The Week in Quantum Computing - November 18th - U.S. Faces Pressure from China's Quantum Advances; Quanscient, IQM Roadmap, RSA out by 2030. AI vs Quantum
Issue #210
Quick Recap
Another impressive week with funding, great research papers, new types of qubits and some sarcasm. The new Trump’s U.S. faces pressure to maintain its lead in quantum technology amidst China's rapid advancements, particularly in quantum communications. The competition between quantum computing and AI intensifies, with AI encroaching on areas traditionally dominated by quantum, raising questions about the long-term viability of quantum breakthroughs. In the investment landscape, Quanscient's €5.2 million funding in Finland and Quolab’s $3.5 million in Japan show how the money still flows. Research and innovation continue to push boundaries, as seen in Uniwersytet Warszawski's record-breaking quantum memory speed and the European Union's €142 million investment in Dutch photonic chip plants. The Post-Quantum Cryptography market is projected to grow significantly, driven by the need for quantum-resistant solutions. Not in vain NIST said to deprecate RSA by 2030! Additionally, University of Sydney researchers have introduced a novel quantum error correction architecture, promising enhanced reliability and reduced resource demands. IQM has released their roadmap promising logical qubits by 2030.
Cryptography Standards
NIST has released the initial public draft of IR 8547, detailing the strategy for transitioning from quantum-vulnerable cryptographic algorithms to post-quantum cryptography. Effectively, think about deprecating RSA by 2030. Get your PQC solutions ready now!
This week, a great paper on the intersection of AI and Quantum has been published: https://arxiv.org/abs/2411.09131v1
But since the paper is long, I wanted to summarize it for you:
Artificial Intelligence for Quantum Computing
As we know, Quantum computing has the potential to impact every domain of science and industry. However, QC faces a number of challenges in transitioning from NISQ devices to FTQC. These challenges include hardware noise, the need for more resourceful quantum error correction codes, faster decoder algorithms, and carefully designed qubit architectures. Sometimes, co-designed with the software.
High performance computing, and in particular, accelerated GPU computing, already drives QC research through circuit and hardware simulations. The rise of generative AI paradigms has only just begun. Foundational AI models, characterized by their broad training data and ability to adapt to a wide array of applications, are emerging as an extremely effective way to leverage accelerated computing for QC. Transformer models have proven particularly powerful.
There is ample intuition to motivate exploring AI as a breakthrough tool for QC. The inherent nonlinear complexity of quantum mechanical systems makes them well-suited to the high-dimensional pattern recognition capabilities and inherent scalability of existing and emerging AI techniques. This paper examines applications of state-of-the-art AI techniques that are advancing QC with the goal of fostering greater synergy between the two fields.
AI for Quantum Computer Development and Design
Fundamental improvements to quantum hardware currently requires precise, costly and extremely difficult experimentation. From design to fabrication, characterization and control, AI can accelerate the workflows surrounding the quantum device development cycle - providing insights into the complexities of quantum systems and reducing the timeline for developing quantum computers.
System Characterization: AI methods can accelerate fundamental research into designing and improving quantum hardware. Hamiltonian Learning seeks to identify the generating Hamiltonian of observed quantum dynamics through the use of ML methods. ML methods have been used to learn quantum device characteristics otherwise inaccessible to experiments, such as disorder potentials and the nuclear environment of a qubit.
Platform Design: AI approaches have been employed to successfully design multi-qubit operations in superconducting quantum devices. AI can also be employed to design quantum optical setups that can then be employed to generate highly entangled states. AI models learning how to optimize the performance of multi-qubit gates in nonuniform semiconductor-based qubits can automate the handling of manufacturing variabilities in these devices.
Gate and Pulse Optimization: Deep learning methods, particularly reinforcement learning (RL) techniques, have proven especially fruitful across qubit modalities for pulse optimization. RL has been used to optimize pulse sequences for qubits on specific quantum hardware, such as superconducting transmon qubits, charge qubits, and quantum dots.
AI for Preprocessing
Preparing quantum algorithms to run on a quantum device is a significant challenge. Practical implementation of algorithms requires generating compact circuits that run as fast and efficiently as possible, whilst accounting for device-specific constraints. Recent advancements in AI methods have opened new possibilities for more efficient and flexible quantum circuit design.
Quantum Circuit Synthesis: Circuit synthesis is the orchestration of potentially hardware-specific operations to efficiently realize some desired quantum circuit. Unitary synthesis is a particularly important circuit synthesis task that prepares a quantum circuit to implement a specific unitary operation. Deep learning techniques can automate navigating the vast space of potential gate sequences during the decomposition process. Diffusion models have also recently been applied to generate valid circuits for arbitrary unitary operations. Both generative and RL AI models have demonstrated promise in generating more compact circuits.
Circuit Parameter Learning and Parameter Transfer: Parameter transfer is the process of using optimal circuit parameters from other use cases to accelerate the generation of optimal parameters in a new, distinct use case. Graph embedding techniques have been used to facilitate such transferability, by identifying structural similarities between graphs representing different problem instances.
State Preparation: The preparation of particular quantum states generally requires circuits having a depth that grows exponentially with problem size. AI-based approaches to state preparation are broad, accommodating the many specialized heuristics, optimizations and initializations that can apply to the wide range of possible state preparation problems. RL has been used for state preparation on both ideal and experimental systems, and has been used to optimize experimental figures of merit such as fidelity, gate cost and runtime.
AI for Device Control and Optimization
All approaches to building and operating quantum processors involve control, tuning and optimization of quantum devices. Control refers to actively modify-ing quantum states through inputs (e.g. microwave pulses) to perform desired operations. Tuning involves adjusting device parameters to target a specific operating regime, and optimization involves refining such parameters to max-imize performance metrics like coherence times, operation speeds, and fidelity.
The characterization, tuning, control and optimization of quantum devices are time-consuming processes. The use of ML approaches for automating these processes is well motivated, since NNs and Bayesian optimization methods excel at inferring appropriate outputs from limited input data without employing costly modeling from first principles. A variety of ML methods have been used to characterize different types of quantum devices, automate tuning strategies, and optimize qubit control.
AI for Quantum Error Correction
Decoding: QEC protocols involve making joint measurements on sets of qubits (syndrome qubits) and using these results to infer which physical qubits (data qubits) have most likely experienced errors. The inference step of this process is performed by a classical decoding algorithm. Decoders face serious scalability challenges. A diverse array of AI techniques are being explored as tools to improve the efficiency, accuracy, and scalability of QEC decoding algorithms. These AI techniques include Boltzmann machines, CNNs, LSTM RNNs, transformer models, and GNNs.
Code Discovery: The discovery of new QEC codes is crucial for advancing FTQC. AI offers a promising alternative by automating the search for new QEC codes, leveraging its ability to identify patterns and optimize structures within high-dimensional spaces. RL has been used to discover new QEC codes.
AI for Postprocessing
Quantum applications commonly require a post-processing stage to extract meaningful results from quantum measurements and optimize the measure-ment process.
Efficient Observable Estimation and Tomography: Estimating quantum observables is a key part of quantum computations. ML and AI techniques have proven useful for reducing the quantity of data points needed to estimate a given observable. Quantum state tomography (QST) has emerged as a more feasible solution to full state tomography (FST) by focusing on alternative approaches that address the exponential scaling cost of FST. NN-based approaches have been used for state reconstruction in QST.
Readout Measurements: AI can significantly enhance the processing of qubit readout by improving measurement accuracy and minimizing the effects of noise and errors. For example, in neutral atom quantum computing systems, AI has been used to improve qubit state detection accuracy.
Error Mitigation Techniques: Quantum error mitigation (QEM) is a set of techniques that attempt to deal with noise in quantum systems without resorting to the full machinery of FTQC. A large range of QEM techniques have been developed. Many of these techniques contain hyperparameters which are usually obtained via device calibration or optimization. The direct application of NNs to QEM has also been explored.
Outlook
The research surveyed in this paper demonstrates how AI has the potential to enable breakthroughs in virtually all aspects of the development and operation of quantum computers. The exploration of how AI can be of utility for quantum computing has only just begun.
Accelerated Quantum Supercomputing Systems: Increasingly sophisticated AI tech-niques require greater processing power to train, and classical computing capabilities will need to scale alongside developments in quantum hardware. The integration of quantum processors within AI supercomputers is widely accepted to be a necessary architecture for building large-scale, useful quantum computers.
Simulating High Quality Data Sets: Many applications of AI models for quantum computing must be trained on large, high quality data sets. This shortfall can be addressed by synthetic training data, obtained through simulation. AI can also be used to simulate quantum systems directly and has driven substantial scientific progress not only in the field of QC, but in condensed matter, quantum chemistry, and similar fields.
Increased Multidisciplinary Collaboration: Another key gating factor is collaboration between AI and QC experts. Diffusion models, advanced RL techniques and generative flow networks all hold great potential for future exploration. Perhaps one of the more ambitious uses of AI for quantum is the design of new quantum algorithms. One future strategy is the use of AI to generate novel quantum algorithms. It may also be the case that incorporating AI into the algorithm design process could lead to the generation of more fundamentally hybrid quan-tum algorithms.
The Week in Quantum Computing
The Trump Administration Must Make Quantum Technology a Priority in the First 100 Days
In 2024, the United Nations declared 2025 as the International Year of Quantum Science and Technology, emphasizing its potential to address critical social challenges. The U.S. is at risk of losing its quantum technology lead to China, which excels in quantum communications and rapidly advances in other areas. Sam Howell argues that President Donald Trump must prioritize quantum technology in his first 100 days to maintain U.S. competitiveness. Quantum technology's interdisciplinary nature promises transformative advancements across industries, with first-mover advantages in encryption, surveillance, and more. The U.S. leads in quantum computing and sensing but faces challenges from China's substantial public R&D funding. Howell warns against repeating past mistakes, such as the U.S. lag in 5G technology.
https://www.justsecurity.org/104566/next-us-president-must-make-quantum-tech-priority/
The Download: AI vs quantum, and the future of reproductive rights in the US
In 2024, the quantum computing sector faces a formidable challenge from AI, which is encroaching on areas traditionally seen as quantum's domain, such as fundamental physics and materials science. Despite significant investments in quantum technology, AI's rapid advancements suggest it might outperform quantum computing in certain applications. Edd Gent notes, "AI could eat quantum computing’s lunch," highlighting the growing competition between these technologies. This development is crucial as it questions the long-term viability of quantum computing's promised breakthroughs. As the tech landscape evolves, the industry must reassess the roles and potential of both AI and quantum computing in shaping future innovations.
Quanscient secures €5.4M
Quanscient, a Finnish company, has secured €5.2 million in funding to advance engineering simulations into the quantum era. Their platform, integrating cloud-native multiphysics solvers and quantum algorithms, promises a hundredfold increase in simulation throughput, crucial for industries like fusion energy and aerospace. CEO Juha Riippi highlights the platform's ability to boost simulation capacity by 100 times, enabling faster iteration and tackling complex challenges. Quanscient has achieved a milestone by running a Computational Fluid Dynamics (CFD) simulation on quantum hardware, with plans to release a quantum product pilot in early 2025. This funding, led by Crowberry Capital, supports Quanscient's international expansion and positions them at the forefront of quantum simulation technology.
Beyond the lab - how interdisciplinary teams come together to build the quantum future
In 2024, Q-CTRL emphasizes the importance of interdisciplinary collaboration in advancing quantum technology. The company integrates diverse skills from product management, design, and engineering to transform quantum research into practical solutions. Seb Lecornu highlights the necessity of a "figure-it-out mindset" and adaptability in their teams. Liz Koswara-Simms, Senior Growth Product Manager, notes the application of skills like experimentation and product lifecycle management in quantum contexts. Ryan Dougherty, Software Engineer, underscores the role of cross-functional teamwork in developing innovative software products. Q-CTRL's approach fosters a dynamic work environment, encouraging continuous learning and creativity, crucial for tackling complex quantum challenges.
Qolab has received a $3.5 million investment from the state-owned Development Bank of Japan
In 2024, a quantum computing startup founded by Google alumni has secured a $3.5 million investment, signaling continued interest and confidence in the field. This funding round was led by prominent venture capital firms, underscoring the potential seen in the startup's approach to quantum technology. The startup aims to address current quantum computing challenges, such as error rates and scalability, which remain significant hurdles in the industry. The investment highlights the ongoing race to achieve practical quantum computing solutions. As noted by one of the investors, "Quantum computing is poised to revolutionize industries, but overcoming technical barriers is crucial."
Quantum Memory: A Tale of Three Patents- Part 2
In 2024, Uniwersytet Warszawski's Michał Parniak-Niedojadło has made significant strides in quantum memory, achieving a record-breaking speed three times faster than Chinese counterparts, as highlighted in a 2017 Nature Communications paper. This advancement allows for the transmission of quantum key qubits over 150 km, surpassing the previous 100 km benchmark. The Warsaw group's patent, granted in just 20 months by the EP office, underscores the importance of effective patent writing in the quantum technology ecosystem. Meanwhile, Chinese researchers have pushed the envelope further with quantum storage of 1650 modes of single photons, showcasing China's prowess in quantum communication and computing. The race in quantum memory innovation is intensifying, with Europe urged to broaden its investment horizons.
https://quantumcomputingreport.com/quantum-memory-a-tale-of-three-patents-part-2-the-art/
85% industry leaders call for major investments in quantum computing: Report
A recent report by Primus Partners reveals that 85% of industry leaders advocate for increased investments in quantum computing, highlighting its transformative potential in sectors like AI, cybersecurity, and healthcare. The survey, involving 200 senior executives, indicates that 79.4% believe quantum algorithms will redefine AI and machine learning, while 68.1% see it enhancing cybersecurity. Healthcare, particularly drug discovery, is seen as a key beneficiary by 61% of respondents. However, challenges such as high R&D costs (70.9%) and a lack of skilled talent (62.4%) hinder progress. Devroop Dhar, Co-founder of Primus Partners, emphasized the need for strategic investments and skill development, stating, "This technology will significantly enhance national security, drive economic growth, and create millions of jobs."
Post-Quantum Cryptography (PQC) Industry Research Report 202 : 029 : Integration of Innovative Cryptographic Algorithms, Hybrid Pqc Mechanisms, Driving Awareness Toward Quantum Computing Threat
The Post-Quantum Cryptography (PQC) market is projected to expand from $302.5 million in 2024 to $1.88 billion by 2029, at a CAGR of 44.2%, driven by the quantum computing threat to traditional encryption methods like RSA and ECC. The report by Research and Markets highlights the need for quantum-resistant solutions to comply with regulations, such as those from the US National Institute of Standards and Technology. Quantum-safe hardware, essential for implementing PQC algorithms, dominates the solutions segment. North America leads in PQC adoption due to significant government initiatives. The BFSI sector is a major adopter, driven by stringent regulations and the need to protect sensitive financial data. As quantum computing advances, the urgency for PQC solutions intensifies.
€142M for Dutch photonic chip plants
The European Union has announced a €142 million investment in Dutch photonic chip plants, a move that could significantly impact quantum computing. Photonic chips, which use light to process information, are crucial for advancing quantum technologies due to their potential for faster and more efficient data processing. This investment aligns with the EU's broader strategy to bolster its technological sovereignty and innovation capacity. "Europe must lead in the next wave of technological innovation," stated EU Commissioner Thierry Breton. The funding will support research and development, aiming to position Europe as a leader in quantum computing infrastructure. As quantum computing continues to evolve, such investments are pivotal in shaping the competitive landscape and technological advancements in 2024 and beyond.
https://www.reuters.com/world/europe/eu-invests-142-mln-dutch-photonic-chip-plants-2024-11-11/
Univ. of Sydney Quantum Researchers Report Advance on Error Correction
University of Sydney researchers Dominic Williamson and Nouédyn Baspin have unveiled a novel quantum error correction architecture, promising enhanced reliability and reduced resource demands for quantum computing. Their approach, published in Nature Communications, introduces a three-dimensional structure that corrects errors across two dimensions, improving upon existing methods that only manage errors in one dimension. This innovation could significantly decrease the number of physical qubits required, facilitating the development of a more compact "quantum hard drive." Professor Stephen Bartlett highlighted its potential to revolutionize quantum computer construction and operation. As quantum computing strives for scalability, this breakthrough represents a pivotal step towards practical and efficient quantum systems.
https://insidehpc.com/2024/11/univ-of-sydney-quantum-researchers-report-advance-on-error-correction/
SDT and Semiqon partnership
In a strategic move to bolster the quantum computing sector, Korean SDT and Finland's SemiQon have signed a Memorandum of Understanding (MOU) to integrate SemiQon's silicon-based quantum processors (QPUs) into SDT's quantum precision measurement equipment. This partnership aims to enhance scalability and stability, crucial for advancing towards general-purpose quantum computers on a million-qubit scale. SemiQon's QPUs, compatible with existing infrastructure, promise reduced production costs and facilitate mass production. Both companies are optimistic about the collaboration's potential to expedite the development of scalable, cost-effective quantum computers.
https://dig.watch/updates/sdt-and-semiqon-partner-to-advance-quantum-computing
How quantum computing could reshape financial services
At the Singapore FinTech Festival, experts discussed quantum computing's transformative potential in financial services, particularly in risk management, investment strategies, fraud detection, and customer analytics. Julian Tan from IBM emphasized quantum computing's unique ability to detect weak signals, likening it to adding colors to a television. Professor Ying Chen from NUS highlighted quantum algorithms' capability in multi-objective optimization, cautioning against overhyping quantum speed. Michael Low from SMU stressed the importance of "quantum literacy" and warned of security risks, advocating for quantum-resistant encryption. IBM's Tan noted Shor's algorithm's threat to current encryption, urging a shift to post-quantum cryptography.
https://www.computerweekly.com/news/366615437/How-quantum-computing-could-reshape-financial-services
A new optical quantum computer in Japan
In a groundbreaking development, RIKEN, the University of Tokyo, JST, NTT, and Fixstars Amplify have collaboratively developed a new type of quantum computer, marking a significant advancement in the field. Led by Akira Furusawa, this project introduces a general-purpose optical quantum computing platform, a first of its kind globally. The optical approach promises faster and larger-scale quantum computations compared to traditional methods, leveraging high-frequency optical signals and room-temperature operations. This innovation is accessible via a cloud system, initially through research contracts, with potential to boost Japan's quantum industry and international competitiveness. Furusawa, a pioneer in optical quantum computing, emphasizes the integration of cutting-edge optical technologies.
https://www.riken.jp/pr/news/2024/20241108_2/index.html
IQM Quantum Computers unveils development roadmap focused on fault-tolerant quantum computing by 2030
IQM Quantum Computers has announced a roadmap aiming for fault-tolerant quantum computing by 2030, targeting a scale-up to 1 million qubits. This ambitious plan involves merging its Star and Crystal processor topologies to enhance error correction, supported by an open modular software stack for HPC integration. Dr. Jan Goetz, Co-Founder and Co-CEO, highlights the use of Quantum low-density parity-check (QLDPC) codes to reduce hardware overhead significantly. IQM's strategy includes developing high-precision logical qubits with error rates below 10^-7, crucial for applications in chemistry and materials science. The roadmap underscores IQM's commitment to scalable, efficient quantum solutions, potentially unlocking a value of over $28 billion by 2035, according to McKinsey.
Transition to Post-Quantum Cryptography Standards
In November 2024, NIST released the initial public draft of IR 8547, detailing its strategy for transitioning from quantum-vulnerable cryptographic algorithms to post-quantum cryptography (PQC). Authored by Dustin Moody, Ray Perlner, Andrew Regenscheid, Angela Robinson, and David Cooper, the report outlines the necessary shift to quantum-resistant standards for digital signatures and key-establishment schemes. This transition is crucial for federal agencies, industry, and standards organizations to safeguard IT products and services against quantum threats. The draft invites public comments until January 10, 2025, to refine the transition plan.
https://csrc.nist.gov/pubs/ir/8547/ipd
planqc to build 1,000-Qubit Neutral-Atom Quantum Computer for LRZ
Planqc has been awarded a €20 million project to construct a 1,000-qubit neutral-atom quantum computer at the Leibniz Supercomputing Centre (LRZ) in Germany. This initiative, funded by the German Federal Ministry of Education and Research, aims to integrate the quantum system into LRZ's high-performance computing infrastructure, enhancing scientific and industrial research capabilities. The project, named "Multicore Atomic Quantum Computing System" (MAQCS), involves collaboration with the Max-Planck-Institute of Quantum Optics. Alexander Glätzle, CEO of planqc, highlights this as a leap forward in solving complex problems across various industries. The MAQCS system's multi-core architecture promises increased efficiency and scalability, positioning Germany at the forefront of quantum innovation.
https://www.planqc.eu/news/20241113-1000_qubit_quantum_computer_for_lrz/
Multiverse launches Singularity in IBMs Qiskit Functions
Multiverse Computing has introduced the Singularity machine learning classification function to IBM's Qiskit Functions Catalog. This collaboration marks a significant step in integrating quantum computing with machine learning, aiming to enhance the capabilities of quantum algorithms in practical applications. Multiverse Computing, known for its quantum solutions in finance and other sectors, leverages Qiskit's open-source framework to potentially accelerate quantum machine learning developments. "This addition to Qiskit is a testament to the growing synergy between quantum computing and machine learning," stated Dr. Román Orús, Co-Founder of Multiverse Computing.
Pressure mounts on quantum companies to deliver corporate use case
In 2024, the quantum computing sector faces mounting pressure to deliver viable corporate use cases, with investors eager for returns on their high-risk investments. Olivier Tonneau of Quantonation emphasizes the urgency, stating, “We’re getting close to quantum advantage on several potential use cases.” Despite a decline in investment since 2022, companies like Riverlane and Q-CTRL continue to attract funding for error correction technologies, crucial for overcoming qubit instability. Bosch Ventures' Jan Westerhues notes the sector's long timeline but potential high rewards. Diverse approaches, including superconducting, trapped ion, and photonic qubits, are being explored, with no clear winner. Pasqal's Nicolas Proust highlights the importance of hardware in capturing quantum's value, predicting quantum advantage within 24 months.
A significant step forward for the quantum secure network in Finland: novel encryption technology tested in a commercial operator’s network
In a landmark development for quantum secure networks, Telia has successfully tested quantum encryption technology in its Helsinki network, marking Finland's first commercial operator trial. This initiative is part of the EU's EuroQCI project, aiming to establish a quantum encrypted network across Europe by 2030 to safeguard critical infrastructure. VTT, leading the Finnish NaQCI.fi project with CSC, Cinia Oy, and Suomen Erillisverkot Oy, highlights the significance of this trial. VTT’s Kari Seppänen emphasizes the importance of understanding the technology's practical challenges, while Telia’s Tero Maaniemi underscores the necessity of experimentation for national security and business needs.
Introducing our QEC decoder toolkit
Quantinuum has unveiled a Quantum Error Correction (QEC) decoder toolkit, a pivotal step towards achieving fault-tolerant quantum computing. This toolkit allows users to decode syndromes and implement real-time corrections, crucial for maintaining computation integrity. The toolkit leverages Quantinuum's real-time hybrid compute capability, executing Web Assembly (Wasm) in both hardware and emulator environments, thus enabling complex data structures and libraries. Notably, Quantinuum's coherence times are reportedly up to 10,000 times longer than competitors, enhancing the toolkit's effectiveness. The toolkit supports any QEC code, offering flexibility and enabling users to develop and test decoders. This development marks a significant advancement in making universal fault-tolerant quantum computing a tangible goal by the decade's end.
https://www.quantinuum.com/blog/making-fault-tolerance-a-reality-introducing-our-qec-decoder-toolkit
A universal quantum dot-based singlet-triplet qubit
Researchers from the University of Basel and IBM have developed a universal quantum dot-based singlet-triplet qubit. This new qubit design, detailed in a recent study, promises enhanced coherence times and scalability, addressing two critical challenges in quantum computing. The team, led by Dr. Andreas Kuhlmann, demonstrated that their qubit can be reliably controlled and manipulated using electric fields, which is crucial for practical quantum computing applications. "This development could pave the way for more robust and scalable quantum processors," said Dr. Kuhlmann.
https://phys.org/news/2024-11-universal-quantum-dot-based-singlet.html
The first mechanical Qubit
In a groundbreaking development, researchers at Delft University of Technology have created the first mechanical qubit, a significant departure from traditional superconducting or trapped-ion qubits. This innovation, led by Simon Gröblacher, leverages vibrating silicon nitride beams to encode quantum information, potentially offering greater stability and coherence times. Gröblacher noted, "Mechanical systems are inherently more robust against certain types of noise." This advancement could address some of the scalability and error correction challenges plaguing current quantum computing technologies. However, the mechanical qubit is still in its infancy, requiring further refinement before it can compete with established qubit technologies. As quantum computing evolves, such novel approaches may redefine the landscape, pushing the boundaries of what's possible in the field.
https://www.science.org/content/article/first-mechanical-qubit-quantum-computing-goes-steampunk
Paper: Artificial Intelligence for Quantum Computing
In a recent paper, "Artificial Intelligence for Quantum Computing," authors including Yuri Alexeev and Alán Aspuru-Guzik explore the intersection of AI and quantum computing (QC). They argue that AI's data-driven capabilities could address QC's scaling challenges, from device design to applications. The paper highlights the necessity of integrating expertise from both fields to advance QC. The authors emphasize that AI could be pivotal in overcoming QC's technical hurdles, stating, "Many of QC's biggest scaling challenges may ultimately rest on developments in AI." As QC continues to evolve, leveraging AI might be crucial for its practical realization, underscoring the importance of interdisciplinary collaboration in this nascent field.