantum algorithms tailored for specific ML tasks and developing effective optimization techniques for training quantum models are crucial for realizing QML's full potential.
Data Encoding and Representation: Efficiently encoding classical data into quantum states is a critical challenge. Research focuses on exploring and improving data encoding techniques for specific QML applications.
Hybrid Quantum-Classical Integration: Balancing the strengths of quantum and classical computing is crucial for achieving practical benefits in the NISQ era. Research focuses on developing hybrid algorithms and frameworks that effectively integrate both paradigms.
8. Conclusion
QML holds immense promise for revolutionizing various fields. While current quantum hardware limitations and algorithmic challenges persist, ongoing research and development efforts are paving the way for the future of QML. As quantum technology matures, QML will become an indispensable tool for addressing complex problems across diverse industries and research domains.
9. Key Quotes
"In a classical system, adding a bit doubles the information capacity, but in a quantum system, adding a qubit scales capacity exponentially."
"VQAs are one of the most significant developments in hybrid QML, particularly in optimization and simulation tasks."
"A key challenge in QML is the effective encoding of classical data into quantum systems."
"QC is set to revolutionize the field of cybersecurity by addressing existing cryptographic challenges and introducing new, more secure encryption methods."
"QML holds immense promise for revolutionizing various fields."
This briefing document provides a comprehensive overview of the key themes and findings from the provided source on QML. It highlights the fundamentals of QC, delves into various QML algorithms, explores quantum computing frameworks, discusses promising applications, and acknowledges the challenges and research horizons in the field.
The Week in Quantum Computing
Reflections on the Collapse of Zapata AI
In 2024, Zapata AI, initially a quantum computing firm, pivoted to AI and secured partnerships with USSOCOM and Mag Aerospace, reporting $2 million in Q2 revenue. Despite this, the company ceased operations shortly after, surprising many, including a six-year employee. Financial fragility, with liabilities exceeding assets by $27 million, and market dynamics led to a rapid stock decline, dropping 45% in September. Key events included a second Lincoln Park purchase agreement and a critical video by Sabine Hossenfelder, though the latter's impact is debated. Zapata's collapse underscores the volatile nature of transitioning tech companies and the challenges of maintaining investor confidence amidst financial instability.
Market Speculation for Quantum Engineers
In the volatile landscape of quantum computing in 2024, market speculation is rife, as illustrated by the recent buzz around Honeywell's potential Quantinuum IPO, valued at $10 billion, and its impact on IonQ's market cap, which stood at nearly $2 billion. This highlights the speculative nature of small-cap stocks in emerging tech sectors. Daniel Kahneman's Nobel-winning insights on decision-making under uncertainty underscore the psychological factors influencing market movements. The lack of impartial quantum computing coverage exacerbates confusion and hype, risking the sector's relegation to "Meme Stock" status. As sentiment and fundamentals both play crucial roles, the industry must strive for rigorous, transparent reporting to foster informed investment decisions.
Paper: Myths around quantum computation before full fault tolerance: What no-go theorems rule out and what they don't
In a recent paper, Zoltán Zimborás and 20 co-authors critically examine the current state and future potential of quantum computing, particularly before achieving full fault tolerance. The study addresses misconceptions about quantum error mitigation and variational quantum algorithms, emphasizing the importance of understanding error scaling and circuit depth. The authors highlight viable near-term applications and the potential synergy between error mitigation and early fault-tolerant architectures. They also discuss strategies to overcome challenges like barren plateaus in variational circuits. The paper underscores the necessity for continued innovation in both hardware and algorithmic design to bridge the gap between theoretical potential and practical utility, aiming for meaningful quantum advantage in the era of late noisy intermediate scale and early fault-tolerant quantum devices.
https://arxiv.org/abs/2501.05694v1
Mark Zuckerberg Backs Nvidia CEO Jensen Huang's View On Quantum Computing: 'It's Still Quite A Ways Off'
In a recent discussion on Joe Rogan's podcast, Meta CEO Mark Zuckerberg aligned with Nvidia CEO Jensen Huang's cautious stance on quantum computing, suggesting it's still a decade or more away from practical use. Huang's comments at CES 2025, predicting a 15 to 30-year timeline for functional quantum computers, led to significant market volatility, with IonQ, Quantum Computing Inc., and Rigetti Computing stocks plummeting by 39%, 43.34%, and 45.41%, respectively.
IonQ has secured a $21.1 million contract with the United States Air Force Research Lab
IonQ has secured a $21.1 million contract with the United States Air Force Research Lab (AFRL) to advance secure quantum networking. This collaboration aims to explore the potential of quantum networks in enhancing secure communications, a critical area as quantum computing continues to evolve. IonQ's CEO, Peter Chapman, emphasized the significance of this partnership, stating it will "push the boundaries of what is possible in quantum networking."
2025: The year to become Quantum-Ready
Microsoft, in collaboration with Atom Computing, successfully entangled 24 logical qubits in November 2024. This marks a pivotal moment as businesses globally are urged to become "quantum-ready." Mitra Azizirad, President and COO at Microsoft, emphasizes the urgency for business leaders to comprehend quantum advancements and their potential applications. Despite the progress, only 12% of organizations feel prepared to assess quantum opportunities. To address this, Microsoft launched the Quantum Ready program, aiming to equip leaders with the necessary tools and insights for building impactful hybrid applications and ensuring cryptographic agility. As the United Nations declares 2025 the International Year of Quantum Science and Technology, the race to harness quantum's transformative power intensifies.
https://azure.microsoft.com/en-us/blog/quantum/2025/01/14/2025-the-year-to-become-quantum-ready/
New methods to enhance the fidelity of superconducting qubits
In a significant development for quantum computing, researchers from the University of Chicago have unveiled new methods to enhance the fidelity of superconducting qubits. The team, led by Professor David Schuster, achieved error rates below 0.1%, a milestone that could accelerate the path to practical quantum computing. This breakthrough is crucial as error rates have been a major bottleneck in quantum computing, limiting the scalability and reliability of quantum systems.
https://phys.org/news/2025-01-fast-methods-enable-fidelity-superconducting.html
Quantum Computing in Telecom Networks
In 2024, Ericsson explores quantum computing's potential to revolutionize telecom networks, focusing on optimization and security. The report highlights quantum algorithms' ability to enhance network efficiency and solve complex problems faster than classical methods. Ericsson's collaboration with leading quantum institutions aims to integrate quantum technologies into existing telecom infrastructures. "Quantum computing can redefine network capabilities," states Erik Ekudden, Ericsson's CTO. The company emphasizes the importance of quantum-safe encryption to protect data against future quantum threats. However, practical implementation remains distant due to current technological limitations and the nascent state of quantum hardware.
Collaboration tests new method for protecting quantum networks
In a significant development for quantum computing, researchers from the University of Vienna and the Austrian Academy of Sciences have introduced a novel method to enhance quantum networks. This method, detailed in a recent study, leverages entanglement swapping to improve the efficiency and reliability of quantum communication. The team, led by physicist Rupert Ursin, demonstrated that their approach could potentially overcome current limitations in quantum network scalability. Ursin stated, "Our method paves the way for more robust quantum networks, crucial for future quantum internet applications." This advancement is pivotal as it addresses one of the key challenges in quantum computing: establishing long-distance, secure quantum communications.
https://phys.org/news/2025-01-collaboration-method-quantum-networks.amp
State of Maryland, University of Maryland Announce $1 Billion “Capital of Quantum” Initiative
The State of Maryland and the University of Maryland have launched a $1 billion initiative to establish the region as the "Capital of Quantum." This ambitious project aims to bolster quantum research, education, and industry partnerships. University of Maryland President Darryll J. Pines emphasized the state's commitment to becoming a global leader in quantum technology. The initiative will fund infrastructure, talent acquisition, and collaborative projects, positioning Maryland as a hub for quantum innovation. This move is crucial as quantum computing continues to evolve, with significant implications for cybersecurity, pharmaceuticals, and materials science. As Pines stated, "This investment will catalyze breakthroughs that will define the future." Maryland's strategic investment underscores the growing importance of quantum technology in shaping tomorrow's technological landscape.
Quantum Brilliance Raises USD $20 Million in Series A Funding Round
Quantum Brilliance (QB), an Australian leader in diamond-based quantum technology, secured $20 million in Series A funding to advance its room-temperature quantum devices. Key investors include Main Sequence, In-Q-Tel, and Intervalley Ventures. This funding will support QB's creation of a quantum diamond foundry and the development of prototypes for quantum sensing. Nat Puffer from IQT emphasized the strategic importance of diamond quantum technology, while Bill Bartee from Main Sequence highlighted QB's potential for groundbreaking products. QB's technology, known for its compactness and room-temperature operation, is poised for large-scale deployment, as evidenced by partnerships with Oak Ridge National Laboratory and a contract with Germany’s cybersecurity agency for a mobile quantum computer.
MIT sets world record with 99.998% fidelity in quantum computing breakthrough
In a significant stride for quantum computing, MIT researchers have achieved a world-record single-qubit fidelity of 99.998% using fluxonium qubits. This breakthrough, led by David Rower and Leon Ding, was accomplished through innovative control techniques: 'commensurate pulses' and 'circularly polarized microwaves.' These methods effectively mitigate counter-rotating errors, enhancing qubit performance. William D. Oliver, a professor at MIT, emphasized the importance of this achievement, stating it "makes a beautiful connection with counter-rotating dynamics." This advancement not only supports fundamental physics but also boosts engineering performance, crucial for fault-tolerant quantum computing.
https://interestingengineering.com/science/99-998-fidelity-in-quantum-computing-by-mit
Bursting Bubbles
In recent discussions, comments by Jensen Huang and Mark Zuckerberg have significantly impacted quantum computing stocks like Rigetti Computing, D-Wave, and IonQ, which saw a drop of over 50% from their all-time highs. Despite this, these stocks remain higher than a year ago. The core issue is whether these stocks are overvalued, given their reliance on future breakthroughs rather than current revenues. IonQ's $12M revenue starkly contrasts with the massive scales of NVIDIA and Meta, highlighting the nascent stage of quantum computing. Matthias Kaiser suggests that while widespread adoption may be decades away, niche applications could emerge within two years, potentially driving significant market growth. As Kaiser aptly notes, "Quantum computing is not a sprint, it's a marathon!"
https://www.linkedin.com/pulse/bursting-bubbles-matthias-kaiser-zaiwe
OQD signs four quantum heavy hitters as founding partners
Open Quantum Design (OQD) has announced the signing of four prominent partners: Xanadu, University of Waterloo, Unitary Foundation, and Haiqu, to join its open-source quantum computing community. This collaboration aims to leverage the expertise of these "quantum heavy hitters" to advance the development and accessibility of quantum technologies. Xanadu is known for its work in photonic quantum computing, while the University of Waterloo is a leading academic institution in quantum research. The Unitary Foundation supports open-source quantum software, and Haiqu focuses on quantum hardware innovations. This partnership underscores a growing trend towards open collaboration in the quantum sector, potentially accelerating breakthroughs and democratizing access to quantum computing resources in 2024.
Executive Order on Strengthening and Promoting Innovation in the Nation’s Cybersecurity
In January 2025, President Joe Biden issued an Executive Order to bolster the United States' cybersecurity, emphasizing the persistent cyber threats from adversarial nations, particularly China. This directive builds on Executive Order 14028 from May 2021, focusing on enhancing digital infrastructure security and promoting innovative cybersecurity technologies. The order sets 53 deadlines over three years, emphasizing encryption, contractor security, and CISA's threat-hunting capabilities. It highlights the need for post-quantum cryptography (PQC) products, with CISA tasked to list available categories. A significant aspect involves improving third-party software supply chain transparency. The order mandates the Office of Management and Budget (OMB), in collaboration with the National Institute of Standards and Technology (NIST) and the Cybersecurity and Infrastructure Security Agency (CISA), to recommend contract language for software providers to submit secure software attestations.
The gist of it. From publication:
Within 180 days: CISA to list and update PQC-supported product categories.
Within 90 days of category listing: Agencies to include PQC requirements in solicitations.
As soon as practicable: Agencies to implement PQC or hybrid key algorithms.
Within 90 days: NIST and International Trade to engage globally on NIST PQC standards.
Within 180 days: Defense and OMB to require TLS 1.3 or successor adoption by January 2, 2030.
Q*Bird leads the shift towards truly scalable quantum networks with new multi-user simultaneous link
In January 2025, Q*Bird, a quantum communications company, announced a breakthrough in scalable quantum networks with its Falqon Quantum Key Distribution (QKD) architecture. This technology enables multipoint-to-multipoint communication, allowing networks to expand to thousands of users without extensive redesigns. The Falqon MQS4000 Quantum Switch, successfully tested in the Dutch national network by SURF, offers enhanced security against current and future quantum threats. Ingrid Romijn, CEO of Q*Bird, highlighted the commercial novelty of this development, stating it as a crucial component for scalable quantum networks. With €2.5 million raised in May 2024, Q*Bird has grown significantly, poised to impact the quantum security market.
How quantum computing is disrupting power utilities
E.ON Digital Technology, led by Dr. Giorgio Cortiana, is pioneering the exploration of quantum computing's potential to revolutionize power utilities. In 2024, E.ON is investigating quantum algorithms to optimize energy distribution and grid management, aiming to enhance efficiency and sustainability. Dr. Cortiana emphasizes, "Quantum computing could redefine how we manage energy resources."
Trump taps IBM’s Dario Gil for Energy’s undersecretary for science and innovation
In a significant move for the quantum computing sector, President-elect Donald Trump has nominated Darío Gil, IBM's Director of Research, as the undersecretary for science and innovation at the Department of Energy. Gil, who has been instrumental in advancing quantum information sciences and AI, previously served on Trump's Presidential Council of Advisors on Science and Technology. His nomination is seen as a strategic effort to bolster U.S. investment in emerging technologies. IBM CEO Arvind Krishna expressed support, highlighting Gil's role as a "champion of science and technology."
QCI shares fall amid fraud alegations from shorting interest
Shares of Quantum Computing Inc (NASDAQ: QUBT) plummeted 7% following allegations by Capybara Research of fraudulent activities. The report accuses the company of issuing misleading press releases to inflate stock prices, citing interviews with former employees. Allegations include fabricated sales deals and undisclosed connections with companies like Millionways Inc and Quad M, both tied to Quantum Computing Inc's founders. A $500k payment to Millionways via an unsecured note is unlikely to be repaid, and a memorandum with Quad M failed to generate revenue. A former executive stated, "There's been no progress. They haven’t even entered the game." The company is under legal scrutiny for announcing fictitious sales deals and engaging in deceptive practices with related parties like Millionways Inc. and Quad M. Additionally, QUBT's claims about establishing a foundry were debunked, revealing only a small R&D lab. These claims have severely impacted investor confidence, reflecting in the stock's decline. Quantum Computing Inc has yet to respond to these allegations, leaving its credibility in question amidst the evolving quantum computing landscape.
The report
https://www.scribd.com/document/816530429/QUBT-capybara-research
A year of simplifying quantum software development tools with Qiskit
In 2024, IBM focused on enhancing quantum software development through Qiskit, introducing Qiskit SDK v1.0 and the Qiskit Functions Catalog. This effort aimed to make quantum computing more accessible, even for those without extensive expertise. The Qiskit SDK v1.0 introduced a stable API with Semantic Versioning, consolidated features, and V2 primitives for improved interaction with quantum hardware. Additionally, Qiskit addons, such as the multiproduct formulas and operator backpropagation, were released to facilitate quantum algorithm discovery. Blake Johnson and colleagues emphasized the importance of these tools in lowering the barrier to entry for quantum computing.
https://www.ibm.com/quantum/blog/dev-tools
Comprehensive Survey of QML: From Data Analysis to Algorithmic Advancements
A recent survey by Sahil Tomar, Rajeshwar Tripathi, and Sandeep Kumar, published on arXiv, delves into the advancements and challenges of Quantum Machine Learning (QML) as of 2024. The paper explores key techniques like Quantum Support Vector Machines and Quantum Neural Networks, highlighting their potential to outperform classical methods in processing complex datasets. However, the authors note significant hurdles, including hardware limitations and noise in Noisy Intermediate-Scale Quantum (NISQ) devices. They discuss emerging solutions such as error mitigation and hybrid frameworks as vital for developing scalable QML systems.