The Quantum Adiabatic Algorithm (QAA) utilizes adiabatic evolution to solve optimization problems efficiently, offering quantum speedup in real-world applications. QAA builds on quantum superposition and entanglement principles, evolving from initial to final Hamiltonian states. Quantum coherence is essential for maintaining delicate quantum states, enabling parallel computation and exponential speedup. Challenges such as quantum tunneling and decoherence require error correction strategies for stability. QAA demonstrates significant speedup in complex tasks like finance and drug discovery, hinting at transformative possibilities in various sectors.
Key Takeaways
- QAA is an alternative to gate-based quantum computing.
- Ground state maintenance is ensured through the adiabatic process.
- Quantum coherence is vital for maintaining delicate quantum states.
- QAA offers significant computational speedup compared to classical algorithms.
- Applications of QAA include optimization, machine learning, and finance.
History of QAA
In tracing the history of the Quantum Adiabatic Algorithm (QAA), it is essential to examine its foundational developments within the domain of quantum computing. The historical significance of the Quantum Adiabatic Algorithm lies in its unique approach to problem-solving through quantum adiabatic evolution. This algorithm has its roots in the broader field of quantum computing, which emerged as a groundbreaking concept in the late 20th century.
The evolution of QAA techniques can be traced back to the early 2000s when researchers began exploring the potential of adiabatic quantum computing for solving complex computational problems. Initially proposed as an alternative to gate-based quantum computing, adiabatic quantum computing offered a different computational paradigm based on adiabatic quantum processes.
Quantum computing origins date back to the 1980s with the development of quantum mechanics and its application to information theory. Theoretical concepts such as quantum superposition and entanglement laid the groundwork for quantum computing, setting the stage for the eventual creation of quantum algorithms like the Quantum Adiabatic Algorithm.
Over the years, the Quantum Adiabatic Algorithm has undergone significant refinement and optimization, leading to advancements in quantum computing capabilities. The continuous exploration and development of QAA techniques have played a pivotal role in shaping the landscape of quantum computing and its applications in various fields.
Adiabatic Quantum Computing Basics
Adiabatic quantum computing basics encompass the fundamental concept of the quantum adiabatic process, where a system evolves slowly enough to maintain its ground state.
This process relies on the transition between energy eigenstates, vital for quantum adiabatic algorithms to function effectively.
Understanding the dynamics of energy eigenstates transition is pivotal in harnessing the power of adiabatic quantum computing for solving complex computational problems efficiently.
Quantum Adiabatic Process
Utilizing the principles of quantum mechanics, the quantum adiabatic process forms the foundation of adiabatic quantum computing, offering a unique approach to solving optimization problems.
In the adiabatic process, the system evolves slowly enough to maintain quantum states, preventing quantum energy transitions into unwanted states. This gradual evolution allows the system to reach its ground state, which encodes the solution to the optimization problem being addressed.
One key aspect of the quantum adiabatic process is its reliance on maintaining quantum coherence throughout the computation. This coherence guarantees that the system remains in superposition, exploring multiple possibilities simultaneously.
Energy Eigenstates Transition
The foundation of adiabatic quantum computing lies in the intricate dynamics of energy eigenstates within the quantum system. Energy eigenstates represent the possible states of a quantum system at definite energy levels. When a quantum system undergoes a quantum adiabatic process, it transitions between these energy eigenstates while maintaining its quantum coherence.
In the context of adiabatic quantum computing, the system evolves from an initial ground state, representing a simple-to-prepare configuration, to a final ground state that encodes the solution to a computational problem. The key to a successful adiabatic quantum computation is ensuring that the system remains in its ground state throughout the quantum transitions. This necessitates a slow evolution of the system to allow for the adiabatic theorem to hold, preventing excitations to higher energy states.
Understanding the energy eigenstates transition is vital for designing efficient adiabatic quantum algorithms that exploit the quantum properties of superposition and entanglement to solve complex computational problems effectively.
Quantum Annealing Vs. QAA
A comparison between Quantum Annealing and Quantum Adiabatic Algorithm reveals distinct approaches to solving optimization problems in quantum computing.
Quantum annealing is a method that utilizes quantum mechanics to solve combinatorial optimization problems. It works by finding the lowest energy state of a system representing the problem, aiming to reach the ground state that encodes the best solution. This approach is particularly valuable for specific types of optimization problems where finding the global minimum is essential. Quantum annealing employs quantum effects such as tunneling and quantum entanglement to investigate different configurations efficiently.
On the other hand, the Quantum Adiabatic Algorithm (QAA) is a broader approach that involves evolving a quantum system from a simple initial Hamiltonian to a final Hamiltonian representing the optimization problem. The system evolves slowly enough to remain in the ground state at each point, ensuring that the solution is found at the end of the evolution. QAA is based on the adiabatic theorem of quantum mechanics, which guarantees that if the system evolves slowly with respect to the energy gap, it will remain in the ground state.
QAA Workflow
Evolving a quantum system from an initial Hamiltonian to a final Hamiltonian characterizes the workflow of the Quantum Adiabatic Algorithm (QAA). The QAA workflow involves the gradual transformation of the system from the initial Hamiltonian, representing a problem that needs to be solved, to the final Hamiltonian, where the solution is encoded. This evolution is governed by the adiabatic theorem, ensuring that the system remains in its ground state if the transformation is slow enough.
QAA workflow optimization plays an essential role in the algorithm's efficiency. Analyzing and fine-tuning the parameters governing the evolution, such as the annealing schedule and Hamiltonian design, are necessary for achieving accurate results within a reasonable time frame. By carefully optimizing the workflow, researchers aim to minimize errors and maximize the algorithm's success rate.
QAA workflow analysis involves evaluating various aspects of the algorithm's execution. This includes understanding the impact of noise, grasping the system's behavior during the adiabatic evolution, and identifying potential bottlenecks that could hinder performance. Through detailed analysis, researchers can gain insights into the algorithm's strengths and limitations, guiding further improvements and advancements in quantum computation.
Importance of Quantum Coherence
The importance of quantum coherence in quantum computing cannot be overstated.
Coherence is fundamental for maintaining the delicate superposition of qubits, enabling complex quantum operations to be performed.
Without the preservation of qubit coherence, the effectiveness of quantum algorithms, such as the Quantum Adiabatic Algorithm, would be severely compromised.
Coherence in Quantum Computing
Quantum coherence plays a fundamental role in quantum computing by enabling the superposition and entanglement of quantum states, which are essential for the implementation of quantum algorithms. In quantum systems, coherence refers to the importance of a quantum state to exist in a superposition of different states simultaneously, allowing for parallel computation. This superposition dynamics enable quantum computers to process vast amounts of information in parallel, providing a potential for exponential speedup compared to classical computers.
Moreover, coherence is vital for maintaining the delicate quantum states needed for quantum algorithms to function properly. It allows qubits to investigate multiple solutions simultaneously, providing the computational power necessary for solving complex problems efficiently. Understanding and controlling coherence in quantum systems is paramount for the development and scalability of quantum technologies.
In essence, coherence in quantum computing underpins the unique computational advantages offered by quantum systems, paving the way for groundbreaking advancements in various fields, including cryptography, optimization, and simulation.
Maintaining Qubit Coherence
Sustaining the coherence of qubits is a vital aspect of quantum computing that influences the reliability and efficiency of quantum algorithms. Qubit coherence maintenance guarantees that the quantum information encoded in qubits remains stable and coherent during computations.
Quantum systems are highly susceptible to decoherence due to interactions with the environment, leading to errors in calculations. To address this challenge, quantum error correction techniques are employed. These techniques involve encoding quantum information redundantly across multiple qubits to detect and correct errors that may arise from decoherence.
Implementing quantum error correction requires sophisticated algorithms to efficiently manage the redundancy while preserving the integrity of the quantum information. By mitigating the effects of decoherence through error correction, the overall performance and accuracy of quantum computations can be greatly improved.
Therefore, maintaining qubit coherence through error correction techniques is essential for advancing the reliability and scalability of quantum computing systems.
Applications of QAA
Applications of the Quantum Adiabatic Algorithm span a wide range of fields, showcasing its potential for solving complex optimization problems efficiently. The table below highlights some key areas where the Quantum Adiabatic Algorithm has shown promise:
| Field | Application |
|---|---|
| Quantum Adiabatic Optimization | Solving combinatorial optimization problems |
| Machine Learning | Enhancing optimization in deep learning models |
| Financial Modeling | Portfolio optimization and risk management |
| Quantum Adiabatic Algorithm | Solving complex financial equations efficiently |
In Quantum Adiabatic Optimization, the algorithm proves beneficial for tackling combinatorial optimization problems where classical algorithms struggle due to the exponential growth in complexity. Machine learning applications benefit from the Quantum Adiabatic Algorithm by enhancing optimization tasks within deep learning models, potentially leading to more efficient training processes.
Moreover, in financial modeling, the Quantum Adiabatic Algorithm is employed for tasks like portfolio optimization and risk management. By leveraging the algorithm's ability to efficiently solve complex financial equations, analysts and researchers can make more informed decisions in a shorter time frame. These applications underscore the versatility and potential impact of the Quantum Adiabatic Algorithm across various domains.
QAA Performance Metrics
Efficiently evaluating the performance of the Quantum Adiabatic Algorithm relies on a set of carefully selected metrics. Two key aspects in evaluating the efficacy of the Quantum Adiabatic Algorithm (QAA) are accuracy appraisal and efficiency scrutiny.
QAA accuracy appraisal involves measuring how close the algorithm's output is to the exact solution of the given computational problem. This metric is pivotal as it determines the reliability and trustworthiness of the results produced by the QAA. Accuracy is typically evaluated by comparing the QAA's outcomes with known solutions, either analytically derived or obtained through classical computing methods. The closer the QAA output is to these benchmark solutions, the higher its accuracy.
On the other hand, QAA efficiency scrutiny focuses on evaluating the algorithm's performance regarding computational resources and time required to reach a solution. Efficiency metrics consider factors such as the number of qubits utilized, gate operations performed, and the overall computational time.
High efficiency in QAA implies that the algorithm can solve complex problems effectively within reasonable resource constraints.
Challenges and Limitations
Exploring the domain of quantum adiabatic computing presents researchers with a myriad of challenges and limitations that shape the algorithm's practical implementation and performance. Two significant challenges in quantum adiabatic computing are quantum tunneling challenges and implementing error correction. Quantum tunneling challenges arise when the quantum system fails to stay in the ground state due to tunneling through potential barriers, which can lead to inaccuracies in the final solution. Implementing error correction in quantum adiabatic algorithms is essential to mitigate the effects of noise and errors that can occur during computation, ensuring the reliability of the results.
Another critical limitation is overcoming decoherence, where quantum systems lose their coherence and become susceptible to external influences, disrupting the computation process. Strategies such as error correction codes and quantum error correction techniques are being developed to address this issue. Additionally, scalability remains a significant hurdle in quantum adiabatic computing. Scaling up the number of qubits while maintaining low error rates is a complex task that requires advancements in hardware, software, and algorithm design to achieve practical quantum advantage.
| Challenges and Limitations | Solutions |
|---|---|
| Quantum tunneling challenges | Implementing error correction |
| Overcoming decoherence | Error correction codes and techniques |
| Scalability issues in QAA | Advancements in hardware and algorithm design |
Quantum Speedup With QAA
Quantum Speedup with Quantum Adiabatic Algorithm (QAA) offers a promising avenue for enhancing computational efficiency. Understanding the quantum speedup associated with QAA involves exploring the advantages it provides over classical algorithms.
Quantum Speedup Explained
A key aspect of the Quantum Adiabatic Algorithm (QAA) lies in its potential to deliver significant computational speedup compared to classical algorithms. Quantum speedup benefits are most pronounced in problems that involve complex optimization tasks, such as finding the global minimum of a function or solving large-scale combinatorial optimization problems. The inherent parallelism and superposition properties of quantum systems enable QAA to investigate multiple solutions simultaneously, leading to faster computation times and more efficient problem-solving compared to classical approaches.
Practical applications of quantum speedup with QAA span various fields, including machine learning, cryptography, drug discovery, and financial modeling. For instance, QAA can improve the efficiency of machine learning algorithms by accelerating tasks like pattern recognition and data clustering. In cryptography, QAA's speedup benefits can strengthen security protocols by facilitating quicker encryption and decryption processes.
Additionally, in drug discovery, QAA's ability to optimize molecular structures rapidly can transform the development of new pharmaceuticals. The quantum speedup offered by QAA opens up exciting possibilities for advancing technology and solving complex real-world problems efficiently.
Advantages of QAA
Leveraging the principles of quantum mechanics, the Quantum Adiabatic Algorithm (QAA) demonstrates a notable advantage in accelerating computational processes through quantum speedup. The efficiency benefits of QAA lie in its ability to solve complex optimization problems more quickly than classical algorithms. By exploiting quantum parallelism and tunneling phenomena, QAA offers computational advantages that can outperform classical algorithms in certain scenarios.
One key advantage of QAA is its potential for exponential speedup in solving combinatorial optimization problems. This is particularly significant for tasks like graph partitioning, where QAA can efficiently investigate multiple solutions simultaneously. Additionally, QAA shows promise in enhancing machine learning algorithms by accelerating tasks such as clustering and pattern recognition.
Moreover, QAA's computational advantages extend to its robustness against certain types of errors, making it a reliable option for quantum computation. The inherent parallelism of QAA allows for the examination of multiple possibilities simultaneously, leading to faster and more efficient problem-solving compared to classical algorithms.
Real-world Applications
The application of Quantum Adiabatic Algorithm (QAA) in real-world scenarios demonstrates significant advancements in computational efficiency through quantum speedup. Industry applications of QAA are diverse, with practical use cases ranging from optimization problems in finance and logistics to machine learning and drug discovery. In industries where complex problem-solving is essential, QAA offers a promising solution by leveraging quantum speedup to drastically reduce computation time compared to classical algorithms.
One notable industry application of QAA is in financial portfolio optimization, where QAA's ability to efficiently handle large datasets and complex variables can lead to optimized investment strategies. Additionally, in logistics, QAA can aid in route optimization, warehouse management, and supply chain optimization, improving operational efficiency and reducing costs.
In the domain of machine learning, QAA shows promise in accelerating model training and improving prediction accuracy. Furthermore, in drug discovery, QAA can expedite the process of identifying potential drug candidates by efficiently exploring vast chemical spaces.
These practical use cases highlight the transformative potential of QAA in reshaping various industries through quantum speedup.
QAA Implementations
Implementation of Quantum Adiabatic Algorithms (QAAs) involves translating problem instances into Ising models or other suitable Hamiltonians for execution on quantum annealing hardware. QAA algorithmic optimizations play a pivotal role in enhancing the efficiency of this translation process. These optimizations focus on minimizing the energy gap between the ground state and excited states of the Hamiltonian to facilitate successful adiabatic evolution. By refining the algorithm design, researchers aim to achieve faster convergence towards the best solution.
QAA benchmarking studies are essential for evaluating the performance of different implementations. These studies compare the effectiveness of QAAs in solving specific problems against classical algorithms or other quantum computing approaches. Through rigorous benchmarking, researchers can assess the scalability, accuracy, and speed of QAAs under varying conditions. This empirical analysis provides valuable insights into the strengths and limitations of QAA implementations, guiding further refinements and advancements in the field.
Researchers continually work towards improving QAA implementations by exploring novel approaches to problem mapping, refining quantum annealing schedules, and optimizing qubit connectivity. These efforts are motivated by the goal of enhancing the computational power and applicability of quantum adiabatic algorithms in solving complex optimization problems efficiently. By combining algorithmic innovations with insights gained from benchmarking studies, the QAA implementation landscape continues to evolve, paving the way for new breakthroughs in quantum computing research.
QAA Hardware Requirements
The hardware requirements for implementing Quantum Adiabatic Algorithms (QAA) encompass specific specifications and performance metrics.
Understanding the hardware specifications is essential for determining the capabilities and limitations of the quantum system being utilized for QAA.
Performance metrics play an important role in evaluating the efficiency and effectiveness of the hardware in executing adiabatic quantum computations.
Hardware Specifications
To effectively execute the Quantum Adiabatic Algorithm (QAA), specific hardware requirements must be met to guarantee peak performance. Hardware compatibility and system requirements play critical roles in ensuring the successful implementation of the algorithm. Below is a table outlining some key hardware specifications necessary for running the QAA:
| Hardware Component | Minimum Requirement | Recommended Requirement |
|---|---|---|
| Quantum Bits (Qubits) | 100 | 500 |
| Quantum Volume | 1024 | 4096 |
| Quantum Gate Speed | 10 GHz | 20 GHz |
| Quantum Connectivity | 80% connectivity | 95% connectivity |
| Quantum Error Rate | < 10^-3 | < 10^-4 |
These specifications provide a baseline for the hardware needed to effectively run the Quantum Adiabatic Algorithm. Meeting or exceeding these requirements ensures that the algorithm can perform at its best, achieving accurate results within a reasonable timeframe. By considering these hardware specifications, researchers and practitioners can make informed decisions when setting up the infrastructure for utilizing the QAA.
Performance Metrics
Considering the hardware specifications outlined for the Quantum Adiabatic Algorithm, an analysis of performance metrics related to these requirements becomes essential. Efficiency analysis plays a vital role in evaluating the Quantum Adiabatic Algorithm's effectiveness in solving complex computational problems. Performance metrics such as runtime, energy consumption, and solution accuracy are pivotal factors to take into account when evaluating the algorithm's efficiency.
In comparison to classical algorithms, the Quantum Adiabatic Algorithm showcases promising capabilities in certain problem domains. Performance comparison studies between adiabatic and classical algorithms reveal that the Quantum Adiabatic Algorithm can outperform classical counterparts in specific scenarios, particularly for optimization and search problems.
However, further research is needed to fully understand the algorithm's performance across a wide range of problem types and sizes.
Quantum Adiabatic Theorem
Throughout quantum mechanics, the Quantum Adiabatic Theorem stands as a fundamental principle dictating the evolution of a quantum system under certain conditions. The theorem describes the behavior of a quantum system when it evolves slowly enough to stay in its instantaneous eigenstate. This slow evolution guarantees that the system remains in a state very close to the ground state, allowing for a smooth transition between states.
In the context of quantum adiabatic evolution, the theorem states that if a system starts in the ground state of a Hamiltonian and the Hamiltonian changes slowly enough, the system will stay close to the ground state of the new Hamiltonian. This principle is essential in quantum adiabatic computation, where the system evolves from an easily prepared initial state to a final state representing the solution to a computational problem.
The Quantum Adiabatic Theorem provides a basis for the adiabatic quantum computing paradigm, where the solution to computational problems is encoded in the ground state of a time-dependent Hamiltonian. By ensuring the evolution of the system is adiabatic, meaning no transitions to higher energy states occur, the theorem supports the success of adiabatic quantum algorithms in solving complex computational tasks efficiently.
QAA Optimization Examples
Examples of optimization problems tackled using Quantum Adiabatic Algorithm (QAA) showcase the algorithm's potential in efficiently finding solutions across various domains. QAA optimization strategies have been applied to diverse real-world problems, demonstrating promising results. One notable case study involves the traveling salesman problem (TSP), a classic combinatorial optimization challenge. By mapping the TSP onto a QAA framework, researchers achieved improved solution accuracy compared to classical algorithms.
In addition to TSP, QAA has been utilized in financial portfolio optimization, where the objective is to maximize returns while minimizing risks. By formulating the problem as an Ising model, QAA enabled the identification of optimal investment portfolios efficiently. This application highlights QAA's versatility in handling complex optimization tasks beyond theoretical scenarios.
QAA performance analysis in these practical examples reveals its ability to outperform classical optimization techniques in certain contexts. The algorithm's quantum nature allows it to explore solution spaces effectively, leading to potentially superior outcomes. These successes underscore the significance of QAA in addressing challenging optimization problems that are prevalent in various industries.
Moving forward, continued research into QAA optimization strategies and the examination of additional case studies will further enrich our understanding of the algorithm's capabilities. By analyzing the performance of QAA in diverse scenarios, researchers can refine its implementation and potentially open up new avenues for leveraging quantum computing in optimization tasks.
Future Developments in QAA
Exploring innovative quantum adiabatic algorithm (QAA) adaptations holds promise for advancing optimization capabilities in diverse problem domains. The future advancements in Quantum Adiabatic Algorithms (QAA) are poised to transform optimization processes across various fields.
One key area of focus for future QAA advancements lies in improving the algorithm's speed and efficiency. Researchers are actively working on developing techniques to minimize the time required for adiabatic evolution, thereby accelerating the overall optimization process.
Furthermore, another important aspect of future QAA advancements involves increasing the scalability of these algorithms. Current limitations on the size of the problems that can be effectively solved using QAA are being addressed through novel approaches that aim to expand the algorithm's capacity to handle larger and more complex optimization tasks.
Moreover, future developments in QAA are expected to explore improving the robustness and reliability of these algorithms. By enhancing error correction mechanisms and reducing the impact of decoherence, researchers aim to create more stable QAA implementations that can deliver consistent and accurate results.
Conclusion and Final Thoughts
To conclude, the advancements in Quantum Adiabatic Algorithms (QAA) hold significant potential for transforming optimization processes in various application domains. As quantum computing continues to progress, future advancements in Quantum Adiabatic Algorithms are expected to further magnify their practical implications. The unique ability of QAA to solve complex optimization problems by leveraging quantum phenomena such as superposition and entanglement opens up new avenues for tackling challenges that are intractable for classical computers.
The practical implications of quantum adiabatic algorithms are profound. By exploiting quantum tunneling and the adiabatic theorem, QAA can potentially provide solutions to optimization challenges that are currently computationally prohibitive. This could innovate industries such as finance, logistics, drug discovery, and machine learning, where optimization plays a significant role in decision-making processes.
Looking ahead, the integration of Quantum Adiabatic Algorithms with emerging quantum technologies is poised to unleash even more powerful computational capabilities. Collaborative efforts between researchers, industry experts, and policymakers will be essential to harness the full potential of quantum adiabatic algorithms in real-world applications.
With ongoing advancements in quantum computing hardware and software, the future of Quantum Adiabatic Algorithms looks promising, paving the way for unprecedented advancements in optimization and computational efficiency.
Frequently Asked Questions
Can Quantum Adiabatic Algorithms Solve Np-Hard Problems Efficiently?
When considering NP-hard problems, their complexity arises from the challenge of finding solutions in a reasonable amount of time. Adiabatic evolution offers a method that gradually transitions a system from an initial easy-to-solve state to a final desired state.
This approach holds promise for potentially solving NP-hard problems efficiently by leveraging the continuous evolution of quantum states. The interaction between adiabatic evolution and NP-hard complexity presents an intriguing avenue for exploration in quantum computing.
How Does Decoherence Affect the Success of Quantum Adiabatic Algorithms?
Decoherence, a pesky adversary in the domain of quantum phenomena, can wreak havoc on the delicate dance of quantum systems.
When applied to quantum annealing, its effects on adiabatic evolution can be detrimental, leading to errors and loss of coherence.
Strategies like error correction mechanisms are vital in mitigating these challenges, ensuring the success and reliability of quantum adiabatic algorithms in the face of the ever-looming threat of decoherence.
Are There Any Real-World Applications Where Quantum Adiabatic Algorithms Outperform Classical Algorithms?
Real-world applications leveraging quantum adiabatic algorithms have shown promise in industries such as finance, logistics, and machine learning. These algorithms have demonstrated the potential to outperform classical algorithms in optimization tasks, offering faster and more efficient solutions.
However, current limitations, such as susceptibility to noise and decoherence, hinder their widespread adoption. Future improvements in error correction and algorithm design may amplify the industry impact of quantum adiabatic algorithms.
What Are the Major Challenges in Scaling Quantum Adiabatic Algorithms for Practical Use?
Scaling quantum adiabatic algorithms poses significant challenges. Resource requirements, optimization strategies, performance validation, and benchmarking are vital aspects that need meticulous attention.
Balancing computational power with energy consumption is akin to walking a tightrope in the digital domain. As quantum technologies evolve, ensuring scalability without sacrificing efficiency remains a formidable task that demands innovative solutions and rigorous testing methodologies.
How Do Quantum Adiabatic Algorithms Compare to Other Quantum Computing Techniques in Terms of Speed and Accuracy?
When examining quantum adiabatic algorithms compared to other quantum computing techniques, such as quantum annealing, considerations for speed and accuracy are crucial.
The ongoing discussion surrounding quantum supremacy explores the potential advantages and limitations of various quantum computing approaches.
Quantum adiabatic algorithms, with their unique adiabatic evolution method, offer a distinct perspective in the quantum computing landscape, showcasing strengths and weaknesses that differentiate them from other techniques regarding computational speed and precision.
Conclusion
In wrapping up, the quantum adiabatic algorithm has shown great promise in optimization problems through the use of quantum annealing and the principles of quantum coherence.
As we look towards future developments in QAA, one cannot help but wonder: What new frontiers will be revealed through the power of quantum adiabatic computing?